Hi, I'm Rusty Komori and this is Beyond the Lines on Think Tech Hawaii. I was the head coach of the Punahou Boys varsity tennis team for 22 years and we were fortunate to win 22 consecutive state championships. My book Beyond the Lines is about leadership, creating a superior culture of excellence and finding greatness which is what this show is all about. My special guest today is a super talented musician and the owner and CEO of Hawaii Music Works. He is Mark Santos and today we are going beyond music. Hey Mark, thanks for joining me on the show today. Thanks for having me. Long time no see. I miss you. I miss you too, we're too busy. Now Mark, tell me about your early years growing up. Early years, well, I moved around a lot because my dad was in the military. He was tough because he's a retired West pointer too. You always kid me about my hair. I had a crew cut until I was like 11 years old. But we moved around a lot all over the place and from the military but I think that was good because it exposed me to a lot of different cultures and things. So that was great. But then when I started high school we came back and so I went to Iolani and that's where I really got involved in music, I mean heavily in high school. So I was in jazz band, I was in marching band and after that I decided because I moved around so much when I was younger, being in the military, I mean military kids, they really have it rough because they make friends and then they lose their friends because they have to move to a new place and start all over again. So when I graduated from high school I said I just want to stay here. And fortunately back in the 80s, not everybody knows how old they are, but the business goes really hopping over at the University of Hawaii. So I wanted to major in business and marketing. That's what they did at the University of Hawaii. So nice. Very nice. Yeah. No, and then I know that you played tennis when you're in high school as well, right? Yes, I did. Good thing you were my coach. I would have cleaned up it. I actually started that when I was living in California when I was younger. So how young were you, Mark, when you started getting interested in music?  So I remember when I was like, I think I was four. Wow. For Christmas, my parents got me my first phonograph. So I crawled underneath and then they had wrapped it so I crawled under the dining table and I was peeling off the plastic. They caught me because they heard, you heard that. But yeah, as young as that, and even, you know, so back then, I liked music and I knew what I liked. I mean, I'm listening to the Beatles, listening to, well, my dad, I mean, he had this big record collection. So I'd listened to everything from the Beatles to Herb Alpert to Bert Backrack, to Dionne Warwick, you know, Trini Lopez, was it at least, Jose Felicea. Yeah, everything. So that's such a wide spectrum right there that you just mentioned.  Yeah, she plays the iPhone. You know, I found, when we're dating, I found an acoustic guitar in your room, and I was like, who's guitar is it? She says, it's mine. And I go, can you play something for me? She said, no, she has never played it for me ever, but it's here in the studio. So use it for teaching. I can, I can hear her saying that to you. Among other things.  I mean, how, how's Kira and Esri doing, doing now? Um, yeah, they're doing good. I can't believe they're adults, they're actually adults, they're adults. I mean, to you and me about what, 30 years old now? Now they're doing good and pursuing that their original music with their good friend, our family, Peyton, who's in the band, the drummer, poor guy, all by himself, the guy. But they've actually come and joined the business. This is really truly a family business, our music studio. Great. And I really like that. I really like that because they bring their enthusiasm, their passion and their experience of playing to the teaching front with our students. Oh, I love hearing that. And Mark, I want to, I want you to share when and why did you start Hawaii Music Works? Oh, yeah. Well, after, um, in the mid 80s, I got connected with, um, I won't say the, the name of the corporation, but, um, I worked for a long time for the largest music corporation of the world, it really is. And I had to move to Los Angeles. So I worked for them for a long time, uh, being a touring musical artist and a clinician, but, um, when I got, uh, when Dylan, I got married and, uh, right before we came back, my last dent with that corporation was music education. So, um, and then I discovered, you know, I really enjoyed it. I really got a high off of teaching people, especially young people. It was really rewarding. I really, I really find it fulfilling. So instead of really concentrating being a performer, which I really thought I wanted to be, I'm like, I decided I wanted to be a music educator. And when I came back and decided when I retire, I'm going to start my own school. So that's what happened. Oh, I love hearing that, Mark. And, you know, Mark, we have a short video that we're going to play right now. If we can roll that about Hawaii music works. Mark, as we saw there, I mean, you're making such an amazing impact with countless, uh, kids in our community. Why do you love teaching music so much? Um, no, there's all kinds of reasons. Oh, let me, I want to see something about that film clip. In the middle of the clip, there's a little girl who's playing piano. Yep. And Kira was teaching and then she made a mistake and then she started to laugh. Yeah. Yeah. And then she goes back. She went to try it again and then she finished it.  And she said, no, I didn't. And I think that's really interesting. And it shows like, you know, what we try to do with our students because she's not afraid to make a mistake. She can laugh at herself and, you know, just take it as like, oh, I messed up. Let me try it again as I learned from my mistakes. Right. And she gets to the end and Kira says, great job. And she says, no, I didn't because she's not satisfied with what she did. And she wants to, she knows that she wants to do it again, do it better because she knows she can. Yeah. So I really, I really liked that clip right there. No, you guys, I mean, what you're doing, it's, it's so amazing. And I, I feel so happy to showcase, you know, Hawaii music works and you because I mean, I know that you're in demand with a lot of these private and group lessons. And at the end, I mean, there's a lot of students that you end up connecting with Hard Rock Cafe, right? Oh, yeah. Cause I, you know, we really think it's important that you take what you learn, apply it, you know, and showcase it, share it with people. Cause music is about sharing. It's a social aspect, right? And then me being able to offer them a nice venue, a real professional venue with a real stage, real lights.  Well, let's, let's talk about that ultra popular band before MK. Okay.  And I want to know, well, people, a lot of people knew that they were the young kids who would play incredibly amazing journey songs. But how did it all begin with them? Yeah, that's a funny thing. Cause everybody, everybody thinks that, you know, I made them do it. I'm Joe Jackson or something. But what happened was, you know, we started the school and they were, they were really young when I started the school. And, you know, I'm teaching and I actually had another group of students that I was working with, actually two or three groups. And they were older, of course, in your teens, but the girls are watching the older students. And then they actually asked, can I start piano? Can I learn piano? So they both started in piano, but I sent them to my instructors because it's impossible for a parent to teach their own child. It just doesn't work. You know that. So my teachers, actually, I owe a lot to my teachers for sending a good foundation. And then as they, yeah, as they progressed, I mean, they started piano when they're four, they added guitar when they're around seven. As we added the bass when she got a little bit, oh no, just like maybe eight years old. And then in vocals and they're in two. So everything is them. They wanted to do it just because they were exposed to it. Yeah.  And I want to know why, why did they become so successful as a band? Um, a lot of facets.  And what, you know, what does somebody mean by success? And the first thing I want success is just if you can do something and be good at it because you are passionate about it and you enjoy it and you can be fortunate. And then really fortunate if it turns out to be something that you can do as a profession. Yeah, a lot of people get stuck in jobs and you don't like what they're doing, you know. And I can tell that you love it. I mean, they all love it. And it just really trans transfers to the students that they work with. Yeah, that's really important to us. We want to make sure that whoever is teaching here transfers their passion, their love of music, you know, and is just not going through the motions, you know, play this scale for me. Yeah, wrong notes there. You know, that's just, you know, that's just barely part of it.  Yeah. And it's all about having fun in, in learning music. Yes, definitely. Because you don't want to learn something you're not enjoying. Exactly. Mark, we're going to take a quick break. And when we come back, we're going to continue going beyond music. Okay. Okay. You are watching Beyond the Lines on Think Tech Hawaii with my special guest, Mark Santos. We will be back in 60 seconds. Stan Osterman, Stan the Energy Man, every Friday here on Think Tech Hawaii. If you're really interested in finding out what's going on in energy, especially here in Hawaii, but also all the way around the world, and especially if it has to do with hydrogen, look into Stan the Energy Man every Friday, 12 o'clock, Think Tech Hawaii. Be there. Aloha. Welcome back to Beyond the Lines on Think Tech Hawaii. My special guest today is a super talented musician and the owner of Hawaii Music Works. He is Mark Santos and today we are going beyond music. Mark, I want to ask you one more thing about MK. They were, they were offered by Sony Japan to have a contract and they turned them down. What, what was the reasoning behind that? Yeah, I had nothing to do with that. That's totally them. And it's funny because that even relates back to an earlier time when they were younger. They did get a Disney offer also when they were younger. And they turned down the Disney thing and the Sony thing for the same reasons. And at two different age, when they're in two different ages, and really the thing is they discovered as they were going through it that it was probably going to make them have to do something or be something that they weren't. And they didn't want to actually pursue music and not be themselves or be able to express themselves for real. So that's why they turned down both offers. No, that's very admirable of them because, you know, most bands will just be like, yes, we'll do it. And yes, we'll, you know, transform into whatever you want, but, you know, for them to really do what they want and play the music that they want and the look that they wanted to have. I mean, that is so amazing. And then Mari left the band and now they are known as close the distance. Now, what's the latest updates with close the distance? Yeah, so they're taking this kind of as a rebranding process and reimagining and trying to redefine themselves and starting all over again. So and they've changed drastically. They've changed musical direction just because they're different. They're in a different point in their life. So, but it's going really good. You know, they still have their endorsement contracts and everything. And but, you know, also from the time that they had those Disney and Sony offers, I mean, the music industry has changed so much and keeps evolving, especially with social media and how musicians have to actually be ready to be their own marketing department and management. So they're going to really pursue stuff independently. Maybe they're really happy and excited about that. They don't want to be signed to label. Yeah, and that comes from interacting with some other artists that they've met like, you know, at the national in Los Angeles and talking to people who are touring. Yeah, they want to just be their own thing and be in charge of it. Now, Mark, how fun and special and special is is it for you to be a guest performer with close to distance at times? I think you've been there sometimes. Yeah, of course. You rock, Mark. Oh my gosh, I look like I'm in pain right there. Okay, so somebody's taking my foot. Okay. No, it's a lot of fun, but you know, when they were younger, it was kind of a cool, you know, they wanted me to do it.  Yeah, you kind of look older and I got for a song or two and stuff, but that's about all I can handle too. Cause man, I can't keep up with them. You know, I'm older as it is already. Well, you know, the special thing is I think when the, when people in the audience are watching it, I mean, it really looks like it's special for them, you know, having you on there too. And Mark, I want to, I want to talk to you about my book beyond the lines. You know, in my book, I talk a lot about creating a superior culture of excellence and when I come into your studio in Pearl City, I mean, you really have a great culture in your studio with, with everybody with your, with your team of employees and your students. I mean, why is that? I think we try and make it, you have to make it feel like a family. Yeah.  No matter who you are, which way you're going internally or externally in your business. So when people come in, I want them to feel like, you know, we're just one big happy family and we're going for one common goal. And in the end, what's really happening is we're all enjoying ourselves, achieving that goal and on that journey to achieve success. I get that. I, and I feel that and everyone that comes in there will have that same feeling as well. And I want to ask you to mark.   And I want to know when you're working with someone or when you have your staff of amazing musicians working with someone. What do you focus on? You know, I think the thing that sets us apart from everybody else and the regular, you know, traditional type of approach to music lessons is we try and get to know the student. Know that, you know, what they feel strongly about what their desires are, find their superpower and then try and bring that out rather than say, here, you know, this do this and do it that way. And, you know, who wants, who wants something just shoved at them and and and done mechanically, you know, so if you can achieve that bond, get that communication of rapport going and really know who you're working with, then you can pull out the best from them and she've excellence. I love that you mentioned that about really trying to focus on what they want to achieve. I mean, what, what is it that they want to improve on?   Now, and you mentioned about communication, how important that is. How, how do you connect with such a wide range of students and personalities? I'm telling you that that's really tough, you know, especially for me and the staff because I mean, we could be working with a five year old and then the next lesson we jump into, it's a teenager. And then for me, because I'm the one that does adults and I might go in and switch and then I'm with a senior citizen. So it's boom, boom, boom. So again, yeah, the thing that you have to do is make that connection and really get that relationship going.  Sometimes they have fears and phobias and you have to talk through them and you know, being a coach and instructor. This is the same thing.  That's for sure. I know what you mean. Now, Mark, I also know that, you know, whether when, when people learn guitar or keyboards or drums, for example, there's certain personal and health benefits. What are some of those things? Yeah, people don't seem to realize it.   And that's really important because if you're doing it the right way, it ties right back. It's just another subject of education. And I think we have to, okay, I don't want to get in a soapbox, but I'm just saying education as a whole is a society. I think we have to rethink it because what we should be stressing is creativity, imagination and being driven by passion. And those are all parts of not just the arts and the humanities. I mean, math and science, you know, those things, language arts, those things have their own beauty in the way they're shaped and everything, but sometimes we lose sight of that. We just get caught up in the details and the facts and then just getting the right answer. But if we tie it in to actually creating something and achieving something that we need ourselves, you know, there's something rewarding about that for everybody. I love hearing that. And what you said right there, you basically said it's all about going beyond the lines, Mark. I think he just did. Now, Mark, who are some musicians that your students are really inspired by when they come to learn, you know, music from you?  Oh, they love Taylor Swift, right? Or they like the Jonas Brothers and things, but it really when we get really surprised when they come in and they say like somebody from the 70s or even the 60s or the 80s and it's like, how do you know who that is? And that never really say, oh, because my parents listen to KSSK. We're driving a school in the car, but but then we discover that they're actually listening. They're really listening to the music and they notice things and they'll say, oh, I like the way this person plays. And because we're in this age where we have technology and we have video, we just don't have, you know, the radio. We can they can watch YouTube. They really, really key on on some of their personality traits and the way they move on stage. And that's a big factor of being inspired to with young students. Do you believe that anyone and everyone could become a great musician? Well, I do. But, you know, the thing is it depends. It not depends, but it's all sometimes people make a mistake about how they define great.  If you achieve your goal and you enjoy doing it. Yeah, everybody has the ability. Music is a human thing. Everybody can do music.  I don't have rhythm. It's like, no, you can't. That's impossible.  You have those qualities, those those traits. But as we get older, unfortunately, we get taught to actually ignore those things and we lose connection with them. So here at the school, we try and help them reconnect with that stuff and discover that it's there. We're just going to bring it out. I love how you guys are bringing it out like that because, you know, some people before they come into you, they might have a preconceived notion of their capabilities. But what you're doing is you're really showing them that, hey, you're capable of doing so much more. Yes, yes. So much more. I think you even sing Def Leppard on your birthday. I tried singing Def Leppard with MK and we have pictures to prove it. So Oh, no. Let's keep that private. Okay. Now, Mark, what's what's a future goal of yours? Because, you know, you've accomplished so much already. You've impacted so many people. But what's a future goal of yours right now? I really want to stretch out, especially online. I want to do and I want to reach out not just as my own business, but I'd like to do it in such a way so I can connect with the schools and offer. Curriculum out to the schools because, you know, music and the arts and that's being taken out. It's just they're not doing it and it's like people saying, well, how can you teach music online? It's like new technology. The computer is actually a musical instrument. If you use it correctly, you can still be very musical. There's a lot of ways to abuse it and do it more like Lego blocks and you know, you just kind of piece and things together. But I want to reach out and do something where we can do music production with the schools, online courses and then the schools and the kids from different schools and different islands can interact and collaborate with each other because it's online and write music together and even write music to video or write music for their history or reports or something like that and just get everybody connected one being music community. I love that as a goal. Let's let's help make that a reality. Okay, Mark. Yeah, I want to network with some with some people get that happening for sure. And Mark, I want to thank you for joining me on the TV show today and really thank you for sharing your insights. Oh, thanks for having me rusty any time and let's get together to sure. And thank you for watching Beyond the Lines on Think Tech Hawaii and a special thank you to my clothing sponsor Ilani Incorporated. For more information, please visit Rusty Komori dot com and my book is available on Amazon and Barnes and Noble. I hope that Mark and I will inspire you to create your own superior culture of excellence and to find your greatness and help others find theirs. Aloha.


The title is M&M, Masked and Max Against Physical Attacks. The authors are Laurene Meijer, Victor Arribas, Svetla Nikova, Vensi Nikov, and Vincent Rijmen. And Laurene will be giving the top. Thank you. You can see the slides. So thank you for the introduction. I'm going to be talking about M&M, Masked and Max Against Physical Attacks. So in the field of embedded systems, I think if you go back to the 90s, there are two very important papers. So the first one being, of course, the first paper on differential power analysis. That was in 1999. And then three years before that, even there was a first paper on differential fault analysis. So in the next year, the community has been working hard to create countermeasures against these things. So against side channel attacks, two important countermeasures are hiding and masking. In this talk, I'm only going to talk about masking. And you already know by this time in the conference what that is. Then against fault attacks, you need some kind of redundancy. So repetition is quite common, but also error detecting codes, or in our case, tags. And then once you figure out how to implement your cryptographic calculation with redundancy, you also have to decide what to do with it. So you could decide to detect faults, or you could try to correct them, or you could go for infection, which means that you want to, as soon as something goes wrong, something is faulted in the computation, you want to make sure that the output is completely random so that an adversary cannot get any information from that. So suppose that we have an adversary that can do side channel analysis and fault attacks combined to make an even more devastating attack. So then we have to combine two of these countermeasures. So we're going to combine masking with some redundancy. So we have here, like, a mask's plain text, and the redundancy is also shared. And then we want to figure out how to implement, for example, AES in a way that we obtain a masked ciphertext and also the redundancy of the ciphertext in shared form. So if you look at the field of thresholds, cryptography, it's quite common to say that masking is like the embedded systems variant of multi-party computation because, you know, it's based on Shamir's secret sharing. And then in recent years, multi-party computation, that community has been looking at active adversaries. That means parties that don't only listen to protocols but also actively deviate from it and thus inject faults in the calculation. So we could see that as the MPC variant of what we were trying to do if you want to combine side channel and fault countermeasures. So I see two ways of getting where we want to get. So the first one is to take this one, to take an actively secure MPC protocol and translate it to two embedded systems. And this is what we did at crypto 2018 with a scheme called Kappa. So Kappa, because it's based on MPC, it comes with very strong security guarantees but it's also very expensive. So the alternative route is on this side. It's to take an existing masking scheme and extend it with a fault countermeasure. So in 2016 at crypto, there was party which is threshold implementations extended with error detecting codes. And last year at chess there was also a polynomial masking countermeasure extended with fault protection. And now this year I'm going to talk to you about M&M. So let's start with the essentials. What can our adversary do? So we have an adversary that can do side channel analysis and for this we're going to use the very popular deep probing model. And then the adversary can also inject faults. So in most cases we're going to assume that faults are random. They can reach any part of the chip. We're not going to limit them in the number of bits that are affected. But maybe the adversary can also has also like a very expensive precise laser that can exactly change some values. And then since those lasers are expensive, we're going to assume that this can only be done on some parts of the chip. So we're going to assume that it only happens in D shares of the D plus one that we're using. And then finally we also want to consider in some sense combined adversaries. But this is quite tricky. I should come back to that. So what's the redundancy in M&M? We're going to use information theoretic MAC tags. So not cryptographic MAC, but information theoretic MAC tags. And it's very simple. You just take for every data block X, you are going to accompany it with a tag. The tag is simply a Galois field multiplication of the data block X with some key alpha. This alpha has to be secret. And for every new encryption, you have to take a new one. So the assumption is that if the attacker wants to inject faults and not be detected, then he should also inject faults in the tags. And since alpha is secret, the probability that he succeeds in keeping them consistent is like 2 to the minus km. Which is based on the size of these tags. So what's our motivation behind these tags? So suppose that alpha would be fixed. Then you can see this as sort of a linear code, which is kind of like the party adversary model. But the party adversary model limits faults in Hemingway. And we didn't want to do that. So alpha can be anything. And then if we look at combined attacks, we have to assume that the adversary can inject faults using some information from side channels. And if he injects a fault on X, then he has to correct it with some fault on the tag. But if we make sure that alpha is secret, then even if he has this capability, then he has no way of finding out what the difference on the tag should be. So M&M starts from an existing masking scheme. So there are many masked multiplications to be found in literature, like really a lot. So this is just an example of a first order masked multiplier. And this is what we're going to start from. The question is how do we do nonlinear operations when we have data shares and tag shares? So how do we compute on this? So with the normal data shares, we can do the same as always. Just use your masked multiplier. But then for the tags, if we use the same masked multiplier, you end up with a shared representation of alpha squared times X times Y. And well, this is not a valid tag for X times Y. But we can correct this by doing another masked multiplier with a sharing of alpha inverse. And then you have your multiplication on data shares and tag shares. Now you can see that we have like a scaling factor of three compared to normal masked implementations because we need three of those red poles. With this linear operations, you can make any algorithm for M&M, but it's a little bit expensive, so let's see if we can do better. Suppose you want to implement a power map. And this is quite common because a lot of S-boxes are based on power maps. Suppose X to the power 3. You have a masked implementations of X to the power 3, and you apply it to your data shares and to your tag shares. So for the data, you're fine. And then for the tags, you end up with alpha to the power 3, X to the power 3, which you can convert into a valid tag for X to the power 3 by multiplying with alpha to the minus 2. And alpha to the minus 2 is very easily obtained from alpha inverse because squaring is a linear operation in masking. You can do it locally on each share separately. So this is quite nice because there are a lot of power maps in cryptography. For example, AES is a Galois field inversion, the S-box, which is the same as X to the power 254, which sadly is not an odd power. However, suppose you have a masked implementation of an inversion and you apply it to your data shares and your tag shares, then you end up with alpha inverse times X inverse, which is very easily converted to a tag for X inverse by multiplying with alpha squared. And again, alpha squared easily obtained from alpha. So you can see that our scaling factor went down a bit because inversions are a lot more expensive than multiplications, especially in the masked world. So our scaling factor has gone a lot closer to 2 instead of 3. So with these building blocks, you can make M&M implementations for any algorithm. And remember, there are a lot of masking papers out there, so you can find a lot of building blocks in there to plug in here. But now the question... So we have figured out how to compute with our redundancy, and then the question is now what? We have to actually use this redundancy to have protection against faults. So one thing we could do is maybe do an error check. But then you have to unmask the difference between what the tags are and what the tags should be, which is alpha times Z. So we don't want to do this because actually this is quite vulnerable to combine the tags. So we're not going to do that. Instead we want to use infective computation. So back in 2012, there was a paper on infective computation which used repetition as redundancy. So we end up with two ciphertexts, and then it would do an infection using the difference between the two ciphertexts. So the difference is used to kind of randomize the output so that if there is a difference... Oops. If there's a difference, you want to make sure that the output is random. If there is no difference, you output the ciphertext. But keep in mind also that if both ciphertexts have exactly the same faults, then you don't detect anything. But so this proposal was broken in 2013 because there was a bias on this random mask R. We wanted to do something similar minus the flaw. So we have our redundancy in the shape of tags. So we're also going to infect using the difference of what the tags are and what the tags should be. So this is this part. But we can do this share by share. And then if you look at the unshared output, you can see that if the tags are consistent, then this part here becomes zero and you're outputting just the ciphertext. If the tags are not consistent, then we randomize this and output something random. So of course we want to check that we are not introducing a bias here like they did before. So suppose that a faulty evaluation results in a faulty ciphertext with the fault being delta. We fill this in in our equation and we can cancel the actual valid part of the tags and we end up with this at the output. So to be safe, we want to make sure that this output mask is uniformly random and this is easily checked. So this is the case because we draw alpha uniformly from the field and r is also uniform but not equal to zero. So you can check this with a very simple Python script. And then so we applied this to AES, but remember again, sorry, I cannot say this enough, there are super many AES S-boxes out there in literature. There was even one presented this year at chess again. And so we used the S-box from the Knudda from chess 2016, but there's a lot more choices out there. So you might have different priorities based on area or latency. There are first order S-boxes that don't use any randomness or there are S-boxes that are tiny in area but have a lot of latency and there are S-boxes that have better latency. So the numbers that we have in our paper are not the only numbers you can achieve. And then secondly, it's quite difficult to compare our results to previous combined countermeasures because everyone considers a different adversary model. We've all implemented different ciphers and we've all used different libraries. But that's why we decided to only look at the overhead factor of the combined countermeasure over only a side channel countermeasure. So let's look at these results. So if we compare to Kappa, we know that Kappa has a very strong adversary model and comes with provable security and is very expensive and you can see that obviously we are cheaper than Kappa. And then when we compare to party, for first order, we see that the overhead factor is almost the same. But again, keep in mind, so for party, if the faults are limited in hamming weights, they actually detect it with 100% probability. And so for us, it's more like the adversary still has a success probability of 2 to the minus km. But then when faults have a larger hamming weight, then party does not detect them and for us, still the success probability of the adversary is 2 to the minus km. So different adversary models. And then we also did some evaluation of our implementation. So for side channels, the established method is to use TVLA. So this is for a second order secure implementation. So we did a bivariate t-test. On the bottom half here, you can see what happens if we turn the countermeasure off. So there's leakage in a lot of places. And this is a matrix because it combines time samples with each other. And then on the upper part, you can see what happens if you turn the countermeasure on. The leakage disappears everywhere. And this is with 50 million traces. And then we also wanted to do a little check for faults. But for faults, there is not this established verification method like you have with TVLA. So we kind of had to improvise. So what we did was we adapted our fairylock, our VHDL description. We added some extra extra gates. This created some extra inputs to the circuit with which we could determine to inject the fault. And so these inputs, we sent random inputs to that. And we did about 50,000 experiments. And of these experiments, there were only 189 where we got a faulty surface that the output that was not infected. This means that our experimental rate of detection is 99.6%, which nicely corresponds to the theoretical rate because we were using 8-bit tags. So that also gives 99.6%. So if you want to know more about this verification methodology, the poster session on Monday had my co-opter Victor presenting the tool Verfi, which is kind of like extending what we were doing here, but making it more automated and more comprehensive. So what do you want to remember? We have this new countermeasure, which is a lot cheaper than Kappa, but doesn't have the provable security against combined attacks that Kappa does. And then we don't limit our faults in hamming weights like they do in party. It's also very versatile. You can plug in any masked multiplication or any masked inversion that is in the literature, or that even still has to be published. In the paper, we also describe how you can combine the effective computation with detection, because you might have an application where you want to know if something went wrong. You want the flag to go up like, something went wrong. So we describe in the paper how you can actually combine this with the effective computation countermeasure that we used. And then finally, there's a lot to be done for future work because getting provable security against combined attacks seems to be quite difficult. We also don't have any verification tools for these combined attacks. How do you verify if the implementation is secure against combined attacks? And then you could do some optimizations with M&M where instead of always updating the attacks to correct to the Maki alpha, you could actually let the Maki alpha change between, for example, AAS. You would go from alpha to alpha inversion and alpha, et cetera. This is a possible optimization. So that was my talk. And if this gave you a craving for M&M's, and you still have your hotel room, then there are some very overpriced M&M's in your mini bar. So thank you. Time for questions. Yes? Just a very short question. About the party, what you have shown in slide number 24. Did you implement party by yourself? Because it's AES and party... Party was led, no? LED? LED, yeah. But then you compare here the LED with the AES? No, I got that number from your paper. But no AES, the party is implemented. I know. But maybe it was a bad implementation of LED. I mean you didn't implement party by yourself for AES? No, I just got these numbers from your paper. Okay. Because the code, the error detecting code, for the LED, the size of the code is 4 bits, right? Because everything is based on 4 bits and AES, you are in 8 bits. You have to use a different code. Yes. But we didn't... Like I said, there's different ciphers, and it's LED for party, and I just got these numbers from your paper. Okay, but then... Did I understand correctly that you are comparing the result of the AES with the LED? Or you have the M&M LED here? We have here... So our first order size was 19 kilo gates, and then the AES that we're based on first order protection is 7.6. But then this one comes from your paper, and also in your paper, you were mentioning that Seychelles only was this size. Yeah, but LED? No, LED for you, AES for me. Then I'm right that you are comparing the area size of LED with the area size of the AES. No. Okay, probably we have to take this offline. Maybe you have to take this offline. Are there any other questions? If not, let's thank Lauren again.


Hi everyone, Joe for Jasper's case breaks comm happy Wednesday. Happy new release day We're trying to get ourselves get into some immaculate basketball tonight So we got a couple of these fillers to fill up the number blocks Which helps fill up the big break five bucks immaculate pick your team one This is part of it big. Thanks to everyone who got into the action really appreciated and we've got the we got the draft About to happen in about 30 minutes. Oh, I see they got us with a pregame show About 30 minutes, we'll see them. Maybe we'll have a break of immaculate going by then There's all the teams in so first off we're gonna do the break itself first then we're gonna give away those two spots in the Lakers and 13 other high-end teams so it's a 14 team total One right there includes Includes where's the where's the team list on this? Wasn't this in the title a second? Losing my mind here. Oh, yeah, it's gonna be in the number block itself Lakers heat nicks bowls calves wizard Celtics bucks sixers furs Hornets thunder rafters clippers Those are the teams involved so you're trying to get spots in that number block that I just showed you a second ago So let's do it the break first. Let's roll it randomized. It's three and a six nine times for each list sub TJ one two three four five six seven And ninth and final time after nine times They got Charles all the way down to Fouad three and a six nine times for the teams one two three four five six And ninth and final time Hornets down to Rockets All right, so here's the first half of the list right here All right, there's the Pelicans for Brad It's a second after the list Matt Arnold with the Gris right there Matthew Powell with the heat. It's a good look at the teams there.  Let's copy everyone's names. Let's roll it randomize it top two after 11 one two Three and 11th and final time Wait, no, I didn't even get to 10 10. Let's get 10 There's 10 and now 11th and final time There's 11 right there top two good luck drum roll drum roll drum roll drum roll drum roll From three down to 30. Thanks. We actually have to do one more of these So maybe maybe try one more time see what happens. All right, congrats to Robert and Johnny There you go Robert and Johnny congrats to you You are in that Lakers that 14 team number block basically. So there you have it. I'm Joe I'll see you next time for the next one


My name's Andrea, and this is Rio, hello, and this is joint work with Virginia Vasilevska Williams, and we're going to be talking about public key cryptography in the fine-grained setting. In a world where one-way functions don't exist, where BPP is functionally equivalent to NP, a world called Pessiland, can we still have meaningful cryptography? Three intrepid researchers find out. All right, so what do we want in this world? Well, we want average case-hard problems, of course. We need BPP not equal to NP. But additionally, we don't just want that these problems are hard. We'd like to be able to efficiently generate both the problems and their solutions so we can get some nice one-way functions. But actually, we don't just want that, right? We also want public key cryptography. We want to be able to efficiently generate a problem that's hard and generate a trapdoor to that problem. But these rely on increasingly strong assumptions on BPP and NP. So we'd like to work with a different kind of assumption, a fine-grained complexity assumption, assumption based on polynomial hardness of problems. And what we mean by polynomial hardness here is that we want to distinguish between, for example, problems that take n squared time and problems that take n cubed time. So first, let's talk a little bit about some related works. Concrete security reductions focuses a lot on the efficiency of these cryptographic reductions, but they're focusing primarily on the change in the security parameter. We care a lot about our security parameter in these fine-grained reductions, but we also care about polynomial overheads in the reduction that don't affect the security parameter. There's been some really nice work on combinatorial crypto based on NP-hard problems, for example, subsetsum. And in the fine-grained complexity world, there's been quite a bit of work in fine-grained complexity. If you're interested, there's a survey paper from 2018 that's quite good. Talk to us after. But you know, more related to crypto, there's been some nice work on fine-grained worst case-to-average case reductions. All of this work so far has been fundamentally for counting problems. So we're able to show some counting problems that are hard, but there have been, unfortunately, some barriers against building crypto out of this. For some of it, it's an assumption called NSF, once again, talk to us after, but then there's also been some really exciting recent work showing that relative to a particular oracle, you in fact can't have fine-grained one-way functions, much less fine-grained PKE. So can we have a meaningful notion of cryptography even if we don't have exponentially hard one-way functions? You're still on. Yes. This is still you. All right. So first, let's talk about something that might immediately come to mind, Merkle puzzles. So Merkle puzzles do in fact require exponentially hard one-way functions, not just exponentially hard, but like very exponentially hard. Like two-to-the-end hard. So this was, the puzzle itself was described by Merkle, and then Biham Goran and Ishai later evinced a specific example with an assumption. And the gap you get there is if the honest parties run at no event time, then the dishonest parties are going to take n-squared time in order to violate the security. There's also been some really nice work. By Del Walker, Ben Kittenathan, and Boris Van in 2016, where based on an assumption about the hardness of NC1 with respect to L-slash-poly, you're able to get one-way functions in PKE, where the honest parties are NC1 circuits, and dishonest parties must be strictly stronger than NC1 circuits in order to violate the security. But what if we don't want to be working in circuits? If we don't want to be talking about P-slash-poly, we want to be talking about different polynomial times. We want to be working in the RAM model. Well then, in this work, based on an average KCRK clique assumption, we're able to build both one-way functions and public key encryption, where the one-way functions get a gap of n to the k over 2 based on average KCRK clique assumption, and our PKE gets n to the 1.5 minus a little bit. So, me is visiting that question from before. Yes, we can have a meaningful notion of cryptography, and we can use these fine-grained complexity assumptions to do it. So in this presentation, we're going to go over some definitions for what we mean exactly by fine-grained one-way functions and a fine-grained key exchange. We're going to go over our complexity assumption, our average case assumption, which will be based off of 0k clique, and then we're going to go over our construction, including the security reduction. So, let's get started in this wild, wild world of fine-grained crypto. To start, first we'll want to compare what a classical one-way function looks like compared to a fine-grained one-way function. Classically, you need to be able to evaluate your function polynomial time, and we say that if an adversary can invert your function, then that adversary has to run in super polynomial time. So you get this huge gap between what honest parties do to evaluate versus what an adversary does to invert it. Now, in the fine-grained setting, if we have an n to the c fine-grained one-way function, in this case it takes n to the c time to evaluate, and if an adversary inverts it, then that adversary has to take strictly, polynomially more time to invert, n to the c plus delta. So, that's one-way functions. We're going to use a similar sort of definition here when we talk about key exchange. So, in a fine-grained key exchange, just like any other key exchange, you'll have Alice and Bob exchanging some messages to produce a transcript. By the end of their protocol, they each can produce a bit that represents their key, and because this is fine-grained, they each run in, say, order n to the c time. And again, if you have an adversary that's breaking this key exchange, that's figuring out what that bit b is, then that adversary has to run in strictly more than n to the c time, n to the c plus delta. All right. So, this is what we want to get by the end of this talk. Let's talk about the tool we're going to be using to get that, our fine-grained assumption on zero k clique. Now, we said this was an average case assumption, so we need to start with some distribution. So, our distribution is we're going to start with a complete k-partite graph. In this example, k is three, and each part has n nodes in it, and then it's complete, so every single edge exists. And then we're going to label each edge with a weight. This weight is going to be drawn uniformly and independently from Z mod R. Finally, we're going to be planting a clique, a zero k clique inside of here, so we're going to choose, in this case, three nodes, a, b, and c, each in a different partition randomly. And we're going to choose one of those edges to be the negative sum of the weights of the other edges, forcing this to be a zero k clique. And our assumption is that finding a, b, and c takes at least n to the k time, meaning you have to check every single clique before you find the clique that you're looking for. Notice also that this is a search problem and not a decision problem, so it's, you know, even nicer of an assumption than your classic yes versus no. Now, when we talk about fine-grained key exchanges, the first thing that will probably pop into your head is a Merkle puzzle. Let's go over why a Merkle puzzle doesn't work in this case. This will be a good warm-up for what our protocol eventually looks like. So you have Alice and Bob. They've got this public input universe. Typically, it's, you know, all inputs, 0, 1 to the n. Alice is going to choose a square root subset of those inputs. So 2 to the n over 2 inputs for Alice. Bob is going to similarly choose a square root size input. And since they're choosing square root size inputs, we hope they only, they collide exactly once. And there's a constant probability of this occurring. Then each of them is going to publish evaluations on those inputs. And because they can see those evaluations, they can tell if they've evaluated the same point. So in this case, they've both chosen four. They've only chosen one overlapping input. So their key is going to be the preimage of that function. Now, in the fine-grained world, well, Alice and Bob have to evaluate this function 2 to the n over 2 time. And if your function takes, say, n squared time to compute, that's 2 to the n over 2 times n squared. So one of these terms is way bigger than the other. And if we're in the fine-grained world, an eavesdropper looking at this transcript can see, oh, hey, f of 4 is equal to f of 4. Let me just invert that one point. And they only need to take a fine-grained polynomial amount of time to invert. So this isn't going to work. We need to do a Merkle puzzle, but different. So for this, Alice and Bob are each going to generate all 0k clique instances. L is going to be a parameter we define based on n later. Then they're each going to choose square root L positions in these instances and plant solutions in those spots. And hopefully, again, they're going to collide in exactly one index because they're choosing square root. Then Bob is going to brute force check every spot that he planted an index inside of Alice's list. And Alice is going to do the same brute force check. So they are solving 0k clique instances. And then they find that, oh, hey, they both planted a solution at index i. Now, for entropy reasons, we can't just use i as the key itself. So Alice is going to publish a random 0, 1 vector of length log L. And the key is going to be the dot product between i and this vector v. So let's go over how much time it takes for Alice and Bob to run this protocol. Well, they have to generate L of these 0k clique instances. Each 0k clique instance has n squared edges. So that's Ln squared. They plant square root L solutions. That happens in constant time. Then they have to do the heavy lifting. They have to solve square root L of these instances. So that's square root L times n to the k. Now, it turns out the best value for L in this case that minimizes the amount of time Alice and Bob spend is L equals n to the 2k minus 4, excuse me, giving a total time of n to the 2k minus 2 for Alice and Bob. All right. So how much time should the adversary take? Well, we would hope an adversary just seeing these lists and not knowing where any of the solutions are planted has to go through and check every single one, brute force checking n to the 2k minus 4 instances, meaning n to the 2k minus 4 times n to the k time total to find the place where Alice and Bob intersect. But this needs a proof. So what we start saying is hard, right, is that this average case 0k clique location is hard. And what we want to reduce to is the problem where we've got two lists of instances and finding the index of the shared list, a shared solution is hard. So first, we're going to talk about three properties that we use in order to do this proof. So these three properties are list hardness. List hardness roughly says that finding which index in a list of L instances has a solution is hard. Now, that's not quite our key exchange. So we need something else. We need also that these instances are splitable. And what we mean here is that if we start with a random-looking instance that has no solutions, we can split it into two random-looking instances that continue to have no solutions. And if we start with a random-looking instance that has one solution, we can split it into a pair of random-looking instances that have solutions. Finally, we need that things are plantable, both for the key exchange itself and for the security reduction. So crucially, not only do we show that 0k clique has these properties, but also any problem that has these three properties will result in a public key encryption scheme here. So let's take a little look at a pictographic diagram of why these properties give us what we need. So we start with some problem that we, you know, is n to the k hard. And then we're going to split it into a list where if this list is formed by L instances of size n, you need to spend Ln to the k time using this list-hard assumption. Next, we split every single instance into, you know, its nice pair of either empty or non-empty solutions and finding this index is going to continue to be Ln to the k hard. Finally, in order to go back to looking like the scheme that Alice and Bob are running, we need to plant roughly root L minus 1, sort of false cliques into the remaining entries. Let's take a look at specifically why 0k clique is average caseless-hard to get a sense of what's going on here. So we've got our average case 0k clique instance. What we're going to do is we're going to split the vertex sets. So as a simple example here, we're going to split the vertex sets in two. And now we're going to look at all the sub-problems generated when you take one choice of each vertex set. So here we're taking the yellow moon sun and star. Here we're taking the green moon, yellow star and sun, etc.  But here there's eight instances when we specifically have k equals 3 and we're splitting in two. And one of these is going to contain our clique. So if we can point out where this clique came from, we can go back and solve that problem and find the clique in the original in the correspondingly labeled sections. So why should these problems be still brute-force-hard? Well, if we have L groups and we're dealing with a k-click problem, we're going to end up with L to the k sub-problems. And each sub-problem is going to be of size N over L. So we've got these, you know, large lists of relatively small problems. And crucially, the following statement is true. Our hardness for the original problem is roughly equal to the hardness of each individual problem raised to the k. So each of these problems is just as hard as the original. This is called the efficient self-reduction in worst-case land. So something you might be feeling a little nervous about right now is you're noticing that these edge sets are somewhat overlapping. Alice and Bob are producing these random instances, but there's correlations here. So it turns out there's a way to deal with these correlations. And if you're interested, please talk to us offline or read our paper. But yeah, there is a way to produce sub-lists such that these correlations become non-existent. So just to recall, we've got list hardness, splitability and plantability, which allows us to take a single instance of a search problem and turn it into an instance of this key exchange and show that finding this shared index is in fact going in faster than brute-force time is going to solve the original problem faster.  There's some correlations to deal with. There's some carries to deal with over here when we split our instance. And we sometimes double-plant. But as it turns out, all of these things can be handled. And once again, talk to us after if you're interested in how to handle these issues. So now we'd like to summarize. So we gave some definitions for what we mean by fine-grain one-way functions and a fine-grain key exchange. We showed you what our assumption looked like. We showed you an average case assumption that is based off of a worst-case-hard problem, but there is currently no reduction there from worst-case-to-average case. And we gave you a construction that ends up achieving a end to the 1.5 minus a little bit gap between what the honest parties do and what the adversary has to do to break it. And doing this, we showed that you only needed three properties. The worst-hard properties to be able to split your instance into multiple smaller instances. We had the splitability property, which we might have forgotten to mention, but it's basically you take an edge. An edge has a weight. You literally take the higher-order bits and split that into one problem, take the lower-order bits, split that into a different problem, then you get two instances. And finally, this plantability property where you can simply take an instance that doesn't have a solution and put one in. So this is a very new area. There's been worst-case-to-average case reductions, but as far as we know, there's been no work showing an actual key exchange using these fine-grained assumptions. So there are tons of open problems. First, end to the 1.5 isn't that great? Also, we are fundamentally bounded by using Merkle puzzles. We are bounded by this n-squared gap if we continue to use a Merkle style key exchange. Can we do better than that? In the full version of our paper, we do get an n-squared minus epsilon construction, but we chose to go over the end to the 1.5 today. Another open question is, can we build fine-grained crypto that has different useful properties, say, could we build fine-grained, fully homomorphic encryption? And because we are dealing with a relaxed security notion, one might think we'd be able to get better properties, like more efficient encryption. Maybe you don't have to deal with error or bootstrapping. It would be really nice to get something like that. Finally, when you have a one-way function in the traditional sense, you get all of symmetric crypto out of it. You get pseudo-random generators, pseudo-random functions. You get your symmetric key exchanges, or you get your symmetric key stream ciphers and stuff, but in the fine-grained world, it's much more difficult, because all of these constructions take one-way functions and add some polynomial overhead that makes it much more useful, but at the same time adds to the time it takes to run all of these protocols. In our paper, we show how to get fine-grained hardcore bits, but even that was tricky. We don't know how to get anything else out of it, so that's a big open problem. If you have any ideas on how to solve these problems or thoughts on what these should look like, please let us know. We're very interested. Thank you for your attention. We'll take any questions. Questions? Yes, I did. In your paper, you consider only time and ignore space. Now, the problem of zero-key-click sounds very similar to the problem of having a number of lists and trying to find a sum of elements from the list, which sums to zero, and this has a well-known time-memory trade-offs, which can reduce the time from below n to the k at the expense of having more memory. So have you considered the effect of having some amount of memory on the fine-grained complexity? Yes, so for specifically the zero-key-click problem, there isn't such a space-time trade-off known. The best time known for log space algorithms, even in the worst case, is the same as the best known when you have as much space as you want. It doesn't appear there's a nice trade-off there, unfortunately. The extra structure of the cliques as opposed to the lists makes some of that trade-off a lot harder. The fine-grained equivalence of what you're talking about is these case sum problems. It's absolutely true that there's these space-time trade-offs, and often there are these funny gaps you reach, where if you try and get below sublinear space, it takes way longer, but that structure doesn't appear to happen here. So... I now really saw that fine-grained would mean non-asymptotic security. Do you have any concrete parameters to propose? What do you mean by known asymptotic security? So your security results are purely asymptotic, right? Yes. And I saw that fine-grained would mean something more concrete than asymptotic. We want to... Sorry to disappoint you. We wanted to mimic the traditional world of crypto, but use fine-grained complexity techniques where everything is still very asymptotic. Your assumption makes sense on the naming, but we're following the naming of the complexity class. Any other questions? Okay, let's thank the speakers again.


Hello everyone and great to be here and I think I really want think about with a huge thank you to, Johnty and all the support, and to everyone just because emf is just fantastic place to be. So I just start there. Also going to start with some stories. This poster. It was taken, a photograph here. There is no photo shop involved. A lathown influences of a friend of mine called Chris Wιοanwchaft I'w'r ddiogel wedi'u trofod o'r llyw'r newydd, iawn o ddofod o isborgs bydd y eternig dros ffordd yw'r blwysgrif hefyd. Mae hynny'n ames yn dysw i gael'r cynhyrchu o'r bwysig. Gynhyrchu hwnng i'r corffon o'r newydd, y gallwn yn ynion i ddefnyddio'r llydy ddechydig hynny. Felly, mae'r angen i'r meddyl, gan heddiw y dyfod am amser o gweithio, ddyn ni'n gwybod yn gweithio am lawer isiau ar gyfer gweithio ac wedi gwasanaeth tusiaeth ar gyfer gwan, ar gweithio y Twngr, a wedi eu hwnnw'r aelodau hyn. Dwi'n fNOedd y ddadffod ag cyfnodydd iaith argymde SDGN, a felly sefydlu y ddal, yn gallu gwneud hynna a'r gwrth o'n ddwy'n unig i'w ddyn ni wedi bwysig i gael y ddylch. Here we are again, so what's different from when I started on this near on 20 years ago is the date is just as much of a mess as it was back then. Most people are still working on spreadsheets and it's not a great starting point. Ond we have got some things that are different, and I think when we look at what is the role of the hacker community and what does hacking mean in this context. I think it's broader than just tech hacking, although we need developers in the mix here. We need people who are good at social hacking, good at financial hacking, as in creating different types of finance and hacking policy. Actually working at how we can rewrite things to be more effective. On that journey over the years, I've got more involved sitting in between bits of government, bays, off-gem, the energy regulator, working with the financial conduct authority, the pensions regulator, the Bank of England, those kinds of people, through to BlackRock. I've got all the money sitting in these rooms and there's people who are in control basically of trillions of dollars of finance. They're all asking the same question. I think one of the things that is materially different is that the people in those rooms are genuinely trying to find a way forward, but I'm going to talk to some of those challenges. So today's talks mostly about pointers and ideas and things you can do to get more informed about where the levers of change are around climate. This needs everyone, but I want to start off with just some straightforward things that everybody can do, and I call this, there was a friend of mine many years ago said a thing called do the green thing. Actually I think here there's a framing of do the dull thing. A lot of this is about plumbing and looking at the financial ecosystem in which we exist and saying, what can we do to affect where the money flows? Because that's going to dictate how we invest and what the carbon footprint of everything is. Just to put a frame around this, the organisations that are able to think for more than a few years, even into the decades, are quite narrow in their scope. Pension funds and insurance companies are among the small number of groups that have to think on a timescale of decades. And when you think we've got a legally binding target now in the UK and other countries around the world, to hit net zero by 2050, actually most of that footprint is going to be baked in in the next decade, in fact in this decade. Because all of the infrastructure, the power systems, the transport systems, the water systems, agriculture, et cetera, that we embed tend to last for decades. So we've got to do something now to redirect that capital flow. So I'm going to flip here between money, tech data as we go through this, but three straightforward actions you can do is call up your bank or your insurer, or if you've got pension, call up your pension provider and ask them what are their net zero commitments, right? And what do they actually mean by them? You'll see a lot of very, if you go to the kind of corporate websites, you'll see lots of PDF files, lots of pretty good images in them. But there's very little in the way of tangible commitments to this amount of reduction by this date. Or yes, we're heading towards net zero by 2050 as a general sort of statement, as a target, but the roadmap and the plan of how to get there is less clear. And that's because we don't know. So it's not necessarily trying to hide anything. It's like it's really actually quite difficult. But what we need to be asking is how are they going to demonstrate those measures? How are they going to measure their progress and what does success look like? And then a really provocative question is how is the chief executives pay and the boards and the senior executive teams pay linked to hitting those targets? Now, these are quite, you know, these are all about by creating the right incentives. If we have a million people calling up a pension provider and saying we want to know these things, they're going to really pay attention. And I want to also emphasise you should be asking this question of your own organisations.  Take them out for a beer, go out for a cup of coffee and say, what's really going on here? Do we believe our own rhetoric here? Are we actually doing something material? And are we going to deliver net zero or are we just heading towards a lower carbon future? Because that's quite a different thing. I think we're well on track to being lower carbon that doesn't actually solve the problem. And there's a great clip, which I strongly recommend to you going to look up from the two Ronnies. As you can tell from the screenshot here, it's a very old clip. But one of my reflections here is that in talking to everybody, and I feel like I've really been through the whole system, you talk to people really low down in an organisation, they desperately want to do something about climate change. Now, whether their sense of agency is introducing recycling or helping to get renewable energy on board, there's still a sense of frustration. Oh, well, it's my boss that is the issue. So you're going to talk to them and they say, well, it's not really me, it's a senior management. Senior management say, oh, it's not really us. It's up to the chief executive or the C-suite. And you go to the C-suite and the chief executive. Well, it's actually up to the board if we don't get the board on to make the decision here. And the board then say, well, it's the shareholders. It's the pension funds. And then the pension funds say, well, we're regulated to make sure that the people in the organisation have a pension. So we've got to take a really high risk analysis here of where do we invest? And if you look back over the last 100 years, very simple equation, which is typically done on spreadsheets, the returns from oil and gas are more predictable than the returns from solar and renewables. Just because there's more data. So there's a loop. So you're right back at the beginning of that journey. So we all need to individually say, we give you permission to these pension funds and so on to say, we want to see these changes. We want you to invest differently. Yes, we know there's risk associated with that. But linked with that, we also need to change the regulation because I was sitting down with a number of the banks. They say, well, we're regulated. We're not allowed to take risk. If we take too much risk and people can prove it, we get fired or we get fined or in some cases we go to jail. So there is a huge amount of fiduciary legal responsibility in these organisations to make sure that everything stays stable. And the challenge there is when you start looking at the systems risks that we're facing, it's massive. So here it's not a technology problem. It's more of a social problem about our sense of individual agency in the mix. So next time you think, and what can I do? Yes, do the solar panel thing. Do the renewable energy. Do all of those things. But also call up the people who control your money. Because they're the ones making those high level decisions. And this is a description of our economy. It's quite straightforward. We only back things where the line goes up. Right? This is still true. We don't like lines that go down. I don't know if it's really dumbing it down, but that's been my experience. Unless you can show a line going up, we don't get the engagement. Because economists don't like to see efficiency. Right? We want to see growth. We're in a growth economy. Now, we might be able to refactor capitalism, but we're not going to do it in the next eight years. So we need to find out how other ways of influencing the system. But we also need to take in mind into perspective here that we have got a situation where we're doing the same thing over and over again. And that is kind of the definition of insanity. And at the same time, things are as simple as they're going to be. Everything from today forward, and this is always true, from today forward, it's only going to get more complex.  We wouldn't have necessarily predicted the war in Ukraine. So all of these things create multiple factors, which when you're already in a stress situation, which the financial systems, our economies, our societies already are, trying to take on board these additional things just makes things more and more complicated. So we now have to try and start thinking differently about how we're going to imagine the future. Now, there are changes. So at COP last year, the climate conference, the various groups announced they're going to follow a thing called TCFDs, climate-related financial disclosures. Most large companies, your pension funds are now mandatory required to publish a report that says what their climate risks are. And I'm going to come on to that a little bit more in a moment. But fundamentally, how finance uses the data, you've got this really interesting kind of mix of quantitative information and qualitative information. And there's a lot of things under the hood of that. And what I wanted to drill into a little bit today is the difference between our financial economy and the real economy. And that language tells you a lot of things that are fairly self-evident. But I think this is one of the learnings that I'd bring today is that our financial economy really looks at financial variables. So this is things like your hedge funds, share prices, interest rates, and they're all geared to line grows up. And then underneath that, you've got the real economy, which is the non-financial elements of our economy, which are things that we produce and the flow of goods and things like that. So those two things are now having to come together in ways that they haven't really had to before. Now, I'm going to just pause there and come back into what's at stake here. You know, when we think about risk, and this is where companies and our economy isn't really geared up to thinking on these time frames because they're looking at next quarter's results or the next two years or the next three years rather than the next 10, 20, 30 years. But we are looking at what is at stake in this century, so in the lifetime of some of the people in this room. And the scientific community here is incredible. There's tens of thousands of people working on the actual climate science, working on what are the implications. But science really struggles to communicate to finance and to politics in a way that is meaningful and has traction. And the language of the IPCC reports, the international group that looks at climate, has got more and more blunt over the years. Basically, they've gone from like, oh, there's a significant risk or it's unequivocal. You know, when a scientist says something is 70% likely, they mean this is going to happen, but they tend not to stand on the table and sort of wave flags. If you say 70% likely to a politician, they go, oh, it's 50-50 then. Or it's probably 40-60 in my favour. Or something like that. There's a real translation gap, but the science here is really quite unequivocal, which we've heard for ages. And you can see that when we have stresses on our systems, so this dip here at the top right of our emissions over time, the dip there was COVID. So COVID did actually stop us doing this. It radically reduced our consumption and our emissions, but not as much as we might have expected. In fact, that also played out over different industries and sectors in different ways. You can see a massive dip there in the ground surface transport, aviation, and so on, residential went up a bit, of course. And you can see these differences over time through what's called extreme confinement at home. So stop doing things. Now, this is something to learn from. We need to stop doing things, as well as investing into renewables and all the other PCs. There's a billion cars on the road worldwide right now. If we're going to switch them all of them to EVs, we're actually going to create a huge footprint just from the creation of the vehicles. Never mind the creation of everything else. So we have to work out how we're going to do mobility differently and so on, because the curve here needs to do this. Now, if you're in an economy where line goes up and the line going up in the economy generates emissions, even if you're switching huge amounts of capital, and you look at a dip like COVID and say, well, we've just bounced back to roughly where we were before. Something radical has to shift. So we have to start thinking about how do we make the line going up really deliver net zero? How can we refactor that? So there's a hack in there somewhere, a psychological hack, to have a line that somehow goes up that takes us to net zero. And it's not just investment in renewable energy. It's also some kind of form of efficiency and some form of reduction where actually the line is going down, but somehow it looks like it's going up. So I'll leave that as an exercise for the reader. And so what we're looking at here is then how do we get data to help us with this journey? And I've heard over the years, many people come to me and say, we just need all the data sat in a room with many financial people representing probably an aggregate about 15 trillion's worth of assets worldwide. And they said, look, we just need all the data. I said, well, that's a really useless question. At which data do you need specifically? And they said, just all we need is all the industry data about infrastructure, so water, energy, agriculture.  So that's better than everything. At least we can start to chunk that up a little bit. And let's then pick it out by sector. And then how do we get this really economy piece, the actual production of things? So here we've got resources that get distributed and then they get used in making things.  There's a bunch of methodologies and models that are applied to those consumption pieces. They get put into carbon accounting packages and turn into reports, the kind of TCFD one that I mentioned earlier. Now those reports then also have lots of qualitative statements about the future. So organisations will say, we'll be net zero later. Trust us. And it's a bit like saying we're definitely going to be profitable in three years. We need to be able to hold people to count far better than we are at the moment. It is not a pleasant situation at the moment. Give you one example within the insurance sector. If you're going at the top level of these organisations, they'll also, yes, we've got a team working on climate change that are doing forward projections and using AI and machine learning and probably there's a blockchain somewhere in there as well. If you go in at the ground level, and I've done this more than once now, it was one national insurance company and I went and spoke to the intern and they said, yeah, I got emailed 15,000 spreadsheets a year from our customers. I copy and paste them into two black box softwares of service risk modelling tools. They spit out a number. We take the average. We adjust that based on last year's number and that's how your insurance premium gets set. Now, as you can tell from that, firstly, what on earth are you doing? Secondly, that's not incorporating at scale all of the future predictions because when you start factoring those in, it starts to really shake some of the foundations of what the economy should be looking like in the future. And that's why it's hard. It's really difficult for organisations to engage with this because it really introduces fairly systemic different types of risk. It's also really difficult for countries to then work with each other because you say, well, if you keep going this way, our country is going to flood. So one of the outcomes that's quite likely there's plenty of evidence pointing in this direction that Florida, for example, might be one of the first areas that just becomes goes underwater because the ground in a lot of Florida is porous. So you can't even build a levee. It just comes up through the ground. But there's still property development happening on the coast of Florida. It's a bit crazy. But the second, the last insurance company pulls out of Florida, the housing market will collapse and then you'll see a systemic economic collapse in that area. So why we're looking at finance and looking at these kind of financial instruments is because they have to care first. The insurance companies have to care first because they're carrying a lot of the risk. The pension companies have to care first because they have to protect an investment that lasts decades. And at the moment when you join these things up, these reports are used by ratings agencies. They're used by investors. They're used by fund managers in the market. And then auditors, at the moment auditors around the world are hiring people with what's called ESG skills, environment science and governance skills. So much so they're actually creating a bit of a vacuum in the market. They're just hoovering these people up because everybody knows that they're going to have to audit these environmental reports but they're massively complex. It's right down to how much steel was consumed in a particular project, what was the carbon intensity at the time of use and so on. So somehow we need to digitise this entire system. But digitising this entire system isn't a technology problem per se. It is a little bit because you've got one, some person who's got 15,000 spreadsheets. But that's a relatively solvable problem compared with the policy instruments you need to be in place to make all this data flow. And so part of the work we're doing at Icebreaker One has been to try and take the principles of open banking and some of the work we've done at the Open Data Institute forward into policy instruments or into things that could be used in procurement or other financial contexts to help this data flow and to almost mandate it. We'd love investors to mandate the data flow. Through this system so they can have confidence in what they're investing in. Excuse me. So just bring that back. So what can we do, right? So we've got this something we've all got a role to play. We're not powerless in the mix. So I want to come back to these kind of asks here which we touched on at the beginning. Why would we want to ask these questions? Well, because the investors, the pension funds in particular need to know from their stakeholders which are us, right? They need to know that this is what we want and we need to be able to articulate the questions to them in a way that is meaningful and creates the right incentives. They also need to have measurable plans and while some are getting there there is a vast amount of work to be done. And the UK is actually one of the leaders in this field. The other thing that's happening is regulation is coming. So across the EU the climate reporting is now mandatory for all large funds effect. So there's about 50,000 companies that are affected. In the US the regulator, the SEC is now mandating the reporting of climate risk. However, that's at the end of the process. So we've now got to say, well, if you're reporting what the risk is what are you going to do about it? And there's a world of pain ahead there. And a lot of the capital is frozen because the investors are terrified of greenwash. It's not just, we all hate greenwash but also do they. They don't, because they don't want to be on the front page of the news with the latest greenwash thing. Also for their own shareholders. For their own investors. They need to make sure that they're investing in the right thing but they don't necessarily have the evidence and they can't necessarily prove what the outcomes are. So there's a huge scope here for innovation and hacking at multiple levels of the system. How can we prove that this particular renewable technology is going to scale? If we can de-risk that, then it will have a huge amount of investment thrown at it. I think the latest number I've heard actually when I set up Icebreaker I set as a target of trying to influence $3.6 trillion per annum of investment. It sounded a bit, well it is a bit ludicrous but the reason for picking that number is that's roughly how much we spend on infrastructure globally per annum and it all needs to be demonstrably net zero. The latest numbers coming out of McKinsey is probably three or four times that in terms of the amount of investment it's going to take to get the whole world to get to net zero. So there's a massive amount I think we can do and this is really call to action for all open communities developing open source solutions developing social solutions developing financial instruments. You know if you want to go learn about how does an insurance policy work or how does an investment product work we've just been 18 months working with Lloyds and Aeon and people like that. What is a climate product anyway? All of that's on our websites all creative commons licensed and for those working on things like policy whether it's internally within a company or working with local government or working with national government there's a huge amount to be inserted in there as well. And if you're working inside an organisation just keep asking the question just keep socialising these questions. This is the most powerful thing we can do as a community is just the more we ask the questions the more people will feel that they've got permission to step into that space and take more risks and that will give them confidence to take those risks. So I'm going to finish there thank you we've got posters here and stickers if you want and this presentation is at the link at the top right of the screen. Thank you very much. Thank you very much Gavin.


Hello everybody. In this training you are going to carry out the analysis of the transcript term of the model organism Arabidoptis thaliana. The ultimate goal of this tutorial is the identification of potential elements belonging to the brassinosteroids mediated gene regulator network. The tutorial is divided in two blocks. In the first one, we will explore the steps necessary to perform the analysis of micro-irona transcripts. In the second block, which will be developed by Pafun-Kumach by then, we'll carry out the analysis of the total transcripts as well as the target identification. Let's start with the introduction. As cesil organisms, the survival of plant under adverse environmental conditions depends to a large extent to their ability to perceive stress stably and respond appropriately to counteract the potentially damaging effects. Coordination of phthermones and reactive of jetting species are considered a key element for enhancing stress resistance, allowing fine tuning of gene expression in response to environmental changes. These molecules constitute complex analgenic words, endowing with the ability to respond to a variable natural environment. There's currently a broad consensus in classifying plant hormones into 9 groups. Absicic acid, oxins, cytokinins, ethylent, gibrelins, jansmonate, salicylic acid, strinolactones and bracinosteroids. VIN, this lab group of phthermones, the one on which we are going to focus our analysis. Bracinosteroids are a group of plant steroid hormone essential for plant growth and development, as well as for controlling abiotic and viotic stress. Structurally, bracinosteroids are polydroxylated steroid-derivated with close similarity to plant hormones. Bracinosteroids have the ability to stimulate plant growth, influencing germination, rise of genesis, flowering, senescence, abscessing and ripening process. In addition, several experimental results have demonstrated the ability to confer a resistant to several types of biotic and biotic stress, such as heat, coal, salinity and growth. Signs bracinosteroids control several important agronomic threats, such as stress tolerance, the study of gene regulatory network in which this hormone is involved have acquired great importance in the field of biotechnology, offering enormous potential for increasing growth yields. According to several studies, microRNA are one of the molecular mechanisms involved in the regulation of bracinosteroid-mediated gene expression networks. MicroRNA are 20 to 22 nucleotide-small RNAs characterized for regulating gene expression at porous transcripts and a level. MicroRNAs are distinguished from other small RNAs by being generated from precursor, harboring an imperfect stem block structure. Unlike in animals, the pre-processing of plant microRNAs occur in the nucleus. The pre-microRNA are then exported to the cytoplasm after methylation and then incorporated into the argonate-1 protein to form the ricks complex. The microRNA itself doesn't have the ability to cleave mRNAs or interfere with translation, but it plays a role in scanning the appropriate target. Power factors determine that the microRNA are considered master regulators. The first one is that multiple microRNA genes are regulated under given environmental conditions. The second one is that computational prediction estimates that each microRNA regulates hundreds of genes. The third factor is that the majority of plant microRNAs regulate genes encoding for transcription factors. And the last one is that target genes include not only mRNA, but also long non-coding RNAs. Now we'll introduce the experimental design on which the analysis is based. As mentioned above, we can divide the analysis into three stages, differential expression of microRNAs, differential expression of mRNAs, and target identification. The starting hypothesis is that there should be sequence complementarity between upregulated microRNAs and downregulated mRNAs. In this tutorial, we'll focus on the differential expression of microRNAs. The result obtained will be used later for target identification using the Tiger 5D tool. Now we'll provide some details about the data and the tools. For the analysis, we'll use data generated by the Illumina Genome Analyzer 2X sequencing platform. MicroRNA was sequenced from three viological replicate samples of Arbidoxy-Stalina in each of two conditions, control and epipyrocynolite treated synthylins. In order to simplify the analysis, the viological replicates will be grouped in Galaxy into data collections. Collections allow to combine numerous datasets in a single entity that can be easily manipulated. In this part of the tutorial, we'll use six main tools, which can be divided into three categories, quality assessment, microRNA quantification, and differential expression. FastQC provides a simple way to do some quality control checks on raw sequence data coming from high throughput sequencing. MultiQC allows aggregated results generated by FastQC across many samples into a single report. Tringalore is a wrapper tool around kubeladat and FastQC, which allow consistently to apply quality and adapt their trimming to FastQ files. To carry out the microRNA quantification, we'll use two modules belonging to the MIRDEEP tool, MIRDEEP MIPER, and MIRDEEP QUANTIFYER. Finally, we'll use DSEC2, a package for differential expression analysis of common data based on negative binomial distribution. Now, let's start.


In this video, we will explore the impact of mental health on attraction, and how it may lead you into the arms of an unhealthy someone. Love Blueprint If you want to find out the roots of your choices, psychotherapist Amy Moran suggests looking for clues in your childhood. Did one of your parents constantly need fixing and reassurance? Do you have to walk on eggshells around them? It might be why you believe that's what relationships are all about. And that's how your relationship blueprint is created. It's the set of beliefs, values, and expectations that shape how you approach and navigate romantic relationships. Life coach Desis Lava Ivanova explains that our unconscious mind is programmed to seek out people who match our blueprint. Your mind is incredibly skilled at recognizing patterns, and it takes all the positive and negative characteristics of your parents or caregivers and combines them into the blueprint. And when you grow up, you're naturally drawn to people that match it. Unfortunately, this can sometimes attract you to toxic partners who awaken old traumas.


So we've learned about forces, which are one way to figure out how things behave in classical system And if you know that all the forces you can work out what's going to happen in the future that can be quite complicated though Simple ways of working things out involved looking at conserved quantities And the first one we saw was momentum now momentum is always conserved. It only has one form The only disadvantage is it's a vector So it has three components it's got a magnitude and a direction And so when you write down a conservation of momentum what you're really writing down is three conservation laws The conservation of momentum in the x-direction, the conservation in the y-direction and the conservation in the z-direction all in one hit A simpler quantity that only has a scalar quantity is something like a charge and charge is always conserved But problems that require us to use the conservation of charge don't turn up that often except in more exotic situations So while being completely true and very important, it's not very useful There's mass, mass is just a scalar, but it's not always conserved. It's mostly conserved That said at non relativistic speeds and so forth when you're not worrying about nuclear reactions Mass is effectively conserved So you can use things like conservation of pens or conservation of storm water if a lot of water goes into a drain You can figure out how fast the water has to be coming out the other end You can work out traffic flow and so forth using conservation of mass. That's a good rule of thumb And we've also learned about conservation of energy. Now energy is important in lots of everyday situations It's a scaler. So that's very handy and it's always conserved, which is great Any problem is it comes in lots of forms and so to use it You have to learn all the different forms of energy and track how energy travels from one to another And in fact this tracking of how energy and learning how to use energy is one of the main things that enabled us to change our Technology over the last few hundred years from all the millions of years of fairly static technology beforehand