From the CUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. Hey, welcome back. You're right, Jeff Frick here with theCUBE. We are doing a special presentation today, really talking about AI and making AI real with two companies that are right in the heart of it, Dell EMC, as well as Intel. So we're excited to have a couple of CUBE alumni back on the program. We haven't seen them in a little while. First off from Intel, Lisa Spellman. She is the corporate VP and GM for the Xeon Group or Xeon and memory group. Great to see you, Lisa. Yep, good to see you again too. And we've got Ravi Pinterkanti. He is the SVP server product management also from Dell Technologies. Ravi, great to see you as well. Good to see you, Jeff and Lisa, of course. Yes, so let's jump into it. So yesterday, Ravi, you guys announced a bunch of new kind of AI based solutions. I wonder if you can take us through that. Absolutely. So one of the things we did, Jeff, was we said, it's not good enough for us to have a point product, but we talked about, I hope, the total of products, more importantly, everything from our workstation site to the server, to these storage elements and things that we are doing with VMware, for example. Beyond that, we are also obviously pleased with everything we are doing on bringing the right set of validated configurations and reference architectures and ready solutions, so that the customer really doesn't have to go ahead and do the due diligence on figuring out how the various integration points are coming across in making a solution possible. Obviously, all this is based on the great partnership we have with Intel on using not just their CPUs, but FPGAs as well. That's great. So Lisa, I wonder, I think a lot of people, obviously everybody knows Intel for your CPUs, but I don't think they recognize kind of all the other stuff that can wrap around the core CPU to add value around a particular solution set or problem set. I wonder if you can tell us a little bit more about the Xeon family and what you guys are doing in the data center with this kind of new interesting thing called AI and machine learning. Yeah. So thanks, Jeff and Ravi. It's amazing the way to see that artificial intelligence applications are just growing in their pervasiveness and you see it taking off across all sorts of industries and it's actually being built into just about every application that is coming down the pipe. And so if you think about needing to have your hardware foundation able to support that, that's where we're seeing a lot of the customer interest come in and not just of course at Xeon, but like Ravi said, on the whole portfolio and how the system and solution configuration come together. So we're approaching it from a total view of being able to move all of that data, store all of that data and process all of that data and providing options along that entire pipeline of moving it. And within that on Xeon specifically, we've really set that as our cornerstone foundation for AI. If it's the most deployed solution and data center CPU around the world and every single application is going to have artificial intelligence in it, it makes sense that you would have artificial intelligence acceleration built into the actual hardware so that customers get a better experience right out of the box regardless of which industry they're in or which specialized function they might be focusing on. It's really wild, right? Because in the process, right, you always move to your next point of failure. So having all these kind of accelerants and these ways that you can carve off parts of the workload, part of the intelligence so you can optimize better is so important. And as you said, Lisa and also Ravi, on the solution side, nobody wants general AI just for AI's sake. It's a nice word and interesting science experiment, but it's really in the applied AI world is where starting to see the value and the application of this stuff. And Ravi, I wonder, you had a customer you wanted to highlight, Epsilon, tell us a little bit about their journey and what you guys did with them. Great, sure. I mean, if you didn't start looking at Epsilon, they're in the marketing business. And one of the crucial things for them is to ensure that they're able to provide the right data based on the analysis they run and what is it that the customer is looking for? And they can't wait for a period of time, but they need to be doing that in their near real-time basis. And that's what Epsilon does. And what really blew my mind was the fact that they actually service or send out close to 100 billion messages. Again, it's 100 billion messages a year. And so you can imagine the amount of data that they're analyzing, which is in petabytes of data, and they need to do it real-time. And that's all possible because of the kind of analytics we have driven into the powered servers, using the latest of the Intel Xeon processor coupled with some of the technologies from the FBGS side, which again allows them to go back in and analyze this data and serve it to the customers really rapidly. You know, it's funny. I think MarkTech is kind of an under-appreciated world of AI and machine-to-machine execution, right?  It's a really sophisticated, and as you said, massive amounts of data going through. So, pretty interesting stuff. And if it's done right, it's magic.  So you got to have a sharp tool. You got it.  If you don't really act on the right data, right? So that is where I think it becomes important. And also, if you don't do it in a timely fashion and you don't service up the right content at the right time, you miss the opportunity to go ahead and grab the attention. Right, right. So Lisa, kind of back to you. You know, there's all kinds of open source stuff that's happening also in the AI and machine learning world. So we hear things about TensorFlow and all these different libraries. How are you guys, you know, kind of embracing that world as you look at AI and kind of the development we've been at it for a while. You guys are involved in everything from autonomous vehicles to the Mar-Tech as we discussed. How are you making sure that these things are using all the available resources to optimize these solutions? Yeah, I think you and Ravi were just hitting on some of those examples of how many ways people have figured out how to apply AI now. So maybe at first it was really driven by just image recognition and image tagging, but now you see so much work being driven in recommendation engines and in object detection for much more industrial use cases, not just consumer enjoyment, and also those things you mentioned and hit on where the personalization is a really fine line you walk between how do you make an experience feel good personalized versus creepy personalized is a real challenge and opportunity across so many industries.  And I think our strategy is really a stacked strategy that starts first with delivering the best hardware for artificial and intelligence. And again, Xeon's the foundation for that, but we also have milliwatt type processing for out at the edge. And then we have all the way through to very custom specific accelerators into the data center. Then on top of that, the optimized software, which is going into each of those frameworks and doing the work so that the framework recognizes the specific acceleration we built into the CPU, whether that's still boost or recognizes the capabilities that sit in that accelerator silicon. And then once we've done that software layer, and this is where we have the opportunity for a lot of partnership is the ecosystem and the solutions work that Robbie started off by talking about. So AI isn't, it's not easy for everyone. It has a lot of value, but it takes work to extract that value. And so partnerships within the ecosystem to make sure that ISVs are taking those optimizations, building them in, and fundamentally can deliver to customers a deployable solution is the last leg of that strategy, but it really is one of the most important because without it, you get a lot of really good benchmark results, but not a lot of good, happy customer. Right. I'm just curious, Lisa, because you kind of sit in the capboard seat, you guys at the core, you know, kind of under all the layers, running data centers, running these workloads. How do you see kind of the evolution of machine learning and AI from kind of the early days where it was science projects and really smart people on mahogany row versus now people are talking about trying to get it to like a citizen developer, or really a citizen data science and exposing in the power of AI to business leaders or business executioners, analysts if you will, so they can apply it to their day-to-day world and their day-to-day life. How do you see that kind of evolving? Because you not only were in it early, but you get to see some of this stuff coming down the road and design wins and reference architectures. How should people think about this evolution? It really is one of those things where if you step back from the fundamentals of AI, we've actually been around for 50 or more years. It's just that the changes in the amount of computing capability that's available, the network capacity that's available and the fundamental efficiency that IT and infrastructure managers can get out of their cloud architectures has allowed for this pervasiveness to evolve. And I think that's been the big tipping point that pushed people over the sphere. Of course, AI went through the same thing that cloud did where you had maybe every business leader or CIO saying, hey, get me a cloud and I'll figure out what for later. Get me some AI, we'll figure out if we can make it work. But we're through those initial use pieces and starting to see business value derived from those deployments. And I think some of the most exciting areas are in the medical services field and just the amount, especially if you think of the environment we're in right now, the amount of efficiency and in some cases, reduction in human contact that you could require for diagnostics and just customer tracking and ability to follow their entire patient history is really powerful and represents the next wave in care and how we scale our limited resource of doctors, nurses, technicians. And the point we're making of what's coming next is where you start to see even more mass personalization and recommendations in that way that feel very, not spooky to people, but actually comforting and they take value from them because it allows them to immediately act. Ravi referenced the speed at which you have to utilize the data. When people can immediately act more efficiently, they're generally happier with a service. So we see so much opportunity and we're continuing to address across, again, that hardware, software and solution stack so we can stay a step ahead of our customers. Right, that's great. And Ravi, I want to give you the final word because you guys have to put the solutions together and actually deliver them to the customer. So not only the hardware and the software but any other kind of ecosystem components that you have to bring together. So I wonder if you can talk about that approach and how it's really the solution at the end of the day, not specs, not speeds and feeds. That's not really what people care about. It's really a good solution.  Most of us probably use the ATM to withdraw money but we really don't know what really sits behind the ATM. My point being that you really care that particular point in time to be able to put your ATM into the machine and get your dollar bills out, for example. Likewise, when you start looking at what the customer really needs, what Lisa hit upon is absolutely right. I mean, what they're looking for and you've said this on the whole solution side of the house. So our mantra to this is very simple. We want to make sure that we use the right basic building blocks, ensuring that we bring the right solutions using three things. The right products, which essentially means that we need to use the right partners to get the right processors in, GPS or GPUs in. But then we take it to the next level by ensuring that we can actually do things. We can either provide no ready solutions or validated reference architectures. The idea being that you have the sausage making process that you now don't need to have the customer go through. In a way, we have done the cooking and we provide a recipe book and you just go through the ingredient process of curing this and then off your off to go get your solution done. And finally, the final stages, there might be help that customers still need in terms of services. That's something else Dell technology provides. And the whole idea is if customers want to go ahead and have some help deploying the solutions, we can also do that via services. So that's probably the way we approach or that's the way we approach providing the right building blocks, using the right technologies from our partners, then making sure that we have the right solutions that our customers can look at and finally if they need deployment help, we can do that via services. Well, Ravi, Lisa, thanks for taking a few minutes. That was a great tee up Ravi because I think we're going to go to a couple of customer interviews enjoying that nice meal that you prepared with that combination of hardware, software, services and support. So thank you for your time and great to catch up. Yeah. Thank you. All right, let's go ahead and run the tape. Hi, Jeff. I wanted to talk about two examples of collaboration that we have with the partners that have yielded real examples of output through HPC and AI activities. So the first example that I wanted to cover is with the Neuromod team up in Canada. With that team, we collaborated with Intel on a tuning of algorithm and code in order to accelerate the mapping of the human brain. So we have a cluster down here in Texas called Zenith based on Xeon and Optane Memory. And we were able, with that customer, with the three of us, our friends at Intel, the Neuromod team and the Dell HPC and AI innovation engineering team, to go and accelerate the mapping of the human brain. So imagine patients playing video games or doing all sorts of activities that help understand how the brain sends the signal in order to trigger responses in the nervous system. And it's not only a good way to map the human brain, but think about what you can get with that type of information in order to help cure Alzheimer or dementia down the road. So this is really something I'm passionate about is using technology to help all of us and all of those that are suffering from those really tough diseases. I'm Julie Boyle. I'm the project manager for the Courtois Neuromod project and the idea is actually to scan six participants really intensively in both the MRI scanner and the MEG scanner and see if it can use human brain data to get closer to something called generalized intelligence. What we have in the AI world is systems that are mathematically and computationally built. Often they do one task really, really well but they struggle with other tasks. A really good example of this is video games. Artificial neural nets can often outperform humans in video games but they don't really play in a natural way artificial neural net playing Mario Brothers. The way that it beats a system is by actually kind of gliding its way through as quickly as possible and it doesn't collect the pennies for example. And if you play Mario Brothers as a child you know that collecting those coins is part of your game. And so the idea is to get artificial neural nets to behave more like humans so that we have this transfer of knowledge. It's just something that humans do really, really well and very naturally. It doesn't take 50,000 examples for a child to know the difference between a dog and a hot dog. One you eat, one you play with but an artificial neural net can often take massive computational power and many examples before it understands that. Video games are awesome because when you do video games you're doing a vision task constantly. You're also doing a lot of planning and strategic thinking but you're also taking decisions several times a second and we record that. We try to see can we from brain activity predict what people were doing. We can break almost 90% accuracy with this type of architecture. Yu Zhang is the lead postdoc on this collaboration with Dell and Intel. She's trying to work on a model called Graph Convolutional Neural Nets. We have been involved with like two computing systems to compare like how the performance was going. The lab relies on both servers that we have internally here so we have a GPU server. But what we really rely on is Compute Canada and Compute Canada was just not powerful enough to be able to run the models that we was trying to run. So it would take her days, weeks, it would crash, she would have to wait in line. Dell was visiting and I was invited into the meeting very kindly. And they told us that they had started working with the new type of hardware to train artificial neural nets. Dell's using traditional CPUs pairing it with a new type of memory developed by Intel which they'll obtain. They're also their new CPU architectures that really optimize to do deep learning. So all of that sounded great because we had this problem, we ran out of memory. The innovation lab, having access to experts to help answer questions immediately, that's not something to negate. We were able to train the Alex Mat within 20 minutes. But before, if we do the same thing on the GPU, we need to wait almost for three hours to finish one Apple. We were able to train the traditional computational neural net. Dell has been really great because anytime we need more memory, we send an email, Dell says, yeah, sure, no problem, we're all extended. How much memory do you need? It's been really simple from our end. And I think it's really great to be at the edge of science and technology. We're not just doing the same old, we're pushing the boundaries. Like often we don't know where we're going to be in six months. Let's face it, in the big data world, computing power makes a big difference. The second example I'd like to cover is the one that I will call the Data Accelerator. That's a partnership that we have with the University of Cambridge in England. There we partnered with Intel and Cambridge and we built up at the time the number one IO 500 storage solution. And it's pretty amazing because it was built on standard building blocks, PowerEdge servers, Intel Xeon processors, some NVMe drives from our partners at Intel. And what we did is we built up this system with a very, very smart and elaborate software code that gives an ultra fast performance for our customers that are looking for a front-end fast scratch to their HPC storage solutions. We're also very mindful that this innovation is great for others to leverage. So the software code will soon be available on GitHub. And as I said, this was number one on the IO 500 where it was initially released. Within Cambridge, we've always had a focus on opening up our technologies to UK industry where we can encourage UK companies to take advantage of advanced research computing technologies. And we have many customers in the fields of automotive, oil and gas, life sciences that find our systems to really help them accelerate their product development process. My name is Paul Collegia. I'm the director of research computing at Cambridge University. Yeah, we are a research computing cloud provider, but the emphasis is on the consulting and the processes around how to exploit that technology rather than the bare results. Our value is in how we help businesses use advanced computing resources rather than the provision of those resources. We see increasingly more and more data being produced across a wide range of verticals of life sciences, astronomy, manufacturing. So the data accelerator was created as a component within our data-centric compute environment. Data processing is becoming more and more central element within research computing. We're getting very large data sets. The traditional spinning disk file systems can't keep up and we find applications being slowed down due to lack of data. So the data accelerator was born to take advantage of new solid state storage devices and try to work out how we can have a staging mechanism of keeping your data on spinning disk when it's not required, pre-staging it on fast NVME storage devices so that can feed the applications at the rate required for maximum performance. So we have the highest AI capability available anywhere in the UK where we match AI compute performance with very high storage performance because for AI, high performance storage is a key element to get the performance up. Currently, the data accelerator is the fastest HPC storage system in the world and we are able to obtain 500 gigabytes of the second read write with IOPS up in the 20 million range. We provide advanced computing technologies that allow some of the brightest minds in the world really push scientific and medical research. We enable some of the greatest academics in the world to make tomorrow's discoveries. All right, welcome back, Jeff Rick here and we are excited for this next segment. We're joined by Jeremy Rader. He is the GM Digital Transformation and Scale Solutions for Intel Corporation. Jeremy, great to see you. Hey, thanks for having me. I love the flowers in the backyard. I thought maybe you ran over to the Japanese garden or the Rose Garden, right? Two very beautiful places to visit in Portland. Yeah, you only get them for a couple weeks here so we hit the timing just right. Excellent. All right, so let's jump into it really and this conversation really is all about making AI real. And you guys are working with Dell and you're working with not only Dell, right? There's the hardware and the software and a lot of these smaller solution providers. So what is some of the key attributes that needs to make AI real for your customers out there? Yeah, so you know, it's a complex space. So when you can bring kind of the best of the Intel portfolio, which is expanding a lot, you know, it's not just CPU anymore. You're getting into memory technologies, network technologies and kind of a little less known is how many resources we have focused on the software side of things. Optimizing frameworks and optimizing kind of these key ingredients and libraries that you can stitch into that portfolio to really get more performance and value out of your machine learning and deep learning space.  Ultimately, easing the complexity of what it takes to deploy an AI capability. So a lot going on there bringing kind of the three-legged stool of the software vendor, hardware vendor, Dell into the mix and you get a really strong outcome. Right. So before we get to the solutions piece, let's stick a little bit into the Intel world. And I don't know if a lot of people are aware that obviously you guys make CPUs and you've been making great CPUs forever, but there's a whole lot more stuff that you've added, you know, kind of around the core CPU, if you will, in terms of access to libraries and ways to really optimize the Xeon processors to operate in an AI world. I wonder if you can, you know, kind of take us a little bit below the surface on how that works. What are some of the examples of things you can do to get more from your Intel processors for AI specific applications, the workloads? Yeah, well, you know, there's a ton of software optimization that goes into this, you know, that having the great CPU is definitely step one, but ultimately, you know, you want to get down into the libraries like TensorFlow. We have data analytics acceleration libraries. You know, that really allows you to get kind of, again, under the covers a little bit and look at how do we get the most out of the kinds of capabilities that are ultimately used in machine learning, in deep learning capabilities and then bring that forward and try and enable that with our software vendors so that they can take advantage of those acceleration components and ultimately, you know, move from, you know, less training time or it could be a cost factor, right? Those are the kind of capabilities we want to expose to software vendors through these kinds of partnerships. Okay, and that's terrific. And I do think that's a big part of the story that a lot of people are probably not as aware of that there are a ton of these optimization opportunities that you guys have been leveraging for a while. So shifting gears a little bit, right? AI and machine learning is all about the data. And in doing a little research for this, I found actually you on stage talking about some company that had like 350, I wrote down 315 petabytes of data, 140,000 sources of those data. And I think probably a not great quote of six months access time to get that data and actually work with it. And the company you're referencing was Intel. So you guys know a lot about data, managing data, everything from your manufacturing and obviously supporting a global organization for IT and a lot of complexity and secrets and good stuff. So, you know, what have you guys leveraged as Intel and the way you work with data and getting a good data pipeline that's enabling you to kind of put that into these other solutions that you're providing to the customers? Well, it's absolutely a journey and it doesn't happen overnight and that's what we've seen it at Intel and we see it with many of our customers that are on the same journey that we've been on. And so, this idea of building that pipeline, it really starts with what are the kind of problems that you're trying to solve? What are the big issues that are holding you back as a company or where you see that competitive advantage that you're trying to get to? And then ultimately, how do you build the structure to enable the right kind of pipeline of that data? Because that's what machine learning and deep learning is, is that data journey. So, you know, really a lot of focus around, you know, how we can understand those business challenges, bring forward those kinds of capabilities all the way through to where we structure our entire company around those assets. And then ultimately, some of the partnerships that we're going to be talking about with these companies that are out there to help us really squeeze the most out of that data as quickly as possible because otherwise, it goes stale real fast, sits on the shelf, and you're not getting that value out of it. So, yeah, we've been on the journey. It's a long journey, but ultimately, we can take a lot of those kind of learnings and we can apply them to our silicon technology, the software optimizations that we're doing and ultimately how we talk to our enterprise customers about how they can solve or overcome some of the same challenges that we did. Well, let's talk about some of those challenges specifically because, you know, I think part of the challenges that kind of knocked big data, if you will, and Hadoop, if you will, kind of off the rails a little bit was there's a whole lot that goes into it besides just doing the analysis. There's a lot of data prep, there's data collection, data organization, a whole bunch of things that have to happen before you can actually start to do the sexy stuff of AI. So, you know, what are some of those challenges? How are you helping people get over kind of these baby steps before they can really get into the deep end of the pool? Yeah, well, you know, one is you have to have the resources.  If you can acquire those resources, can you keep them interested in the kind of work that you're doing? So that's a big challenge and actually we'll talk about how that fits into some of the partnerships that we've been establishing in the ecosystem. It's also, you get stuck in this POC dooloo, right? You finally get those resources and they start to get access to that data that we talked about. They start to play out some scenarios, they theorize a little bit. Maybe they show you some really interesting value, but it never seems to make its way into a full production mode. And I think that is a challenge that has faced so many enterprises that are stuck in that loop. And so that's where we look at who's out there in the ecosystem that can help more readily move through that whole process of the evaluation, the prove the ROI, the POC, and ultimately move that thing, that capability into production mode as quickly as possible. That, you know, that to me is one of those fundamental aspects of if you're stuck in the POC, nothing's happening from this. This is not helping your company. We want to move things more quickly through that. Right, right. And let's just talk about some of these companies that you guys are working with that you've got some reference architectures, DataRobot, GridDynamics, H2O, just down the road in Iguazo. A lot of the companies we've worked with here is theCUBE. And I think, you know, another part that's interesting, and again, we can learn from kind of old days of big data, is kind of generalized AI versus solution-specific AI. And I think where there's a real opportunity is not AI for AI's sake, but really it's got to be applied to a specific solution, a specific problem so that you have better chatbots, better customer service experience, better something. So when you were working with these folks and trying to design solutions, what were some of the opportunities that you saw to work with some of these folks to now have an applied AI application slash solution versus just kind of AI for AI's sake? Yeah, I mean, that could be anything from fraud detection and financial services, or even taking a step back and looking more horizontally, like back to that data challenge, if you're stuck at the, hey, I've built a fantastic data lake, but I haven't been able to pull anything back out of it. Who are some of the companies that are out there that can help overcome some of those big data challenges and ultimately get you to where, you know, you don't have a data scientist spending 60% of their time on data acquisition, pre-processing, that's not where we want them, right? We want them on building out that next theory. We want them on looking at the next business challenge. We want them on selecting the right models, but ultimately they have to do that as quickly as possible so that they can move that capability forward into the next phase. So really it's about that connection of looking at those problems or challenges in the full pipeline and these companies like DataRobot and H2O, Aguazio, they're all addressing specific challenges in the end to end. That's why they've kind of bubbled up as ones that we want to continue to collaborate with because they can help enterprises overcome those issues more fast, you know, more readily. Great. Well, Jeremy, thanks for taking a few minutes and giving us the intel side of the story. It's a great company.  I worked there many, many moons ago. That's a story for another time, but really appreciate it and- I'll interview you. We'll go there. All right, so super, thanks a lot. So he's Jeremy, I'm Jeff Frick. So now it's time to go ahead and jump into the crowd chat. It's at crowdchat.net slash make AI real. We'll see you in the chat and thanks for watching.


It is now time for members' statements. The member for Windsor West. Thank you, Speaker. In Windsor, one in four children live below the poverty line. Our downtown mission now serves an average of 726 hot meals per day, and 1,100 working poor people are forced to use their food bank every month. I know that Windsor is not the only municipality in Ontario that is struggling with these issues. And yet, just three months into their mandate, Premier Ford and his Conservative government have consistently attacked those most in need. One of their first moves was to slash social assistance rate increases by 50%. They're going to eliminate the minimum wage increase, and they cancelled the basic income pilot without a shred of evidence to support this decision. In fact, the evidence we do have shows that these programs are crucial to lifting people out of poverty. In a letter to Minister McLeod regarding the pilot project, the Windsor-Essex County Board of Health stated, �We strongly urge your government to reconsider this decision. Our position is based on extensive evidence that income instability reduces opportunities for health, social and economic prosperity. This Conservative government's priorities are completely backward. Even Ron Dunn, Executive Director of the Downtown Mission, said to me, and I quote, �Our Premier should be embarrassed that Bucca Beer was a priority, while our most vulnerable citizens are not cared for.� I agree with Ron, Speaker, and I will do everything in my power to ensure that this government is continually reminded that they have an obligation to actually help every person in this province, especially those living in poverty. Member statements? The Member for Whitby. Thank you, Speaker, and I am pleased to rise today to pay tribute to my friend and former colleague on Durham Regional Council, Joe Drum. Joe is retiring from public service after 41 distinguished years serving his constituents. Speaker has been a truly amazing career, and it is difficult to imagine Whitby without his leadership on so many local issues. In total, Joe has been elected 14 times, worked under six different mayors, and sat on countless committees at both the town and regional levels. Speaker, Joe has been a dedicated representative, winning over residents with his unique ability to balance a hard-nosed approach with a light-hearted Irish charm. Whitby Mayor Don Mitchell, in reflecting on Joe's career, said, �He has earned the full trust and affection of our residents, staff, and Council call leaks through his integrity, work ethic, and humor.� And so, Speaker, to Joe Drum, I say thank you. Thank you, Joe, for your dedication and hard work, my friend. You are and remain an inspiration to so many in the town of Whitby. God bless, Joe Drum. Member statements? The Member for Ottawa Centre. Thank you, Speaker. Tomorrow, high school students are organizing a coordinated provincial walkout to protest this government's decision to roll back modern sexual health education. They also oppose this government's decision to stop the TRC curriculum writing sessions for public elementary and secondary schools. As the MPP for Ottawa Centre, I have two messages for these students. First, thank you for supporting acceptance and inclusion in our schools. Second, if you require a supporting letter for your absence from class, please contact our constituency office and we will note your walkout participation is an act of fulfilling volunteer hours with your MPP. We love you to continue volunteering with us. We think democracy isn't something that happens once every four years. Democracy is about ensuring public officials are held to account all the time and ensuring people in my profession make the right choices. My friends in government may claim your walkout is unruly and contemptuous of parents. As a parent of two young children myself, I disagree. What's contemptuous is denying our kids an education that's queer and transaffirming. What's contemptuous is failing to honour our treaty obligations with Indigenous peoples. What's contemptuous is not teaching young men at a young age the value of consent culture and about healthy relationships. The students walking out tomorrow are teaching us a lesson. From the bottom of my heart, I thank them for their leadership. Thank you very much. Member Statements. Member for Richmond Hill. Mr. Speaker, I agree with Minister of Community Safety and Correctional Services that there is no place for racism in our great province. We need to take this a step further and we also do a lot of things to support this. We are doing this to respect each other's culture. We see that when we participate in different events in our community and also hosting different events among different cultural groups, we are really supporting this, especially in the riding of Richmond Hill, we have 57.4% are immigrants. My riding also cover a part of Markham, which is actually Canada's most diverse community where more than 42.3% are visible minorities. I am here to have ... I would like to do a lot of things to support this and make this a strong community. We see that when we respect each other's culture and attend each other event, we will have just recently celebrated the Rosh Hashanah and attended the fun fairs hosted by the Muslim community. Last week, I attended the barbecue at the Islamic Community Center, having a fun time with them while answering the question on Queen's Park. Actually next ... Thank you. Thank you very much. Thank you very much. Member Statements. Member for Brampton East. Mr. Speaker, there is a fable that when the sun was setting for the first time, light was decreasing and the signs of darkness were appearing. Darkness set its foot on the earth, but it is said, far away in some hut, one little lamp lifted its head. It proclaimed, I challenge the darkness. If nothing else, then at least around myself, I will not let it settle. Around myself, I will establish light. Watching that one lamp in other huts, other lamps arose, and the world was amazed that these lamps stopped darkness from expanding so that people could see. Mr. Speaker, these are the words of the late activist, just once in Kaldra. He challenged the darkness by uncovering the murder and disappearances of approximately 20,000 Sikhs throughout Punjab by the Indian state. He came to Canada to bring light to this injustice. On September 6, 1995, upon his return to India, he himself was picked up, disappeared and murdered by members of the state. His legacy lives on, and the spirit of just once in Kaldra and SAF, a human rights organisation, has released their database which documents the systemic, widespread and targeted killing of thousands of Sikhs conducted by the Indian state. In the month of September, Mr. Speaker, let us not only remember this great soul, but also let us commit to being that lamp that fearlessly challenges darkness and injustice in all of its forms. Thank you, Mr. Speaker. Member for Scarborough-Gildwood. Speaker, I seek unanimous consent to make a member statement. Scarborough-Gildwood is seeking unanimous consent of the House to make a member statement at this time. Agreed? Agreed. Member for Scarborough-Gildwood. I heard some noes. Member statements? Point of order. Point of order. The member for Timmons. I think what the member was trying to say was to switch her private members in the place of Mr. Gravel is what she was trying to do. You can ask again. I'm seeking unanimous consent to switch places with my colleague, Michael Gravel, to make a member statement. Agreed? Agreed. Member for Scarborough-Gildwood. Member for Scarborough-Gildwood. Thank you, Speaker, and thank you for the opportunity to speak on behalf of my constituents. On September the 10th, at the very start of the school year, I visited an amazing program in my riding, Systema Toronto. To meet with students and to find out how they are doing in this program, MP John McKay also joined this visit. We had a chance to talk with all the students and to find out how they are doing and how this program was benefiting them. One student said that it was helping her to focus more and to have discipline. And all of the students are actually doing better in school as a result of this program. But Speaker, I am concerned that the current government has actually pulled plan funding for Systema Toronto and so that this program will not benefit more students in need. Systema currently serves 250 students and their families in neighbourhoods with some of Toronto's highest child poverty rates, Parkdale, Jane and Finch, and East Scarborough in my community. Systema students develop crucial social skills like empathy, problem solving, and communications while they are learning music together. Mr Speaker, a mother said about her daughter who has been in the program for two years that formerly she was shy and really this program, it's like a cocoon becoming a butterfly. She said that instead of kids just staying at home, watching TV, that they are improving themselves through this program and I believe that it should be supported by this government. Thank you very much. Member statements? Point of order? Members come for the training and I don't want them to miss any part of the training. I just want to quickly introduce them. I've got Lily, I've got Lily Ahn, I've got Alexandra Scappini, I've got Tarun Saroya, I've got Maxine and Swell S. Braden joining us today. Member statements? Member for Mississauga Streetswell. Speaker, today I would like to talk about a young man, Trooper Mark Dia, who chose to serve our great country in the Canadian Armed Forces. Mark always made it a point of coming back to Mississauga during his leave to take part in youth camps, becoming a role model and a mentor. Sadly, Trooper Mark Dia died on March the 8th, 2009 after his vehicle hit an improvised explosive device in the Kandahar province of Afghanistan. Mr Speaker, he was only 22 years old and about to come home. In his honour this past Saturday, I attended the Streetswell Overseas Legion for breakfast to join the Canadian Armed Veterans Motorcycle Unit, who came from across the province to kick off their annual Trooper Mark Dia memorial ride. During my conversations with Trooper Mark Dia's family and fellow vets, I advised them of the all-party unanimous passing of private member's bill ballot item number 12 to erect a monument on the grounds of the Legislative Assembly of Ontario in honour of the brave men and women who fought during the war in Afghanistan. Needless to say, Mr Speaker, the announcement was very well received by our vets, who felt they were being recognised for the sacrifices made. Welcome to our gallery and also to Mark's cousin, Ghassan Krish. Unable to join us today, but watching live on TV, are Mark's parents, Hani Jihan and their entire family. I would like to take this opportunity to thank all members of the Legislature for passing of this bill and to stand as a testament of sacrifices made by our troops, lest we forget. Member's statements, the member for Kiwet Nong. Thank you. To pick up the conversation from this morning, like the one I talked about regarding the Bearskin Lake youth suicide, I think it's very important to know that all the MPPs that you know this issue is a marker of a bigger issue and that relates to intergenerational trauma. You know, intergenerational trauma has been passed down from generation to generation for our people because of the things that happened in residential schools. And one of the things that sometimes for me, you know, cancelling the indigenous curriculum programme, this government has decided to teach, not to teach young students about residential schools, and not even to have a conversation about what reconciliation is, is an issue. Resolving the problem of First Nation youth suicide and indigenous youth suicide were required whereby all Ontarians need to know and understand the root cause of this issue, the problem issue of suicide. Without this next generation of children learning about the history of First Nations people, indigenous peoples in this great province of Ontario, how can the people of my writing expect people to care? Without the caring, you know, how can we ever expect things to get well changed? Thank you. Member's statements, the member for Don Valley North. Thank you, Mr. Speaker. On Saturday, September 8th, a sunny and cool afternoon, I hosted our first annual community barbecue in my writing of Don Valley North at the Coma Park. Over 2,000 residents joined us for free food and drink and enjoyed various multicultural performances. In addition to my constituents, friends and volunteers, my fellow member for Markham Univer from Scarborough Agincourt and Scarborough Ruth Park joined us as well. Also Markham Univer MP Bob Soria, Markham Regional Councilor Joe Lee and Richmond here, City Councilor Goldman Chin have attended. All three levels of government were represented. We have local Armenian, Chinese and Korean community groups entertain. The audience with amazing cultural and musical arts. It was a great success. I look forward to hosting our second community barbecue next year. Thank you, Mr. Speaker. Thank you. The next member's statement, Mr. Markham Stovall. Thank you. Thank you, Mr. Speaker. It's a pleasure to rise and give a statement today. I want to just take a brief moment to talk about the Markham Fair, if I can, Mr. Speaker. As you may or may not know, Mr. Speaker, back in 1844, the Markham East York Agricultural Society got together to start a local fair. The Markham Fair has continued since that time, Mr. Speaker. Over 700 volunteers annually take part in making sure that we have a great four-day fair, Mr. Speaker. I know that you would probably appreciate, all members of the House would probably appreciate the many different contests that they have. Of course, they judge the best heifer, the best bull. There's a lot of pie eating contests, all the things that you would find at a fair across Ontario, but it's more special for our community, Mr. Speaker, given the fact that our farming community, our agricultural community was under such pressure for so many years, Mr. Speaker. In particular, its greatest threat came with the last Liberal government, Mr. Speaker, when the creation of the Rouge Park was underway. The previous Liberal government, of course, had wiped out farms in my riding and planted trees across those farms. Now, well, nobody can suggest that tree planting isn't important for our community, but farming is also important to our community, Mr. Speaker. And I'm very proud that this tradition is continuing and that this government and the federal government that I was a member of before stood up for farmers, continued to stand up for farmers, and that the great farming tradition and the important business that is agriculture in my community will continue for a long time, despite the threats that it had from previous governments in this place, Mr. Speaker. So I encourage all members to get out, come to the Markham Fair after you've had a chance to visit Ford Fest, get out and visit the Markham Fair because you'll have a great time. Thank you, Mr. Speaker. Point of order, the member for Nickelbelt. I would wish to correct the record from my colleague from Kitchener Center. This morning she said the Ministry of Economic Development, she met to say a committee of the Ministry of Economic Development. Thank you, Speaker. Thank you. Yeah, I think that standing order allows a member to correct their own record, but we appreciate the information on the lesson.


This is a paper on a new simple technique to bootstrap Barris lattice zero-knowledge proofs to QROM secure NIC case. My name is Xiu and I'm from ICE. So this is our result. We provide a simple semi-generic method to construct QROM secure lattice-based zero-knowledge proofs. The new technical tool that we develop is an extractable linear homomorphic commitment, which I might just call extractable in HC. What I mean by this semi-generic is that it doesn't work for any sigma protocol or public-cointeractive protocol, but it only works for this limited class that has a linear response. But this is very natural and it's satisfied by a lot of lattice-based public-cointeractive protocols. So starting from this, we can apply our linear extractable linear HC and then obtain a QROM secure NIC case, which is also an online extractable proof of knowledge. Let me first provide the background and motivation. So in this talk, we'll be considering non-interactive knowledge, NIC case. In NIC case, there's gonna be a statement X that's in this language L and any prover that holds a valid witness W for the statement X shall be able to construct a proof pi. And this proof pi will convince to the verifier that this prover knew this witness W. There's gonna be two security properties that we require. So one property is zero knowledge and this states that for a cheating verifier, given this proof pi, this proof pi leaks no information about this witness W. And the only thing that the cheating verifier gets to know is that the statement X was actually in the language or not. We also want to consider a secure notion for cheating provers and we'll be talking about proof of knowledge in this work, which is strictly stronger than soundness. So what this tells us is that there's going to be an extractor such that for any cheating prover that outputs a valid proof pi, it's able to extract a valid witness W that satisfies this type of relation. And this implicitly implies soundness because if this proof pi can be used to extract a witness W, then it means that the statement X was actually in this language. In this work, we'll be considering NICKs in the random oracle model. And in the ROM, all the users in the system, the prover or the verifier, they're gonna have access to the random oracle. And NICKs in the random oracle model is heuristic some extent because it can only be secure, it can only prove insecure in the random oracle model. But this is typically fine in practice because this results in the most efficient and practical schemes. So there's two types of ROM that we can think of. So if we have a quantum adversary, then this quantum adversary can evaluate real-world hash functions over qubits in the real world. So we want this cure ROM to model this capability as well. So while in the classical ROM, it only made classical inputs and received classical outputs. In the quantum ROM, we'll allow this adversary to query inputs in superposition and receive outputs in superposition as well. And this creates a lot of difficulties in the proof of cure ROMs. So there are a lot of proof techniques in the classical ROM that we take for granted, which seems hard to import to the cure ROM setting. The first two are observing the adversary's input query and also knowing what this corresponding output was with respect to this input query. And why is this difficult? So the reason is that since these are in superposition, if we measure it, it will collapse to a single state. And if these input and output queries were entangled with the quantum state of this adversary, then measurement might disturb the adversary's state. So the adversary might actually notice that it was being measured in the cure ROM. And this doesn't show up in the classical setting because in the classical setting, we just write proof saying that the adversary outputs and outputs X to the random oracle, we sample random Y. So we know these X values and Y values, definitely. But in the quantum setting, this is a bit difficult. Also, another issue is adaptively programming the random oracle. And this is because the quantum adversary may query all the inputs in superposition to the random oracle. So at that point, it seems as though the random oracle is gonna be defined on all outputs, meaning that there's not gonna be any notion of adaptively programming the random oracle while the game transcends. And these three seemingly very difficult problem. Nowadays, well, the past two or three years, there's been a lot of work regarding this. So we now know many ways to overcome these seemingly difficult problems. However, they do not come for free as in the classical ROM setting and it requires a lot of complications. And sometimes there are still really difficult things that we have to overcome. So that was the issue about whether we're talking about classical or quantum ROM, but there's actually difficulty just in using quantum adversaries, regardless of this ROM being classical or quantum. And one representative example is rewinding type of arguments. This is a type of proof where we fix a random R for this adversary and we run the reduction while the reduction algorithm programs the random oracle up to this point, well, it proves the random oracle. And what we do is that we will rewind the adversary to some point, let's say, to this query X prime. And we will reprogram the random oracle on this X prime. And from that point on, we run the adversary again on the same random as R. So we rewind the adversary and we rerun it. And the issue of this in the quantum setting is that there's no notion of fixed randomness. So this general technique might not really work in the quantum setting. And now let me explain a bit more about lattice space, lattice space, Q-ROM, N-I-Z-Ks and how we construct them. All the N-I-Z-Ks that we'll be mentioning this work will start from a Sigma protocol or in more general, a public coin interactive proof system. If we focus on Sigma protocols, it's very standard to define two notions on its verifier zero knowledge and special soundness. Where a special soundness is very interesting because it states that given two valid transcripts with the same A first flow, there's an efficient way to extract a witness W. Let me explain two famous ways to convert a Sigma protocol into N-I-Z-K. The first one is the Fiat-Schmidt transform. And in the classical setting, this is excellent because it works for any type of Sigma protocol and the proof overhead is minimal. One relatively small downside is that the proof of knowledge requires rewinding. So it does incur a reduction loss. There's also the UNDER transform, which was originally created for this quantum setting, which I'll explain later, but it works for the classical setting as well. And it works again for any Sigma protocol, but we have to restrict the challenge set to be small. And due to this, we require a lot of power repetitions and we also have to include a lot of garbage terms to make the UNDER transform work, where this C is the size of this challenge space. So there's gonna be a lot of blow up here. The good thing is that the proof of knowledge is straight-line extractable. So it results in a tighter proof. It doesn't require rewinding. But still the general rule of thumb, I guess in the classical setting, is that we wanna work with a Fiat-Schmidt transform because it's just really good. And UNDER's transform, the overhead is quite big when the challenge set, the original Sigma protocol can have a challenge set that's as large as exponential. However, if it's capable of using small challenge set to begin with, then sometimes UNDER transform is competitive too. The situation in the quantum setting is a bit different. So the Fiat-Schmidt transform no longer works for any Sigma protocol, but it only works for a class called collapsing Sigma protocols. Also the proof of knowledge, it works through rewinding, but there's gonna be a lot of small subtleties that we have to take care of and technical issues that we have to take care of. And it will lose a bit more in the reduction law, reduction compared to classical setting. Compared to this, the UNDER transform, it's much better because it's basically the same argument that we wouldn't make in the classical setting. So it's just, it's very similar and easy. So the obvious downside now is that not all existing Sigma protocol are known to be collapsing, so we have to show that it's collapsing. And this is not, this is sometimes very tricky, I guess. So the good thing about UNDER is that if we can show in the classical setting, then it also will work fine in the quantum setting too. So it's very easy to analyze. For general two N plus one round PCIPs, the situation for the Fiat-Schmidt transform the same, but the UNDER transform, it's a bit different. It doesn't work for any two N plus one, but it only works for five round protocols with a specific type of challenge sets. And here, this might be able to be expanded to multi rounds, but we don't know of this yet. Let me talk a bit more about this QROM securification transform. So this was shown by these two groups in 2019. And they start with the collapsing Sigma protocol. And they first argue using a rewinding argument that this can be transformed into a Sigma protocol with a quantum proof of knowledge, where basically this is a Sigma protocol with a proof of knowledge where the adversary is also a quantum adversary. And in the second step, they argued using a new reprogram technique to show that the Fiat-Schmidt transform applies and it constructs a QROM securing ICK. So this is the two steps that they took. And the thing is, it's really not clear if all these existing schemes are collapsing. So this part is the difficult part now. And also this reprogramming step, most of the times it will incur reduction loss, a bit more than a classical setting. So let me explain a bit about the recent lattice-based PSIPs in the classical ROM setting. So I only talk about the Fiat-Schmidt with abort types of Sigma protocol, so I won't be talking about the Stern type protocol in this slide. So the most basic one is the original Liboshevsky 09012 paper, which provided a relaxed proof for SIS and LWB relations. And interestingly, the two papers show that this is a collapsing Sigma protocol as well, with a slight increase in the parameters. So we know how to apply the Fiat-Schmidt transform securely in the QROM setting. For these very basic Sigma protocols. For the more recent ones, like opening to commitments, range proofs, one of many proofs, we don't know if these are collapsing Sigma protocols. So we don't know how to apply the Fiat-Schmidt transform, but we do know how to apply the UNDER transform because it's still a Sigma protocol. The caveat is that these schemes can use exponentially large challenge set, but we have to restrict them to be polynomial size or even smaller than that, to make UNDER transform work. So there's gonna be a large blow up here, compared to the classical one. And finally, there are these new five round schemes or even larger than five round schemes, where we don't know how to apply the Fiat-Schmidt or UNDER transform at all. I wouldn't know that this modified UNDER transform for the five round protocol might work, but there are a lot of details that we have to check to really know for sure if it works or not. So this is the current situation. And this brings us to our main question of this talk, which is, can we get the best of the Fiat-Schmidt and UNDER transform and even a bit more? So the Fiat-Schmidt transform, it requires no overhead and it works for exponentially large challenge set size. UNDER, it works for any Sigma protocol and it has a tight reduction, which is a straight line extractable proof of knowledge. However, there are these schemes that are not covered by Fiat-Schmidt or UNDER. So it will be interesting to see if there are other transforms that covers protocols that lie outside of these two schemes. And this brings us to our result. So our result is a new transform that provides a partial answer to the previous question. It's a semi-generic approach that sits somewhere between Fiat-Schmidt and UNDER. So these are the properties. And the first one is that it works for many lattice-based PCIPs or in general, any PCIP with a linear response where this notion will become clear in the later slides. The overhead of our transform is larger than the Fiat-Schmidt transform while it's much smaller than the UNDER transform when the challenge set size is exponentially large, which is the case for many of these lattice-based PCIP protocols. The reduction loss is smaller than Fiat-Schmidt since it's a straight-line extractable proof, like UNDER. And our security proof is very simple and it's almost very classical. So it requires minimal knowledge on quantum computation. And finally, it works for PCIPs where Fiat-Schmidt and UNDER are not yet known to work. The new technical tool that we develop in this work is called extractable linear-homophic commitment. And with using this extractable in HC, we can start from any sigma protocol with a linear response. And this is a very natural sigma protocol and it's satisfied by many sigma protocols. Here, if we start from this primitive, we can combine with an extractable linear HC and bypass this rewinding argument to directly get a sigma protocol with quantum proof of knowledge. And then we can use prior reprogram techniques to get a QROM secure NIC came. Or, this is a simplified extractable linear HC. But if we start with a more structured extractable linear HC, which is not that different, we can directly get to this QROM secure NIC came without even having this reprogram technique. So this will provide us the most tightest and simplest transfer if we want NIC came. If we only want a quantum proof of knowledge, then we can just use this transfer. We will explain our idea in a bottom-up approach. So we'll start from this very base example, which is a sigma protocol for the SIS or LWB relation by Louis Boshatsky. So this will be a scheme that's here in the classical ROM.  The proof we will sample a short vector R from some distribution, let's say a Gaussian distribution and create this vector W and sends it to the verifier. The verifier samples a short element from this RQ and sends it to the prover. The prover sets Z as C times E plus R and it will do this rejection sampling step, which is not really important for this talk to maintain the shortness of Z without revealing the witness E. It will then send this Z and the verifier will just check that Z is short and this equation holds. So basically this is Schnorr's protocol, but using lattice language. The main question here is that to eventually show proof of knowledge of the Fiat-Schimmer N-I-Z-K, we want to extract a witness from a single transcript. But in order to invoke this special soundness of this sigma protocol, we need two transcripts. And the question is how to obtain these two valid transcripts without rewinding. So this is the first step. We add a linear homomorphic commitment. So when the prover computes this W, first flow W, we will also commit to the witness E and to the randomness R. And we'll send this commitment, come E and come R to the verifier. The verifier will sample challenge C and sends it to the prover as before. And now the prover will set this Z, which is, when you look at it here, it's a linearly homomorphic operation over this E and R, the committed messages. And it will also do the same for this committed randomness now. So it will create a delta Z randomness from the delta E and delta R. And now the prover will send this delta Z to the verifier. What does the verifier do? So it does the normal checks and it also checks that this commitment, come E and come R was constructed correctly. Using this Z, the Z and the opening delta Z. So if this equation holds, then the verifier will accept. And this is implicitly checking that this commitment, two commitments were created honestly. And this is still standard Sigma protocol because if this commitment is, let's say, hiding, then we have honest verifier, zero knowledge and special soundness. We just can ignore these purple elements and we just invoke it using the base Sigma protocol. So right now it doesn't provide us anything new. So we want to, as a second step, add extractability. So assume this commitment scheme was public keyed. And in the original public key, it's just gonna be a random bit string. But in the proof, we'll swap this into a new public key star with a trapdoor town. And this trapdoor town will allow us to extract from any honestly generated commitment. So given a commitment of this form, then extract com will extract this element X. So now what we wanna show using this added extractability is that only given this first valid transcript, we want to extract a witness E. An incorrect naive argument will just be to say, well, run this extractor on this commitment E, which is given to the verifier in the first round here. But this is obviously wrong because there is no guarantee that this commitment E, com E, is valid. The verifier or the simulator or the reduction algorithm only knows that this commitment, this part is correct. But since the commitment, we already know that this opens to Z and delta Z. It seems like there's no place to use this trapdoor right now. So this third step, we will now show how to argue extraction correctly. So the simple observation is that this commitment E and commitment R is prepared before the challenge C. So for simplicity, we'll assume that this challenge set is small right now, even though eventually we want this to be exponentially large. For now, we'll just think that this is small. And we'll further assume that there's, we are guaranteed that there is another valid transcript that V will accept eventually, the verifier will accept it. Then with these two assumptions, what this extractor will do, the Sigma protocol extractor, what it will do is that it will just run through all the challenge and create commitment for all this challenge I and extract from this commitment. So by assumption, since this verifier will accept on this challenge C prime, it means that for C prime, this commitment Z created for the C prime, it will be valid. Therefore, this is guaranteed to be a valid commitment. So the trapdoor will extract this Z prime as promised. So if we're given this first flow, I mean first transcript, then we can run this extractor, which will run through all the challenge set. Then it will find Z prime at some point in polynomial steps and we'll be able to extract these two valid transcripts. The remaining question is how to make this argument work for exponentially large challenge set. And here, the point was that we needed this to be polynomial or large to run through all this space. And an easy modification is that we're just gonna make this probabilistic. So we're gonna have an upper bound large N and we're just gonna keep on running this until it succeeds. So we'll sample a random challenge and just keep on running this extraction. And the question is, does this work and how do we set this large N? So this is a simple statistical argument. So let's assume an adversary has non-negligible advantage epsilon right now in completing the sigma protocol. Then using a very standard counting argument, there has to be at least 2D times epsilon challenges for which A would have been able to answer. Therefore, we can set this large N to be roughly lambda, both lambda secure parameter over epsilon. And after sampling this many random challenges, then with overwhelming probability, it will hit another valid commitment. And this analysis works regardless of the adversary being quantum or not because it works the moment the adversary is able to produce a valid transcript, then we can invoke this reduction, regardless of A being quantum or classical. So this is how we make it work. So summary so far is that using this simple extractable in AC, then we can get a sigma protocol with a quantum proof of knowledge. Extending this to the QRAM secure NIZK setting is very easy, which is covered by this blue line. We don't want to have time to explain it in detail in this talk, but it does follow very naturally from previous results. Some details worth mentioning is that in our instrument transformer, we require a slightly stronger flavor extractable in AC because the sigma protocol is only competition on this very far zero knowledge. Also the analysis extends easily to multi-round. And since the commitment key was a random binary string, we can just use the random oracle to output this public key in the real protocol rather than relying on a CRS, a common reference stream. Also, when we look carefully, our NIZK is actually dual mode because depending on the public key we use, it will be statistically sound or statistically zero knowledge.  So in fact, the dual reg of PK is already what we want, an extractable in AC. So the commitment key in the real world will be a random binary string. So this will be structured into a matrix A and B. So this will correspond to a dual reg of PKE public key that has no secret keys. And to commit, we're just going to encrypt using this random public key. And here the randomness here for this extractable in AC will just be the randomness for this encryption scheme here. And linear homomorphism follows naturally because dual reg of PKE is linearly homomorphic. So you can just get the C times E plus R term here. And in the extraction mode, we'll just set this public key into an actual dual reg of public key with a secret key. And using that secret key, we can always decrypt this and obtain this C times E plus R term back. So this is a very basic way of constructing an extractable in AC. And in the paper, we also provide a second method that uses an entry like public encryption scheme to get better efficiency. As a concrete application, we benchmark using this BLS-19 protocol, which provides an exact sound 5-round PSIP protocol. First of all, the finish may transform. It's not obvious whether it applies or not because we don't know if this is a collapsing Sigma protocol or a 5-round protocol. And also the under-transform doesn't work because it's not a Sigma protocol, but this modified under-transform might apply. So we will benchmark using this modified under-transform assuming that it works. So the normal classical ROM N-I-Z-K, it's gonna be 812 kilobytes, the proof size. If we apply the modified under-transform, it's gonna result in a 45 megabyte proof, which is roughly more than 100 times larger than the classical one. And if we apply our extractable LIN-HC protocol, it's only gonna be at most 2.6 larger, which is 2,071 kilobytes. So compared to under-transform, it does provide a smaller proof size. So this was our talk, and this is a summary and open problem. Thank you for listening.


This is dock number 11487Z. We are here at Summit Automotive in Fond du Lac, Wisconsin near new and used car and Sports car headquarters today. We are checking out this super clean 2013 Chevy Camaro ZL1 This vehicle has the 6.2 liter supercharged V8 motor which pumps out 580 horsepower Blue ray metallic is the color we shoot all of our videos in 1080p 60 frames per second So if you have HD capabilities on your computer tablet or smartphone device turn them on right now because it is definitely your best way To check out the quality and condition of the car before seeing it in person This car has been fully safety and inspected by our service shop as a fresh oil and filter change All the fluids have been checked and topped off and this car is 100% Ready to go and if you'd like to see all the photos on this car and the upper right hand party screen is a link to our website Click that and check us out there So the ZL1 package gives you the 20 inch painted alloy rims It has the Brembo brakes and the brakes look really good on there at least the rotors do and Has good year Eagle F1 285 35 ZR 20 tires in the front and you can see that all the tread is on the inside of these tires These tires have probably about 50 60% on the tread left on them Front fenders an excellent condition. I didn't see any denser dings on that. It comes with the HIV headlamps the LED running lights the factory fog lights Front bumper and lower valence are an excellent condition. I didn't see any scuffs or scrapes or dents or dings on that Has the carbon fiber insert on the hood with the air induction there Passengers side front fender is an excellent condition to in the passenger side rim No scuffs or scrapes on that As you go down this side of the car take note of how clean the body is how reflective and mirror like that paint is We take these HD videos So if you are far away or even if you're close by you just can't make the trip down But you're so interested in purchasing the car You can see the car hear the car and have confidence in the vehicle that you're looking at Before you even get here so that when you do get here There are absolutely no surprises and that's why we offer the summit auto Fast pass option in the upper right hand part of your screen a link right to our website where you can apply for financing Get pre-approved see a payment quote even appraise the trade all from the comfort of your own home Back rims in excellent shape as well and see that tread a little bit better on those back tires They have just as much tread as the front tires As we come around to the back of the vehicle you get the rear bumper Sensors parking sensors the rear bumpers in great shape, and you get the chrome tipped exhaust Declan spoiler and deck lid are in excellent condition And we'll check out the trunk in just a second Blu-ray metallic one of my favorite colors on the Camaro's To go down the driver's side just as clean as that passenger side No denser dings on the quarter and for full disclosure this back rim excellent condition as well Down the rest of the side of the car looks really good, and it does come with heated power mirrors As we look inside the vehicle you can see that this one comes with the black Leather and suede interior you get the red stitching you get the ZL1 stitched into the backrest The back seats are in excellent condition as well no rips or tears on them And they do fold down for extra storage, and you do get the latch child safety system If you want to try and get a car seat back there that is up to you auto headlamps tilt telescopic steering wheel power windows power locks power mirrors Boston sound system, and I always like to point out this dash and door insert trim It's actually got a little bit of metal flake to it, which is Really cool. We'll take a look in the trunk quick since we're up here Back storage area is very clean and Your RPO codes are actually right here So feel free to pause that if you want to check those out make sure it has all the options you're looking for Shuts nice and solidly the paint is really nice on this car Everything's really nice on this car. We'll take a look at the instrument cluster here This one only has 9,856 miles on it You do get the digital speedometer the compass display instrument cluster is very nice and clean you get the flat bottom suede wrapped steering wheel cruise controls on the left of Bluetooth and in from audio controls on the right and You get the paddle shifters since this is an automatic transmission this one has the Touchscreen radio with the factory navigation system. It does have AM FM and Sirius XM radio capabilities You do have heated seat buttons right there And you get an oil PSI oil temp volts and boost gauges on the center console here tour sport mode and stability control automatic transmission Passenger side Format and seat are in excellent shape as well. No rips or tears on there This car has never been smoked in smells very clean inside and the headliner is in very nice clean condition Map lights up there power sunroof buttons as well as home link buttons for your garage door security systems and lighting systems And you get on-star capabilities in that review mirror We will start it up and take a quick look under the hood Other than the hood being open no information center Warnings or check engine lights this one does have the heads up display as well We'll take a quick look under the hood I would personally like to thank you for checking out the video today and hopefully from this HD video You'll have been able to tell just how clean this car is all the way around inside and out Under the hood we have the 6.2 liter Supercharged V8 motor 580 horsepower Engine bay is very clean runs very smooth Once again this car has been fully safe and inspected by our service shop as a fresh oil and filter change All the fluids have been checked and topped off and this car is 100% ready to go I'm just gonna go to the back so you can hear the exhaust real quick Sounds really good I would highly recommend this car from a quality and condition standpoint and to see more pictures of this car One of our other 450 new and used cars trucks SUVs Sports cars Camaro's Corvettes Mustangs GT 500 Challengers Chargers you name it. We got it go to our website right there www.SummitAuto.com full pictures and descriptions of every single vehicle from two locations all at Summit Auto.com And if you'd like to check out more HD videos you can go to youtube.com slash Summit Auto Remember to like subscribe and share on this video and all the videos that you see there In fact in a second you will see a link to subscribe to our YouTube channel on your left A link to more Chevy Camaro videos like this one on right if you have not been to our website on the bottom link to this vehicle on our Website click those check us out and we really look forward to help you with this extremely clean 2013 Chevy Camaro ZL1 and blu-ray metallic. Thanks again for checking out the video


What's going on everyone Sam here at late in sports car driven 22 upper-deck skybox skybox metal universe champions hobby one box break number one random pack style There's a look again at your random results as always you can find those on the websites under the results tab as well as the link in chat So let me organize these really quick Alright, so we'll open it what packs one through five and on the left in the middle six through ten and eleven through fifteen on the right It's kind of how we've always done these skyboxes Box is facing me. Let's make sure they all look good. Hopefully it's not messed up True Just a bunch of tires looking at you Geez one through five right there. That's one two three four five It's one through five six through ten and eleven through fifteen So start with number one that is will see with one and two here and of course the upper deck Possible open. Yeah, Max Christie Yeti cappy a I Brent Brandon Maya at Francisco turn chow a Wayne Gretzky premium skybox and a Malik Willis fresh foundations This one there Pack number two again is gonna be will see it's good luck here will Robert Schwartzman Schwartzman Michael Foster Kennedy Chandler Bruno Kapeloza, that's a Riley Green Z-Force. Those are cool Marcella Meyer on the metal shredders and Fresh foundation of Caleb Williams I will not too bad for you there. Just a no just a no on number three here Christian Hernandez nice see her and Roger Arias. That is a Matt Corral got Garrett Wilson cool one there Collin more a kawa that is a Brady house To 150 nice one there believe that's a PMG But nice one on the yellow and Malik Willis metal shredders for Dave M looks like the Christine just go for a little bit. Yeah, it does. It looks just like that actually now you say that Amen Thompson that is Malik Willis. We've got Ray Cooper a Nolan Hickman. Oh, that's sick. That is gonna be Adrian Griffin. That's the junior. Yeah, that's like the specimen right there That's really nice. I think that's AJ right there. Yeah, that's AJ. That's the Hawks guy Fresh foundation Johan Dotson and Kenneth Walker. No, but I think those are the like super short front the stand-up specimens Yeah, I think they're they're the 100 oh, wow good stop there Dave congrats Dave. I'm assuming that's the Michael Nice, they're super short friends right there. Yeah, that's buddy Onto number five Michael L Alex Alcala Bryce young Caleb Williams Oswald Paraza Marcello Meyer universe Fresh foundations Kenny Pickett and a Henry Davis metal shredders All right going on to our second stack number six is gonna be Paul C Here we go Paul No more tops chrome tonight. No, unfortunately, Annie that is Maria Sharapova Olga Carlin, that's fencing. That's cool. Talia Tagovailoa. That is a Caterina Macario Anthony Pettis that is a 2500 degrees Joao Felix. Those are sick. Love that design and metal shredders Alex Alcala Pack number seven will Z. Good luck will Walker Kessler Jose Perdomo Chandler Smith John Pack That is a cut above Jacob Gonzalez on the short print nice one there insert fresh foundations Alex Alcala and scoot Anderson metal shredders. I scoot All right number eight Brian B. Look Brian here we go, buddy Zalatoris Ken Griffey a nice one there has gonna be a dandy day in Gerade Ricardo Pepe Azalea cut to her Liga Christian Hernandez nice one there and a Walker Kessler Congrats Brian on the will Z again at number nine left here will Caleb Houston Nicola Jovic Traylon Burks the Desmond Ritter Colin Moorakawa Z-Force nice one there That's gonna be an AI metal shredders and fresh foundations Marcella Meyer Number 10 just to know rounding out the staff number two or column number two I suppose is the way to call that Is that Kedin slovis or Kedon slovis Marjanbo champ Steven bourge wine Jamari boy, there's Malik Willis a Jackson holiday metal shredders and Jake Wiggins Alice Top screwing baseball every day there you go Mel 11 Michael L. Good luck here Michael Johan Dotson and Antonio Carlos Jr. Kenneth Walker nice one there. Amen Thompson on the Z-Force That is a fresh foundations. Isaiah Spiller and Christian Hernandez nice see her without the Without the logos of the Kenneth Walker. It looks like he's on the chest Sean picking up a spot in each of the team halls. Thank you Sean Matt L at number 12.  There you go Matt Dave Amit 13 now. Good luck here Dave the portrait one. I think yeah Kendall Brown Roush Manfio, there's a breeze hall Troy Merritt Looking into the future Marcella Meyer die-cut Got a Peyton Watson and Kenneth Walker. All right. We've got Michael L at 14 here now. Good luck here Michael Joelle Felix Wayne Gretzky Virgil Ortiz Jr. Danny Way nice one there. I love it Riley Greene a Drew Jones intimidation nation very cool Of course the Diamondback prospect and Brian Akumya All right, Will Z rounding us out at 15. Good luck. Will See what we've got man Armando Cruz a shade on a sharp Willie T. Ribs Kayla Harrison Jamal Swanson Henry Davis and Michael Foster on the metal shredders That is gonna do it for the break guys. Thanks again for the fill up next is number two