Hello, everyone. My name is Jiang Zhang. The title of this talk is taking the asymmetry of large-spaced public key encryption and signatures. This is a joint work with Yu Yu, Su Qin Fan, Zheng Feng Zhang, and Kang Yang. Okay. This talk has four parts. In the first part, we will give a short introduction. We will show how to take the asymmetry in the design of large-spaced public key encryption and signatures in the second and the third parts. Finally, we will end this talk with a short conclusion. Let's move to the first part. Since the introduction of public key encryption in 1976, the public key cryptography has been widely deployed in real applications. But most of them are based on the hardness of solving integer factorization and discrete logarithm problems. So far, this is good for algorithm on classical computers. However, because of short quantum algorithm, the basis of those great classical schemes is shaken. In fact, if large quantum computer is available, those schemes based on the traditional number theoretical problems will become insecure. Actually, as commented in a report on quantum threat timeline, quantum threat could very well become concrete in next 10 or more years. Facing this threat, the world has started many projects on constructing post-quantum cryptosystems that can resist against quantum attacks. One of the main research direction in this area is large-spaced cryptography. For example, large-spaced schemes are about 41 percent and 46 percent of all submissions in the first and second round of least PQC competition, respectively. The last decade has greatly decreased the great development on large-spaced schemes, but they are still less efficient than their number theoretical counterparts in terms of public key, suffix, or signature size. In particular, for adequate security, large-spaced schemes typically need thousands of bytes, but they later only require at most a few hundred bytes. In fact, reducing the size has become one of the main problems on constructing practical large-spaced schemes. After observing the asymmetry in the design of large-spaced schemes, we propose the asymmetrical MLWE problem and exploit its asymmetry to obtain public key inclusions with shorter public keys and suffixes. Compared to the least-run two submissions at a category 3 security, we achieve the smallest public key and suffix size, both are less than 1,000 bytes. We also propose asymmetric M6 problems for obtaining signature schemes with shorter public keys and signatures. Compared to the least-run two submissions, our scheme has smaller public key and signature size than deletion. Third, we adapt the best-run attacks and their variants to the asymmetrical problems and conduct a thorough analysis on choosing concrete parameters. For example, we consider two variants of the primer attacks and the three variants of the dual attacks to make use of the asymmetry of the underlying problems to reduce the complexity. Let's move to the second part. The learning with errors problem was first introduced by Regev in 2005, which basically asks an algorithm to solve a noisy modular equation. That is B equals A times S plus E, where A is a uniform matrix, S is a uniform vector, and E is chosen from a noisy distribution chi with the parameter eta. The decision of variants asks an algorithm to distinguish the LW tuple from uniform, which is as hard as a computational LW problem. A major variant of the LW problem is the permanent normal form LWE, where the secret S is also chosen from the noisy distribution. The LW problem is proved by as hard as some lattice problems, such as the SIWP and SOVP problems in the worst case. Many lattice-based public key implications are based on the Lindor-Pakerless scheme at the CTRSA in 2011. The public key and the cell flex basically consists of several LWP tuples. Concretely, the public key is generated by randomly choosing a matrix A, vectors S0, E0, from the noisy distribution. Set A, B equals to A times S0 plus E0 as the public key, and S0 as the secret key. Given all bitmills, the sender randomly chooses vectors S1, E1, and E2 from the noisy distribution. Set C0 as the transpose of A times S1 plus E1 and C1 as the inner product of B and S1 plus E2 plus an encoding of mu that is mu times q over 2. The receiver compiles u equal to C1 minus the inner product of S0 and C0. One can easily prove the security of this scheme by applying the LW assumption plus the correctness of this scheme requires the decryption noise E prime which is determined by S0, S1, E0, E1, and E2 is smaller than q over 4. Clearly, the decryption noise is mainly dominated by the inner product of E0 and S1 and S0 and E1. When it comes to choosing concrete parameters, we have two conflict constraints on the parameter eta. On the first hand, the correctness is determined by the function g alpha n which is the size of the decryption noise and is expected to be as small as possible. This needs to set a smaller eta. On the other hand, the security is related to the function f alpha n which is the size of the decrypt vectors s and u and is expected to be as large as possible. This needs to be to a larger eta ideally for targeted security level, kappa. We hope to pick an eta for simulating laterally achieving two to the matters kappa decryption error and two to the kappa security. But typically, there does not exist such an optimal eta. If we set the parameter for achieving a given security, there usually exists much room for the decryption noise. In other words, the size of the decryption noise might be far smaller than q over 4. Then we can use the compression function to obtain shorter public key which basically cut off the lower bits of the public key and suffix. This operation can reduce the size of public key and suffix but it will result in a much larger decryption noise. This is okay as long as the final decryption noise is still less than q over 4. Unlike the case without compression after compressing the public key and suffix, we find that the lower of s and u in the decryption noise is asymmetrical. In particular if the x part that is introduced by the compression, the public key and suffix is much larger than the e part. Then the size of u is less significant than z of s in the decryption noise, e prime. In other words, the size of u will significantly reduce the decryption noise but reducing or increasing the size of e will not change the decryption noise too much. This feature inspires us to reduce the size of s as much as possible for smaller decryption noises that increase the size of e for maintaining the security. Then we can further make use of this as metric to obtain new skins using different parameters eta1 and eta2 for the secret s and e as well as different modules p0, p1 and p2 for the compression of public key and suffix. By doing this we can flexibly choose the parameters to achieve short part public key and suffix while at the same time not significantly affect the security. Specifically we can set smaller eta1, p0, p1 and p2 for short public key and suffix but larger eta2 for maintaining the security. Now the concrete security is based on as metric variant of the standard LWE where the secret key and the noise are chosen from distributions with different parameters eta1 and eta2 obviously when eta1 is equal to eta2 this is exactly the standard LWE problem where contact of other radices and best lower attacks the experiment shows that the security remains unchanged if we keep the product of eta1 and eta2 unchanged. For example for the standard LWE with eta1 equal to eta2 equal to 2 this product of eta1 and eta2 is 4. For most of the same security we can also set eta1 to be 1 and eta2 to be 4 such that the product of eta1 and eta2 is still 4. This basically gives us many choices of eta1 and eta2 for making better trade-off between equations and the security. By instantiating the above scheme with as metric variant of modular LWE we obtain shorter public key and suffix for example for the target 108 bit security both the public key and suffix are less if than 1,000 bytes and are smaller than this run-to-submission kyber for better trade-off between equations and the security. Now let's move into this third part this small integer solution problem was first considered by Atai in 1996. This problem ask as an algorithm give you a matrix A or vector T and a real gamma to output a vector X such that A times X equals to T and the norm of X is at most gamma when T equals to 0 this is a standard 6th problem otherwise it is an inhomodulous 6th problem it is choosing uniformly at random. Both 6th and I6th problems are probably as hard as some noticed problems such as SIWP problem in the worst case. Now we consider the identification scheme from 6th by Leibovsky at the Asia Club in 2009. Let SATA be a set with absolute value less larger than ATA the secret key is chosen from SATA and the public key T equals to 8 times X. To convert the verifier that has the secret key X the prover first randomly choose a vector Y from SATA sends W equals to 8 times Y to the verifier. The verifier randomly choose a challenge for simplicity. Here we only consider C to be a bit. One can easily extend the protocol to bit strength of any fixed lens. Finally the prover returns Z equals to Y plus C equals to X. To protect the secret key X we cannot direct the output Z instead we only output Z if the infinity sum of Z is not bigger than gamma minus beta where beta is the maximum size of Z times X in this case for any choices of X and Z we have a vector Y such that Z equals to Y plus Z times X. This means that X is perfectly hidden in Z. By applying the fade-shammy transform we obtain a signature from X by hushing the vector W to obtain a challenge Z. In order to choose conflict parameters we have conflict constraints on the parameters M, bet, gamma. For example for security we expect the gamma to be small but for the computational efficiency we need gamma to be big such that the expected number of repetition is small. In practice we cannot obtain optimal choices of parameters for all goals. In private we have signature scheme for the security of Z's problem we need a very large M which gives a very large signature size. To reduce the signature size we can switch this underlying hard problem from Z to LWE by setting the public key as A times S plus E where S and E are chosen along S eta. In fact the LWE problem can be seen as a special size problem where the matrix is a uniform matrix followed by an identity matrix. In this view this change is intuitive and direct. After changing to the LWE problem a use feature is that the vector W equals to A times Y1 plus Y2 which is mainly dominated by A times Y1 as Y2 is smaller. In particular the higher bits of W are most equal to the higher bits of A times Y1. This also holds for the vector view. At CTRSA 2014 by Anagabris found that one can utilize this feature to further reduce the size of signatures by removing the part related to Y2. For this goal we need to add one more rejection on the lower bits of W minus one C times E such that it is not bigger not too bigger and will not affect the higher bits of W. Now the expected number of repetition is dominated by two size. Specifically we can reduce it by either increasing gamma 1 or gamma 2. Notice that the signature size is also dominated by gamma 1 but it is irrelevant to gamma 2. This isometric feature allows us to use a smaller gamma 1 for smaller signature size but a larger gamma 2 for maintaining the expected number of repetitions to fully utilize this feature. We also switch the standard LW problem to its asymmetric version such that the secret S and E are chosen from the distributions with different parameters, eta 1 and eta 2. This allows us to use different beta 1 and beta 2 for rejection. Beta 1 is the maximum size of C times X and beta 2 is the maximum size of C times E. For security, in addition to the asymmetrical LW E, we also did the asymmetrical variant of SACE which basically splits the solution into two parts and put different constraints gamma 1 and gamma 2 on the two parts. By doing this, the expected number of repetition basically has two independent parts. This finally allows us to choose one for short signature and appropriately chooses other parameters to maintain the other features. By instantiating the above scheme with the asymmetrical variant of modular LW E and modular SACE we obtain a scheme with shorter public key and signatures. In particular, for most of the same computational efficiency our scheme has shorter key and signature than the least run-to-submission deletion. This finishes our third part. Here is a short conclusion. We obtain public key encryption with shorter public key and suffix by taking the design of public key encryption using asymmetrical MLW E. We also obtain signature schemes with shorter public key and signatures by taking the design of signatures using asymmetrical MLW E and M6. We conduct subtle analysis on choosing concrete parameters by adapting the best lower attacks to both problems. This is the end of this talk. Thanks for your attention.


Bugs 200 and 400 these batteries are so much better than that than that one It's not even like that. It's not even like shown a battery shortage It's not even showing like that the batteries been used. I think I'm gonna make just a tiny bit of gold And put it on here You think that'll be a good idea This hexagon I'm just gonna put a little bit of gold in the clear and just kind of give it some dimension There's zero gold there's another jar somewhere Wow


So welcome. Good morning, good afternoon or evening depending on where you are joining us. Thank you, Anna. So today I'm really, really happy and pleased to be here with Pantia Lee and Pantia is working with us in the movement strategy research and she's going to tell us a lot more. Pantia is the principle of reboot, which is a design research firm that has been partnering with us for now over two years, I think, over a year. Wow. We've done a lot of work in a year. Yes, as you're really productive together. And I also want to mention that Zach is here with her. He's also principal at reboot. And we hope to be learning a lot from them. And before Pantia, you start talking and just wanted to make sure that you all feel free to ask questions and interact and interact with us. As you're hearing the many interesting, fascinating things that she's going to share with us today. Okay. So, to start, tell us a bit more about reboot. So thank you for coming. Hello. Yes, great. So, is a design firm that works with mission driven organizations to design policies and services and products that help them better meet the needs of their users and of their constituents. So that means we work from anyone from the UN to the city of New York to grassroots activists collectives. And, you know, we were we were founded on the belief that people should have a greater say in the policies and the products that impact their lives. And we do that by bringing in a lot of the practices that the private sector uses to design things people actually want to need. And we try and translate that to the public sector and to the social sector, where there's not always the same incentives or the same accountability and mechanisms. And so what that means in practice is we've done everything from helping the government of Libya post revolution design the world's first mobile voter registration and elections management system to working with New York City on criminal justice reform. We've been working specifically on bail reform and helping immigrants better understand their rights. Yes, fascinating, fascinating. And how the work with the Wikimedia Foundation, right, like how, how has been working with the Wikimedia Foundation. Yes, so I think our work together started about just over a year ago. A lot of reboots work is global. And so I think it was at a point where the foundation was thinking about investing more deeply into understanding the really diverse communities that you guys serve all over the world. And so it really started with this new readers project where we're helping you all design and conduct design research in Nigeria and India to try and inform strategies from communications to product and partnerships, whatnot. And I know that's led to some really interesting things that, you know, Jack and Zach are doing around communications around sort of offline support that and leading whatnot. So that's where it started. And then we did some work around mapping your various audiences, developing an audience framework and thinking about how to prioritize engagement and investment in research on different audiences, which led to some work around conducting design research with with with editors actually with editors. Great. And so we've been working with the editing team in in midsize wikis in South Korea in the Czech Republic to understand the editor experience and where all you are where you guys are gaining people and losing people and learning from lateral communities as well. And then sort of coming full circle, we've been obviously doing work around movement strategy track D now called new voices research in Brazil and Indonesia just to understand how are people getting information? What are the trends at the foundation and the movement need to be aware of looking out to the next 15 years? How do people get online? How do they come to seek and trust information online? What do they know about Wikipedia and do they care? So some of those questions that we've been batting around to inform the strategy process. And why design research? Like how design research compares to a more analytics data driven approach? You know, design research is at the heart of a lot of what reboot does, because we believe that fundamentally understanding human experiences, human behaviors, mental models is critical to designing things that people actually want and need and will use. And so what that means is, you know, instead of just cold hard logic, looking at sort of market analytics, which are important, we also use empathy as a primary processing tool. And it's been really cool to have foundation staff with us on all of these research projects to understand, okay, once we talk to all these different users and potential users of Wikipedia, can we walk in their shoes? Can we understand what they care about, how they feel about things, how they get online, what the challenges that they have are? Because if we can understand that and put ourselves in their shoes, that will make us better strategists, that will make us better designers. And I think it's actually, we do a lot of generative design research, which means we don't necessarily know what the solution or answer will be at the very start of the process. Compared to some of the more, I think, evaluative work that the foundation currently does, which is also very important. I know the product teams have been doing a lot around sort of, you know, testing different sort of products and approaches and iterating upon those. But I think some of the more generative work here has been really valuable for this sort of strategy process where we want to think bigger picture. So yeah. And you were thinking, we were just talking about the movement strategy and how, right, like how ambitious this big process is. And I think that everyone is really interested and excited to hear what are the findings so far from Brazil and Indonesia. We have a 25 page memo that you're all welcome to read. You know, I think we have a lot of findings that we're still processing and want to work with you all and the movement strategy team over the coming few weeks. But I think one of the biggest things that is coming out from the research is that I think looking forward, Wikipedia and the Wikimedia movement is going to have to think about how Wikipedia is not just a destination. For knowledge and for information, but how it can become a source of knowledge and a source of information in all the different and diverse ways that people learn.  What we're seeing now and what we're finding through the research is people are learning and getting information in all these really diverse and fascinating ways. From people learning to, you know, cook and groom their eyebrows on YouTube. To, you know, all the different homework help communities, the brainly comms, the other things, you know, and young people especially are just finding really creative and new ways to learn to share information to get information. And they're not really going to websites anymore. And so, you know, if we play that out for Wikipedia, I think it's interesting to think about how the site and how the platform and all the knowledge that you guys have, how to make it more modular, how to make it more portable to be able to take the resource that you all have built and feed it into all the diverse and different channels and ways that people are learning. And I think that's going to be something quite interesting because, you know, the first 15 years was, you know, I think for you all really about building this incredible resource and really thinking about how this production model works. The next 15 years might be really thinking about, OK, so then once we have this information, how do we actually get it out to people? What is the distribution model? And I think that's going to be really interesting to, you know, look at how Wikipedia innovates next. And some of, like, what would be some of the key insights when you're thinking about, like, Brazil and Indonesia specifically, right? Are there things that is to doubt for you? Something that was really fascinating for us was looking at the rise of messaging apps and just how prevalent and popular they are now. I think WhatsApp is installed in something like two-thirds of smartphones in Indonesia and it grew 300% between 2015 and 2016. And there's a lot of factors driving that, one of them being that a lot of telcos, mobile network operators are offering these apps for free. They're zero-rating them or they're including them in sort of data bundles and packages, whatnot. And so these are really, you know, whether it's social networking apps, messaging apps, they're really becoming people's on-ramp to the internet. And for some people they are the only ways that they are getting online and they're not even thinking about using WhatsApp as being online or using the internet. And where cost is a factor or where it's just like fun to chat with and share links with your friends, what does that mean for Wikipedia? And I think what's interesting as well is people are also using these messaging apps now, not just to chat and to share links. They're also forming these what we were calling sort of hyper-targeted social networks. Like people don't want to use Facebook anymore. They're saying, gosh, Facebook is for old people.  So Facebook is for old people. What we want to do now is we want to form our networks for when we go to an event afterwards we have our community that we form a WhatsApp group around that. I have a study group for every single one of my classes at university. I want to form these hyper-targeted social networks and then my free messaging app that Telco is supporting is enabling me to do that. So what does that mean for Wikipedia? How do you guys appear and show up and help people in these study groups and appear organically in context? Is that in app previews of Wikipedia content? Is it some other ways to sort of help people share information where and how they're doing so? And I think that that was really fascinating to us. And then I know you guys know this, but there's obviously a lot of brand confusion that we surfaced. And I know it's been, you know, talked about, you know, and I know the comms team is working on this, but Wikipedia has great brand recognition, but a lot of brand confusion. And so what that means is people think of you all as, you know, this is a technology giant. We know the name, but, you know, we will compare it to a Google or a Facebook. That's kind of crappy for you because you get judged then to be a pretty poor search engine or a really confusing social network. And so, you know, and so how do we help people really understand what Wikipedia is, how it works, and why they should care? And those are some of the things that are coming out. I'm happy to talk about any of the other findings that surfaced. Yeah, and I'm also interested in talking about the youth, right? Like you mentioned that. And what are some of the key interesting things that we're seeing particularly with that audience? And if there are things there that we should be particularly paying attention to as we think about our next 15 years as a movement. Cool. That's a big question. What are the kids doing? So I think one thing that is really interesting about the youth that we were looking at is trust in content. Whether or not they trust content doesn't really matter. Trust doesn't equal usage. Trust doesn't equal utility. You're seeing a generation that is highly skeptical of media, of information sources, and particularly of online content. And there's a lot of factors that give rise to that in Brazil and Indonesia. Specifically, you have long history of government control of the media. You have highly concentrated media ownership in both markets. You have the proliferation of fake news and people now use fake news as a term to describe seemingly everything. And you have business models that incentivize clickbait and sensational content. So there's lots of reasons that we can talk about. But ultimately what we're seeing is young people don't trust the content that they have. Or they know content is biased, but they will use it anyway. They will then take that content and discuss it with their friends to try and triangulate between lots of different sources to say, okay, should I use this for what purposes? I know it's biased. And I think that's quite interesting because Wikipedia and Wikimedia spend a lot of time thinking about trust and accuracy. And that's great. But also, how do we think about relevance and utility in what's actually going to get people to use this content? And one of the things that we're seeing too around trust is that the indicators of trust are changing. Young people are not trusting institutions to give them credible, verifiable content. They're trusting each other. And so they are looking for indicators such as number of followers, number of likes on articles, other sort of social and more individual indicators, you know, the reputation of a content curator to help them determine what content they should trust and use. And I think there's really interesting implications there then for Wikipedia because your process and your content is driven by individuals. And so, you know, is there a way of showcasing that and surfacing that to help people understand how the sausage is made because they want to know that to then understand, you know, whether or not they should invest in and use a piece of content? Yeah. And then maybe the final point on young people is that, you know, I think we hear visual, we hear real time, we hear social, and we know all that. But those aren't just buzz words. I mean, kids, young people are getting their news on Instagram. You know, instead of going to the website of a newspaper, they are following the Instagram account of the newspaper because we're sitting there and they're scrolling through, like, this is the exact amount of content that I want on a significant news story. Less than 100 words, that's it. You know, big photo, great. Scroll through. They're getting breaking news by following trending topics on Twitter. They're then taking that to WhatsApp to discuss with their friends. So what does that mean for Wikipedia? You know, do we need to think about using, allowing video as references, for example. Do we think about, I don't know, push alerts around articles that are getting sort of rapid, distributed and concentrated edits? So people, you know, so Wikipedia can be seen as relevant in real time in the way that, you know, they want their news and information. I think those are interesting questions to explore and wrestle with. Yeah, and I'm wondering here now that you just mentioned some of the possible opportunities and things that we could be exploring and considering. And how we take all that, right? We touch base on, you touch base on some of the bigger key findings, how youth is relating to content information, how they're accessing it. So making sense of that and connecting that with the five thematic directions of the strategy, right? Like how are you seeing those? How are you seeing what you learn and saw from Indonesia and Brazil relating to the teams? So we were really excited to see the five movement strategy themes. They are ambitious. They are visionary. They are comprehensive. And we were then also thinking about how to map our findings against those. And I think what's interesting is we tried to separate out between objectives and strategies and then tactics, because I think the five themes are slightly different in that way. So I'm going to get the letters confused. Okay, you have them. But the ones around being a respected and relevant source of knowledge, that is an objective. And we can do that through advancing with technology. We can do that by engaging the knowledge ecosystem. Those are tactics. Becoming a truly global movement, that's an objective. And again, we can do that through technology, through engaging diverse partners. And so as we were mapping some of the opportunities that we saw, and I know we have a workshop with some of the movement strategy folks after this, we tried to essentially map our opportunities into a matrix of sorts. I know folks can't see this to understand how we use technology to meet each of these objectives. Obviously the work that we were doing was really focused on readers, on communities, on audiences. But we know that just strong, healthy communities will be critical and foundational to driving all of this forward. But our work under this wasn't really focused on track A. And I think what's really then interesting about this is this notion of being more modular, more portable, engaging with diverse partners to push content out. I think that's really going to be at the intersection of all of these themes. How do you work with educational institutions to think about using Wikipedia content in after-school programs that extend learning outside of the classroom? How do you all work with nonprofits that are investing in skills training for unemployed youth to take Wikipedia content, mix and match it, to develop a curricula? I think those will require both a mix of technology and partnerships. And I think those will effectively help Wikipedia be relevant and global and sustainable into the future. Thank you for showing and talking about this map and how you are interpreting that based on the data. And I think another thing that I would love to hear from you is that how do you think we are serving the emerging markets and emerging communities? What are the things that we could be doing differently there? And what are the ways of actually identifying their needs and then serving those needs? So I think in terms of emerging markets, I think one thing that we've all been talking about is emerging markets, global sound, it's a very broad term. And you all have been investing in research in a lot of specific markets. So I think one key step moving forward is to try and disentangle some of this and to break these markets apart. So if you take a look at Indonesia and Nigeria, for example, where you guys have invested in primary research, these are quite different markets. Let's take a look at mobile penetration and cost of data, for example. Nigeria may be representative of markets in sub-Saharan Africa. You know, data is relatively expensive and so people ration it, it's kind of a scarce resource. And so the strategies that you guys develop there will be applicable to certain regions. But Indonesia, where cost of data is dropping quite rapidly, even though there's still sort of barriers to access, that might be a much more sort of illustrative view of where different markets are going in terms of internet usage, information behaviors, what not. So I think first step might just be to sort of disentangle emerging markets and think about what each market can tell you about larger patterns in which regions. And then I think from there, we see that Wikipedia has, you know, we talked about sort of brand recognition, what not. But I think one group or one set of users at the foundation I know and the movement is thinking about, you know, whether and how we can better serve them is, whoa, we might term sort of more marginalized communities, lower income users, what not. I know that's been a topic of conversation here and I think that is an area where there is still a significant gap through no fault of the movement.  And so I think an interesting question in these markets and with these users in particular is what does the movement want to serve them? And if you do, who are the partners that you might need to engage to do that? So there are, you know, there are nonprofits, there are government programs, there are other folks that specialize in serving communities and populations such as these that very much need the content that Wikipedia has that could really learn from and draw on the energy and the passion of the community that you all have. These folks, you know, we work with tons of nonprofits that we want to develop educational content in an extra wide way. We have the, we know how to reach low income populations, but we don't necessarily know how to, you know, package and put together things for them or have the resources to invest in developing this content. So I think, you know, that's an area that the movement might continue to explore because we do see that as a gap right now. So I think we already, I don't know, I don't have my phone on me, so I don't know how we're doing on time. How are you folks feeling in terms of asking questions and jumping in? Because I think I have more questions and I can keep asking them, but I just want to do a check and open a little bit more of the floor for us to have more of a conversation with Pantia and with the group. So how are we feeling? Can, like, we have questions? Do we have comments? Yes, we have Sati there. Brendan, just go over there. Oh, there's no mic in there. Okay. Hello. Hello. Okay. So I have many questions too, but I'm going to ask one that's probably not fully filled out. And I guess my question is a lot of what you've said have been talking about how kind of a comparison, right? Like we sitting here, you know, today in San Francisco, understand a world a certain way. We see Wikipedia in a certain way. We do research, we ask people to understand kind of Wikipedia in their own context, right? And then there is this feeling, I guess I get, where we say, well, they have barriers to, let's say, accessing the world in the way we do. And I guess I'm asking, that's a very, that focuses a lot on deficiencies, right? Like barriers, like things that they don't have or ways in which they do different things. And I guess I'm asking, in what way should we actually be learning from them? Should we actually be saying maybe the world is trending in a way where we're actually behind and they're progressing and instead of trying to match them to us, maybe we should be matching us to them. Does that make sense? Yeah, no, absolutely. I think that I don't necessarily mean to say, you know, there are people have deficiencies. I think it's more, there are barriers. So Wikipedia works a certain way right now that then sort of erects barriers to people being able to access and use you all. And so, you know, what are ways that then the movement foundation might be addressing those barriers. So I do think that there are things that the movement can definitely learn from the creativity of, and it's hard to talk about these populations because we're talking about, you know, quite a diverse set of users and markets. But I think, you know, one of the things that has been really interesting is the rise of these social networks. People that have, you know, cost barriers to be able to use the internet are getting around them in quite creative ways.  And so that's actually quite interesting. Instead of using other social networks, they are creating their own. And I think that's a really interesting way for people for you all to explore. I think also there are really interesting, you know, we work with a lot with nonprofits and media organizations and whatnot that are leveraging traditional media in quite interesting ways as well. Thinking about, you know, how do we use television and radio in creative ways to deliver educational content. And I think that could be an area, you know, for you all to explore as well. So, so then I guess then my follow up to that is then you've kind of laid out it feels like two models, one where right now we're kind of this self encased thing like a website like a Facebook like an ecosystem in a sense. And what I'm hearing from you is that we actually need to be more ubiquitous, right that we need to be embedded in some way we need to show up in ways that might not even be branded in a way that's recognizable. But if our true mission is about knowledge, let's say a part of it is about knowledge dissemination, then that knowledge it's really important for that to show up in the ways that people are accessing it. And so how I guess in that vein, and that's a lot about readers, how do you think about content, let's say curation creation, kind of the upstream pieces to dissemination. How does that that end of the flow match into kind of this ubiquity that you're kind of describing at the other end of the flow. So I think that I. Yeah, I think you're absolutely right you know instead of becoming like a product. So I think ourselves as a product and knowledge here you know how do we actually be like an engine that pushes you know information out you know in ways that you know to enable ubiquity to be able to you know push it out to other content creators or folks that are you know providing educational resources whatnot and I think that. I think the role of curators here is really interesting and important. We see a lot of you know young people following bloggers and bloggers and you know that have built up trust in certain ways that may not see Wikipedia content as credible or easy to use or whatnot and so does the role of the community, for example, change here do the role of affiliates change. You know right now I think the community often in the way that I understand it. A lot of it is dominated by editors that are contributing content and you know and doing sort of a lot of different functions but you know in the future is it about developing topic guides to help you know to help nonprofits are thinking about digital literacy. Okay so these are the resources that we have these are you know through API through you know more modular content what not this is how you might be able to mix and match it to design your own curricula in extra wide way. Is it about thinking about community community members as liaisons between you know other content creators whether they're more individuals that you know alternative history bloggers on YouTube to nonprofits it's focusing on you know educating people about you know women's rights. I think those are different ways that you all might be able to think about it, but I think this ubiquity point is is a really interesting one and definitely worth considering. Please. It's a really great question where you ended and I think one other piece of it that's somewhat simple to add on what pantheon said is the more ubiquitous you are and the more people you're reaching the more opportunities you have to build somebody up the ladder of engagements where they might become a contributor and actually be an editor so there is a sort of feedback loop that comes from reaching more people in terms of potentially increasing the pool of contributors. Oh yeah so let's maybe call for them. Let's get some people. Alright so we have a set of questions from blue jeans here. JMO you're first in the list, and then we can go to Edward and mirror. Hello, how's my audio. You sound great. Oh, thank you. So thank you very much for speaking with us. I had a question about methods because I'm a design researcher and so I like methods. My question is around how reboot gets to the point of providing these you know the high priority findings and recommendations. So I believe that a lot of people have an idea of what how design research works in terms of you know you talk to people, you observe what they do, you go to where they live, you find out kind of how they interact with in our case you know Wikipedia or information technologies or technology in general how it fits into their lives. And then you take lots of notes. But I think that it's really interesting and kind of less understood how you get from okay we learned all this stuff about people and about what they do and what they believe and what motivates them. How do we get to the point where we are synthesizing that and prioritizing what are the most important findings. What are the big takeaways and for this audience you know for our clients, Wikipedia in this case. I think I think we did a really great job of that in the new readers project. But I'd love to hear a little more kind of about how that process works within your organization. I love nerding out methods. And so thank you for the question. You know so I think for us. I'll try not to get lost in details. But I think you know where we started with you all is thinking about this research framework this research framework and what were the questions that we wanted to ask. And usually where we start here is you know one one principle we have in mind is always don't necessarily ask about the thing that you're most interested in. Which means we did not start by asking people about Wikipedia. We started by asking people about just information systems like what what information they need what they care about how they get it. And we go from sort of the broadest to how they get information to then sort of narrow how do they get online to then you know how do they come to Wikipedia how do they use Wikipedia. And so we go from sort of broadest to most narrow. Happy to talk about the research framework I know that sort of been shared with different team members. And then as part of the field research. We bring a team of local researchers from the city the community wherever that we're working and then paired with a Wikimedia team as well to make sure that we both have the institutional sort of context and knowledge. And and then also the sort of local and cultural translation as well. For us it's really important to make sure we spend a lot of time with you all to understand your strategies your work processes what not because I think sometimes user centered design is misinterpreted as you know just let's focus on the end user. Whereas in fact you know we actually need to do a lot of work to understand the organizations and the institutions that serve them to be able to design strategies that are actually feasible and implementable rather than you know shiny blue blue sky deck that you can't do anything with. And then I think from there in terms of the actual research we do a lot of sort of semi structured ethnographic interviews we do user observation we do tech demos what not happy to get into any of those. But then we end up doing nightly synthesis sessions with the entire team which means we are making sense of the data collected every single day. What are the patterns what are the connections and where should we go deeper the next day. Because this type of research it's really applied and so we're not asking the same set of questions every single day. If we're asking the same questions we did on day 10 as we are on day one we would have failed. You know as we're thinking about opportunities we're trying to figure out how to hone in deeper on those so that we can start sort of testing some of your hypotheses and so it's quite sort of active and iterative research. And then I would say in terms of how we got to some of these higher priority findings. And then I'll stop because I might be losing people is we we tried to map all the users that we spoke to in each context against a matrix. Along sort of digital confidence and literacy and then also the type of use cases that they are using the Internet for from there we then segmented into a couple of. Subsets of users and then and so you all have user personas now for these sort of different archetypes and then from there we mapped the user journey of each of these types of users to understand to help them to help us understand. In their information journey to try and get the information that they want and need to do extra why depending on the type of user. What are the biggest barriers that they face and what are the most valuable information sources from them for them for the most valuable information sources we then think about what can we learn. From these lateral examples and then for the biggest barriers we then try and sort of aggregate them to understand OK what are the most prominent and significant barriers that are preventing. More users from being able to take advantage of Wikipedia and then that's how we end up rank ordering the opportunities that then we put forward to you guys. That was pretty fast. I'm not sure if that was useful. Excellent answer. Thanks. Nice work. OK so we had I think Edward next is that right. Edward would you like to take over. Sure my apologies I'm in a coffee shop right now so can you all hear me. We can hear you. Thank you. Yeah so thank you so much for talking with us. Yeah this is super interesting work and I've been following the audience's work for a while and I've actually been trying to use it in my own work. So I do a lot of surveys at the Foundation and with communities so I'm also interested in the methods a little bit. So I'm actually curious to hear generally what have been some of the limitations or challenges that you're finding in conducting conducting your research. Especially what you said at the beginning which is around how you try to bring voices into the design process so what have been some challenges around that. And perhaps as if there's been anything in our context that has been challenging for you. Time. Challenges I think that. You know I think one of the biggest challenges for us has been the the. Thinking about the breadth of. Opportunities there and then how to organize them which is why sort of going to John's questions previously had for us having a really structured process. To narrow down to rank order to what to process the opportunities and findings was really important but I think that has been a challenge but I think what's it's been helped by is the fact that. It's been really refreshing and really exciting to work with such a cross functional team we've been working with global reach and partnerships and community and community engagement and coms and product and whatnot. And so that you all having the conversations to then give us a sort of more targeted brief that's been really useful. I think that we've I think there is more that we could do to think about how to use surveys and other sort of quantitative methods in conjunction with the more qualitative methods that we use. Typically when we do this we you know design research is really good at going deep. And you know understanding people's you know behaviors attitudes what not. And I know we and we've been working with Dan and taking a look at some of the trends and findings from the mobile surveys thinking about how that informs our research. I think we could do more perhaps to think about OK so now we've gone really deep how do we go broad again to you know test out the representativeness of some of our findings here against a wider audience. And and then I know that I think one area that has also been somewhat challenging is thinking about is the time factor. You know we've we've been doing this sort of quite rapidly and we've had basically time for about you know two weeks Prince in each of these countries. And so which gets which has been about sort of 70 respondents per country. And so but and so we haven't really had time to be able to prototype any you know sort of solutions or strategies which sometimes given a longer sprint. We are able to do to test out and come back with with user feedback on specific strategies or you know product designs that you might want to pursue. On that note of time I think there was a question also there about the user personas not being posted they're not posted yet but they will be soon. So there's a big back coming with all all the personas from Indonesia and Brazil and more information. So yeah stay tuned for that. And then I believe that the next question is from Amir. Is it right. Hello can you hear me. Yes we can hear you. Oh you might also hear my son in the back. OK so quick question. It was all really really really interesting. Thank you so much for coming and talking about this. I appreciate this a lot. The global perspective is really important for us. My question is about something that you mentioned in the beginning of your talk. You said that the messenger networks like WhatsApp are really important today. How do you think Wikipedia could get there. Because currently we are very much a web organization. People access us through browsers and a few people access us through the Wikipedia app but it's it's almost the same. As reading it in the browser. What could we do with these networks given that they are so important and popular possibly even more popular than Facebook by now. What could we do there. It's a good question. So I think that first of all is just I think really understanding what people are using these networks for. And so you know it wasn't really a focus of our research but it sort of emerged quite quickly as a key trend that we are seeing. So you know I think I mentioned you know whether it was study groups I think that is actually that could be a big opportunity for Wikipedia. In terms of people are talking about homework assignments they're debating they're debating topics learned in class and whatnot sort of on these networks. And you know is there a way to bring in Wikipedia to be a sort of like a context provider when people are having these debates and conversations. I'm not sure the specific sort of you know product strategy that would mean but basically how do you be sort of organic and in context. I think there's been some experiments with WhatsApp chat bots I'm not sure I think I heard something about this. But those could be ways to help people you know again to get information from Wikipedia that is in many markets you know low cost and where people are. I think that there's also there's also you know in many of these markets we found that the ways that so this this now maybe ties a little bit into the editors work that we've been doing. But in some of these markets there are not great representations of like essentially sort of people's like culture in context on the Internet. I think Wikipedia is current model of you know determining sort of what is good content that can be included on the site is present some barriers to cultures that may be sort of more oral or may not have the you know resources and the the the references rather to to to have to be like a you know verified and trustworthy sort of Wikipedia article. Are there ways to to have different versions of Wikipedia perhaps that allow different types of contribution that you might be you know sourcing via these these chat channels that then sort of gets put into larger processes of verification of what not. Do you guys think about it as a way to sort of get content or to source material that then might be put through more rigorous processes I think those are also things that you could explore. But yeah those are some ideas. I mean is that answer your question do you have follow ups or comments. Yeah this is the question. Just just a tiny follow up. You only mentioned WhatsApp but I know that there are several other platforms around the world like Viber or WeChat or Telegram. What are there any others that you suggest looking closely at which which are the important ones around the world or maybe in particular regions. I know some of this is in our in our findings memo. I do know in both markets what's up was dominant. I know line and Telegram are and people use them for various purposes. You know we've heard what's up we might use for more sort of professional and schools reasons but we might use line because we like their emojis better. And and so we we didn't really do a deep dive into comparing the messaging apps but there is good there is good market research on some of this. A note of caution on the market research is oftentimes they really sort of just give you penetration in terms of like downloads what not and that may not be the same thing as usage. And so I think sort of probing into why people are using specific messaging platforms for what and how could be something that you guys might want to think about further. Right. We have a question there. Thank you Amir. And we have Rosie there with a question who have the mic in the back. Thank you. This has been pretty enlightening for me. I have a question about who's being left behind. So for the first 15 years there's been a lot said a lot written about the teenagers and young men in their 20s are the ones who kind of dug in deep and were the ones that spent the most time on the Wikipedia that we we've known of the last 15 years or so. And that there was there were definitely big segments of people who are kind of left out a very small percentage of women versus men and so on. And so I'm wondering does your research touch on that. Do you have a feeling for you've spoken to groups of people in Indonesia and Brazil. But can you sense who has been left out of the conversation in Brazil in Indonesia. And is there a way that in the next 15 years we're not going to replicate this feeling of somebody being left out. Will we be able to be more inclusive based on the things you're kind of learning so that we kind of learn from what we've experienced in these first 15 years. That's a really interesting question. So I think on just your earlier point around it being mostly young men really digging into this. I think that our work on editors which is most which has been in South Korea and Czech Republic that somewhat firms what you're saying here. And we were sort of probing into where you lose people in terms of contributors. What is the editor experience and where do people drop off. And I think we see that there are elements of you know policies norms cultural norms and practices that you know pose barriers to. I think you know we saw sometimes you know women feeling excluded or just you know it was a difficult. It was I think we heard people say you know Wikipedia is almost more more more hassle than it's worth getting into the tussles to be to be able to contribute. And so I think we saw that I'm not sure. And I think now people are then finding and founding other other you know we saw a family wiki in South Korea. This new resource that had been developed by contributors to Wikipedia that just you know didn't want to get into the fight as they saw it. We're seeing sort of older people retirees that we spoke with that had that chose instead to contribute to other platforms. You know we were analyzing how for Sarah the sort of MOOC platform how they recruit and you know on board and then continue sort of encouraging contributors to their platforms and they've been successful. We've seen with you know retirees that actually have a lot to give. And so those are some of the things that we're finding through the editors research in terms of movement strategy. I think we got a sort of big push from Adele and her team really to look at who's been left behind and to really talk with populations that are not currently online. Lower income populations to you know test out some of our hypotheses that you know Wikipedia may not be reaching them and may not be able to on its current trajectory. And I think that's really where some of the ideas around partnering with nonprofits or other social organizations that really serve these populations and who's like you know core value is reaching is doing outreach to them. And then thinking about how you guys bring your unique model to then you know be the engine of their their work in terms of contributing content or you know community members helping them navigate your content. I think that's where some of those ideas came from out of the push to speak with those types of populations. Absolutely yeah and I think the for us right when we were thinking about this and like really taking advantage of design research and really being able to hear from the users that we're trying to serve. We had no limits right it was really like let's go there and really understand and I think we were trying to probe and go further into who we are leaving behind now. So those are really like a lot of people in this country is right and they they are like women not non white populations right like so racially racially ethnicity diverse populations. Age diversity like all the all the things that we have that we feel that we should be paying more attention to and those are part of the new voices that we feel that have not been represented or or included in our conversations right and now in this process. We want them to be sitting at the table and being part of that and and I think the work that we did in Brazil and Indonesia but also with all the other work that we have been doing in all those countries or. Are really our go to that right let's really open the floor and let's really elevate those voices and make sure that we are hearing from them. And we are now at cycle three right in the movement strategy process and I think that is the that is how this information is coming right to the communities. And and we're sharing all that and we are connecting OK so where we want to be in 15 years and can write like can we work and have those new voices as part of this movement right can we build something that it's not that is with them. And I think we are right like I'm excited for this phase because I think we have a lot to exchange a lot to learn from each other right like Sati was talking about how we learn from them and I think there's so many things that we we have to learn and they are out there right like this findings the insights and and really interesting creative ideas are out there but we need to open up the space for the new voices to be in the dialogue. Yeah, one more question and who is it. Jessica Jessica you're there. Yeah, can you hear me. Yes, we can hear you. Perfect. Thank you. Thank you for this great talk to begin with it was really really really interesting. Just a quick questions that I know we don't have a lot of time but. You mentioned something that was really interesting I thought around brand awareness and brand confusion that you know a lot of about a lot of people know about Wikipedia but they might not have a good understanding of who we are and how we work. And I was just interested in getting some high level thoughts and suggestions from you of, you know how that can be addressed. What can we do better as a movement and foundation site in order to educate people about you know who we are how we work and and why they should care as you put it very clearly. So I think that we're seeing a lot of interest from different users around sort of understanding the process behind like how they're, you know how how the sausage gets made. We heard people say, you know, I know Wikipedia, but they seem to be, you know, they're they're they're not very transparent. We don't know where they are we don't know how they work. But you know, Google seems much more transparent because Google posts videos about you know how they work and we like see their offices and their offices are colorful and we're like really that's that's so fascinating. And so, you know, and, and we saw people, we had users that were on the Wikipedia Instagram we were watching them sort of scroll through and they go, I don't get it.   And so, you know, they're trying to understand also like, what is Wikipedia because I can't make sense of all of this content. And so, you know, I think there are things that you all might be able to do to expose the process. Who are the people behind Wikipedia people want to know people want to know how Wikipedia is made who are the people behind them because like we're seeing trust shift from institutions to individuals and so you guys are a movement of individuals let's let's show that because, and there are things that you can do whether in terms of you know communications campaigns but then also I think on articles and on the platform to be able to you know show that process to help people understand how it gets made. And then I think around the actual sort of product is it around disaggregating you know just knowledge, you know one platform and developing sub channels topic guides whatever it is thinking about you know how you communicate whether it's sub channels around your Instagram accounts or other things to help people understand okay Wikipedia is all of these things but I don't have to just engage with Wikipedia has like one singular platform. I, you know, I can, I can find whatever it is that Wikipedia offers that is interesting to me and sort of ignore the other stuff and I think that is that that could be another interesting area to explore. But I know the comms team has been doing different sort of communications campaign in local languages and through through other sort of, you know, locally relevant distribution channels and so they might be better suited to speak to that. So I think that was our last question. Thank you, Jessica. Thank you, Panthea. This was really good. We're really happy to have you here. And yeah, thank you for all attending as well. And that's it for this morning. Yeah, thank you having me. This is a really fun work.