Zero Knowledge: Solution for Digital Identity? w/ Leo Fan
I spoke to Leo Fan, founder of Cysic, about what zero-knowledge proofs actually are, and why they might be one of the more promising answers to the internet's identity problem. We dig into how a ZK proof lets you prove something is true without handing over any identifying personal information, why that matters as digital identity systems become pushed by governments, and Cysic's model for verifiable compute. Leo also breaks down why the hardware and cost problems around ZK have kept it fr...
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Transcript
But either have the deployment we found, like, the the poor generation is, like, too slow. Even we use a, like, very large AWS, instance, it's still, like, very slow, which, like, inspired me to design some specialized hardware for that. For instance like I want to say like transfer probably like $10 online but I need to prove to the chairman or like to some like authority that my bank balance is, like, more than $10. So, like, I can't prove that part but without releasing, say, vector
balance number in my bank account. What are the implications of this technology? Is the ability to selectively disclose specific bits of information about yourself over a digital space, which is, you know, historically really hard to do because you you produce so much data when you're over the Internet. Seems like if they can, like, you don't need to trust anyone. Like, you just trust the math and the math, like, you can do it by yourself.
And we are not creating, like, a honeypots, like, for, for the hackers to hack.
Hi, everyone. You're listening to the Blockchain Socious podcast. I'm Josh, and I am here today actually in potentially my new studio that I'm going to be using, going on into the future. So more news on that that I will share, soon. But before I go into that, I wanted to introduce my guest for today. His name is Leo Fan, and he is the founder of Sysic, which is a super interesting project, building called an on chain compute network. But before I butcher trying to explain it, maybe, Leo, if you want to give a quick introduction to who you are and what CySEC is. Yeah. Sure.
My name is Leo Fan. Like, I, I got my PhD from Cornell, like, in cryptography. After Cornell, like, I did some, research engineering job at Lyft and Agrovant that started as an assistant professor at Rutgers. Yeah, so when I was at Aggravand, like, we we delivered the the first DK Bridge between Aggravand and Ethereum, Agravand and Sonana. But at the time of the deployment, we found, like, the the pool generation is like too slow. Even we use a like very large AWS instance, it's still like very slow. So which like inspired me to do something to like, hop on like design some specialized hardware for that. Yeah. So it's the the word like cipher is a combination of two words. The first is, like, cipher, and the second part, the SIC, is, like, coming from, like, ASIC. So it's a combination of, like, building some specialized hardware for cryptography. So that's, like, how we, like, started SASIC. And, like, while doing SASIC, like, we we do a lot of things, like, we do, like, the the GPU and FPGA acceleration. And we also did some, like, work
on ASIC design for for the k. So maybe just to to help listeners, just in case they don't know what some of these terms mean, ASIC is an application specific integrated circuit, if I'm not mistaken. It's kind of like specialized hardware. Often, you hear about it in the context of mining Bitcoin. You you you get ASICs, hardware in order to, mine Bitcoin really, really well because it's a specialized hardware that's really good at specifically the thing for, mining Bitcoin, for example. And then, if I'm not mistaken, cipher is also a cryptography term Yes. That I believe is, like if I'm not mistaken, is it it's the key? Is the cipher the key in, like, a cryptographic secret kind of way to explain it? So, like, in the encryption part, like, the encryption will turn in this, like, plaintext into into ciphertext. So ciphertext is encryptates,
plaintext.
So ciphertext is kind of, like, the secret itself, I guess. Yeah. So jumping off a couple of levels from, you know, assuming you guys understand a little bit about cryptography, which is just the math around, sharing secrets and, saving data and memory for computers, Zero knowledge cryptography or z k cryptography is a different type of cryptography that's a little bit more advanced and is kind of on the on the bleeding edge of things. Maybe you can explain a bit what is zero knowledge cryptography, and why is it, you know, why is it expensive to generate and and use right now? So we can, like, trade the ciphertext we just talk about as,
unlike encryption or, like, some representation of the plaintext, but in the in the ciphertext way. So like for ZK you can think about like we so ZK to competition is what ciphertext is to, plaintext. So like we basically compress a lot of like a competition into our like very tiny base stream. And this base stream will represent this, like, computation. But this, like, this stream is not like the hash stream. So you still have some, like, structure inside which will enable people to trust you if they verify that the the bitstream we handle to them, satisfies some requirements. So that's like how you can say, the the k, the the knowledge part. So it's like a representation of the of the computation you just carry out. Yeah. And and why it is, like, useful, it has, like, two nice properties. The first one is, the knowledge base and the second second one is the succinctness. The the knowledge base means, like, it will preserve the like the privacy, and without releasing the privacy, the private part like to you. Like for for instance, like I want to say like transfer probably like $10.10 bucks online. But I need to prove to the, say, to to the channel or, like, to some, like, authority that my bank balance is, like, more than $10 So, like, I can prove that part but without releasing, like, how, like what's the exact balance number like in my in my bank account. So like I handle you this like, this like they can proof and you can verify and then trust that my bank balance is like is larger than like $10 and then you can do the transfer. So that's all, their knowledge part. The second part is succinct. So the succinctness means like the the proof is like very short. It's probably like several 100 bytes or several 100 kilobytes. So it's a very small, proof which, which, like, you can use our proof to encode and, like, a very large, like, communication. So that's the first part of the succinctness. The second part is although the proof generation is like takes a lot of time, but the proof verification is like super super fast. You can verify they could prove on your cell phone like in less fifteen fifteen milliseconds and with a very very little, computing resource, so you can do the verification. So next as I say, we have this, like, my Sysic app, which you can download it. And then to verify the proof we generated, like, in part of our network on your cell phone. Yeah. So that's, like, two nice properties that they are knowledge less and succinctness of, like, SDK.
Right. Yeah. I think one of the ways that I've also heard people try to explain zero knowledge proofs is the example of, like, in The United States, for example, where if you want to go inside a bar, you have to show your your ID, your your driver's license. And, you know, you give your driver's license to the bouncer, and he doesn't he not only sees that maybe you are or are not, you know, old enough to enter, you're above 21, they know exactly your birthday, they know your full name, they see your address, they see a lot more information than maybe they necessarily need to know. So this kind of it's it allows you to say to the bouncer, I'm over 21 without giving any other information, without getting any any leakage of of unnecessary information. It's just kind of like a a way of doing selective disclosure, I guess, is this this is the the zero knowledge part, at least, aspect of of zero knowledge proofs. And I think that's why it's interesting from, you know, what are the implications of this technology is the ability to selectively disclose specific bits of information about yourself over a digital space, which is, you know, historically really hard to do because you you produce so much data when you're over the Internet and everybody, you know, if if you own the platforms or if you own, I don't know, a lot of the the piping between everything, then you know, maybe a bit more or you've given out more information than you intended to give out. Right. Yeah. Yeah. But and then, succinctness, it's also good for compressing data, I guess. Yes. Yes. Yeah. That's kind of how I think about it. Yeah. But are there any, like, maybe specific real world examples of zero knowledge proofs and cryptography kind of, in use that, like, you know, the average person may be able to to relate to for you? Right. Yeah. So, like, if so, like, we all know, like, the,
I, like, both, Bitcoin and Ethereum, they're, like, public ledger where, like, all people can check, like, the the balance or, like, how how many coins they have, like, in one, worldwide address. But there are some, like, sound privacy oriented, blockchain such as, like, Zcash or Aleo. They like if you do the they can basically shield the number of, like, tokens like we have in your wallet so that people cannot check the exact number. And all the transform public I would say, like, 80% of the transform also, like, happen privately and which preserves some privacy of like, the traders or like some other guy like who use, the blockchain. So that's like on the the knowledge nest part. And the second part is on the, succinctness. We can use the succinctness, like, to scale up on a blockchain. So like for take Ethereum for for instance, like if like how Ethereum, like, validates the transactions that they executes all these, like, transactions in Ethereum. So that's like, which is like why they put a gas limit for for for say like four block. But instead of like re execution, you can generate a ticket proof. Where like since like a ticket proof is like one proof you generate and everyone can verify it and then if it pass the verification then, then they will like trust like the the statement in this proof is true. So instead of like re execution, you can just verify from the k proof which proof, that all the transactions, they are like valid. And the verification of mechanism, they could prove takes a very, very small, guess. So which which means it will increase the guess limits, which you can, like, have, like, more transactions in in one block, like, for for Ethereum. So that's, like, two, nice applications of, they can in in blockchain.
Right. Privacy and scaling. I think kinda I think what's what's important to understand for a lot of first, for some users, at least, when I've tried to explain cryptography for them is that, like, a lot of cryptography can be used for both privacy and scaling and that they're kind of like, Competing with interest. The same property. Yeah. Yeah. Like, the same property of of of of this math that you're that you're using in these systems can be used for, like, things that you don't think are related, but they end up being related in in in these types of systems. I think it's really, really interesting. It's kind of like a when I realized that, I was like, oh, this is why it's, like, you know, quite expansive what what you can think about doing with this. Yeah. Yeah. So you mentioned that, like, proof ZKG proof generation is, fairly expensive or it's costly. It's, like, kinda slow. It's still, you know, a type of technology that people are figuring out how to do, better, faster, etcetera. But I'm curious why where where does that, like, cost come from? Why is zk proof generation so much slower than, like, say, you know, other forms of of cryptography that we're already using today? Yeah. So, like, I I guess, other
crypto tools, like, we are you using today is, like, encryption, digital signing, and also probably like some hash functions. So like compare against the, these tools, the, the data handled by by the capabilities, like, way much longer. So like a typical z case statement will handle this, like, degree of like very large degree polynomials. And the the degree is unit two to the 24 or something. So it's a very large polynomial. And for and the coefficient for this polynomial is also two fifty six bit long. So it's a large polynomial with a lot of like data in there. And handle this kind of polynomial will require enormous computing resources to do that. And also the computing resources like when they do the, process, they also need to, like, communicate with each other which means like you also need to a very high bandwidth to support, the transmission of the data between this, like, processing units. So that that's the, like, two most important parts in the competition, like, very large, computing resource and and and a very high, bandwidth to support the processing. So which also explains, like, why, like, it's quite expensive to generate that they could pull for you to have a machine that's, like, powerful enough to do both things, like, very efficiently.
Right. So it's just, basically, it's more math Yeah. Is the Yeah. Yeah. Is the simple answer. Yeah. Bigger numbers
take longer time. Yeah. Bigger numbers, takes longer time to both, like, compute and also to to transfer the thing.
Right. So I guess so this is kinda like my understanding at least is that then, you know, like your average computer hardware is not necessarily built at the moment to be generating zero knowledge, proofs. Mhmm. Yes. And so I guess this is kind of why, if I understand correctly from what you guys are doing, that you guys also provide hardware, like a a network of hardware for people to use to generate these proofs?
Yeah. Do you wanna do you wanna talk a little bit more about about the hardware side of things? Yeah. Sure. Sysdig is a CompuFi network. So by CompuFi, we means, like, we turn this, like, computer resources into some early machines for you. You can plug in your like servers, your laptop, or even your cell phone like into our network to do something something like in the computation. And of course like we categorize like, each we categorize this hour into the different groups, which is, like, responsible to do different things. Like for for instance, if you're plugging your, like, server public is powerful enough to generate something they can prove in a in a short period of time. And we also use this, like, servers to to do something like AI in inference for us. So so that's like the the the server side. If you plug in your, like, laptop or or even your cell phone, which is, like, not powerful enough to do the the two things I just mentioned, like, then we are fine. It's, like, they keep verification, AI verification job to you, which, like, you're not very powerful, computing. How hardware still can handle that and also some errors on, like, yield. And all the things were wrong in the in the back end, so you won't be able to notice any any any difference, like, in using your hardware.
Yeah. Right. Yes. There's, there's generation, the proof generation, which is taking in all the inputs that make up your your zero knowledge proof and, you know, depending on how you how you're, like, setting it up, you create you generate a proof, which is, like, kind of a reference to, you know, all these inputs and gives you kind of answer, but then that answer has to be verified by some by, you know, the person that receives the answer to double check to know that this proof is saying what you it's claiming to say. And if I understand correctly, the generation is much more, compute intensive than verification. Yes. Yes. That's right. So there's these these these two types of computes, verification, you know, if you got, probably any any probably laptop or or phone can do verification. The generation, maybe you can do, but it will take a lot longer than, like, you know, a beefier machine. But you guys are basically if I understand correctly now then, you guys are creating a kind of network where anybody can can bring whatever hardware they got, and they could potentially make a little bit of money off of it because other people are paying for the verification and the generation of z k proofs, on the network. Yeah. For for it. Yeah. Nice. Super cool. It reminds me of I don't know if you remember one of these early I think I think that was, Golem maybe. It was, like, one of the first kind of attempts to do something like this, if I'm not mistaken. I think there are other ones. It reminds me of, like, these proof of useful work blockchains that you used to have back in, like, twenty twenty seventeen Twenty seventeen. Yeah. Maybe. Yes.
Yeah. So so that's, like yeah. So at that time, like, people are saying, like, all this, like, Bitcoin mining is, like, a waste of, computing resources. It's, like, you produce some, like, specialized hardware, which will also cost a lot of, like, electricity in computing the hash. So people think, like, probably instead of doing that, like, we can use use, like, the resources, whether it's, like, electricity or computing power to do some, like, real jobs for human being. Yeah. So that's the time, like, in 2014 till 2017, I think. Yeah. So since this seems like my like before, coming to Cornell for PhD, and, like, I did some Bitcoin mining, back then. Yeah. For for for for several years. So, like and the idea of, like, using some specialized hardware to accelerate the case also, as you mentioned, like, this idea was also inspired by Bitcoin mining.
Okay. Nice. Yeah. And is, actually, which, network does CySEC,
connect to? Which blockchain networks? Yeah. So right now, we are, like, proving, so it also depends on the markets. So so probably like last year, like Sysiq is a biggest pover for for like several, large, like, near tooth including our score, the guessing. Yeah. Seems like they this near tooth, they their job is to scale up Ethereum. So they generate a lot of, like, transactions and they need also which means they need a lot of, like, computing resources, like, to to to generate their code for them. And, like, so for instance, like, we successfully, like, reduce the proof generation time from, like, one point five hours to about, like, twenty twenty minutes on a two car, two fourteen ninety ks, GPU car like machine. Yeah. So so so that's why, like, we so this, like, NARE two thing, they want to work with us to reduce the burden of, like, generating data proofs. But, like, so that's, like, something that's the safe situation for last for last year. But as you can see, like, all these Nerd two, they are not doing very well right now. Yeah. So so, like, we shift, like, some of our computing resources to support, Ethereum and also to support some perp DEX. Yeah. This perp DEX, they also need to generate z k for for each, for each transactions on their DEX. So, like so that's also, like, a very large number to not large number of, like, z k proof to to generate.
Okay. Are these perp do they verify those z k proofs on chain, or are they just generating the proof?
So, like like, our process is, like, we we, like, send we we can talk about the lifespan of, like, a ZK proof in in SaaS network. So so, like, for for for each, like, task we receive from, say, like, this, like, proof text, we pick, like, several, like, random, tuples, to generate the proof. And then like we pick a large number of like verifiers which like on your cell phone or on your laptop to verify the proof. After a proof is like verified say by eight out of like 10 verifiers, we cause this proof is like settles and we and then we put it on a size in network. And and also additionally like we send the verifier proof to the PerpDEX. So the proof is already like settled in our chain and some of the problem like, I I guess, the PerpDEX or like they don't have the energy to verify like each proof, but they just do some like sampling on the proof already settled by us, like, to do to check the validity of the proof.
Yeah. Mhmm. I see. Interesting. So you have, like, the network also has a level of kind of redundancy, I guess, where you you want to you want to be able to have trust in the in the proof, and so you have a few, at least, people run the same proof or verify the same proof, to make sure that it's valid. Yes.
Yes. I'm sorry. Yeah.
So maybe we can talk a little bit more about Computefy. I think it's interesting. I think there's there is, like, this general I mean, I'm just I I've I've never really engaged with any of this, but I've seen some stuff of, like, you know, people trying to financialize a bit of their their like, the computes on their on their computers to sell that as, like, future compute used in AI inference or something like that. I I'm wondering if you can explain a bit, like, how how do you see ComputeFi? What is it? And, like,
what does this future hold for us? Yeah. So, as you can see, probably, like, there are, like, several also similar platforms such as, like, io.net or some some some others. So but but they they are like the io.net or like the other, they are just creating a platform where, like, people plug in their, servers there and and wait to be, like, rented or, like, used by others. So it's basically like a AWS or, like, Vasta dot ai, this kind of platform, but they are in in in in the blockchain or crypto wars. So like, Sinsek differs, from these, competitors in a very significant way. So like in in addition to creating, creating this kind of like platform, we also bring some like real jobs, to be compute, by the by the servers, like in Sanjay network. Say like for for instance, the the lead capable generation is like one thing. And also we have this like, a lot of like AI products on on facing network, which also consume the under, consume the tokens. It's, like, generated by the underlying machines. So, like, in addition to just, creating a platform, we are we are bringing some, like, real jobs for the for the servers on the system network to to process. So that's a very big, big difference. Yeah.
Do do you think, like, in the future, you know, one one well, I guess the thing that kind of you're describing, just to to step back a bit, is almost like a, you know, this this, it's almost like a meme to me, but, like, this meme of the decentralized cloud, so to say, of, like, computes or computers being able to connect from anywhere to take part in this network and to provide that infrastructure in a way that is resilient, and redundance Yeah. At least at par with, you know, other types of cloud providers, and that people would be able to kind of, like, you know, have almost, like, have have like, make money off of the data center you have at home. Yes. Yeah. Yeah. You know, rather than necessarily the the mass build out of giant data centers, that's maybe going on right now. Maybe one alternative is we could use the excess computes that we already have available to us with everyone's consumer hardware rather than needing to, you know, overbuild, you know, all these amounts of data centers. This could be one kind of alternative,
to that. Yeah. Yes. That's why it's yeah.
So, yeah, are there any other, I guess the, yeah, are there any other non blockchain related z k examples that you're you're particularly interested in?
Right. Yeah. So, like, as you you can, as you mentioned before, like, the ID thing is, like, one, very good example. Like, in addition to showing up, like, your ID, you can just, generate that they can push, saying, like, you are, like, older than 21, then you can enter the, enter the, the bar. And another example is, like, there are some, events, like, say, like, probably, like, last year or the year be before last year, there is a there is a dev con in in Thailand. And when, like, if you are, like, a local, like, in in Thailand, like, you can apply for some, apply for a special discount on on on the ticket for for the event. And how how you, like, proof you are, like, a local, like, is like, previously it's like you just upload your your passport and then you are showing like you are local. But like that will review a lot of information you don't want to reveal. So like very nice way of like using zk to wrap around it like you download a software and then you generate the zk proof, of your passport showing that you are, from like Highlands and then you upload only the zk proof to, to the ticket one website. And then then after they verifies, then you can go the next special discount. Yeah. So that's yeah. It's basically digital ID, and you can use a digital ID, like, to do a lot of, like, things. Yeah.
Right. I and this is, like I mean, this is super interesting to think about just in the current context with the amount of legislation that's being passed right now, requiring people to prove that they're, you know, over 18 or whatever to use certain platforms over the Internet. I mean, it is something that, is being pushed. Different people have different different views on, like, whether or not it's, like, the right approach to kind of actually solve the problem that they that they think they're trying to solve around, protecting kids. And so yeah. I mean, ZK, it it what what is interesting to me is that, like, at least as far as I could tell, a lot of the the technical, specs of a lot of these identity solutions from, I think, particularly, like, The UK and Australia. I know I had some, and I think The US is working on something. But, these these identity solutions don't include z k, as far as I as far as I know, which is interesting because you would you would think that, you know, if if you actually wanted to solve the actual problem Yeah. That they say that they're trying to solve while also not causing any externalities, AKA creating a giant honeypot of people's data in someone's databases, like, then zero zero knowledge proof should be, like, your first that should be, like, one of the first things you should be considering, you know. Right? Right?
Yeah. So I yeah. So, like, we are pushing, like, The UK public to the traditional world or, like, the the mainstream to make people aware of that and then, like, to use to use it. Seems like if they can, like, you don't need to trust anyone. Like, you just trust the math and the math, like, you can do it by yourself. And we are not creating, like, a honeypot, like, for, for the hackers to hack. They adjust that they can proof and they can proof, like due to the nice properties of the k. You basically cannot get anything out of like of the k proofs there. Yeah. It's it's like more reliable, more transparent, I would say, like, compared with the current solution. Yeah.
Yeah. Right. So yeah. I don't know. Call I think it's, like, we're at we're at a really funny stage in in in politics almost where, like, you know, it's like call call your senator and teach them about zero knowledge proofs because Yeah. What they're proposing right now in this legislation, like, is not is not privacy preserving at all. It, like, really highly highly depends on, you know Yeah. Someone to build a database that is, like, just going to get hacked. It's not a matter of if, it's a matter of when, you know. Yeah. Yeah. It's just a matter of time to to hack this kind of database.
Yeah. Yeah.
Go go to your capital and teach your, government officials a little bit of math, a little bit of zero knowledge cryptography. But, yeah, thanks, Leo. Is there any are there any other
last things that you wanted to mention before we close it off? Something like we we also want to mention is, like, we open source, like, the DKVM, code repository. Like, you can try it out, and we have a lot of, like, products on on on Fisic. You can just check out, like, fisic, .xyz for our, like, for our products. And then you can follow us on Twitter, like, it's, c y s I c dash, x y z to for the latest updates, like, from our team. Yeah. So we have a lot of, like, interesting product ongoing.
Cool. And how how can can people right now today be able to join the network to be able to make a bit of money off of their hardware? Yes. Yes. It's, like, total,
permission is, like, you can use your, like even if you don't have a very powerful server, you can always use your laptop or even your cell phone to join. You can download, like, my Scisic app on on your whether it's, like, Apple or, like, Android phone, you can always try it out.
Cool. Yeah. Great. Well, thanks, Leo, so much for coming on, and appreciate you explaining Zero Knowledge Proofs to us and sharing about Scizac. Thanks a lot. Thanks a lot for for having me here. Yeah. I had a very nice conversation. Yeah. If you like what I'm doing here, consider supporting the show on Patreon. Your contributions help me keep doing this work and dive deeper into the politics of decentralized technologies. I promise you absolutely zero financial returns, no airdrops, and your investment may go to zero. But you will get good content. Check out patreon.com/theblockchainsocialist to support the show.