Cryptography Will Revolutionize AI Data Privacy with Daniel Kang

**Daniel Kang** (0:00)
So if I want to somehow prove that I'm human today, the way that I need to do that is to essentially reveal a lot of information about myself because AI generation methods are becoming so powerful. Maybe we don't want this. So for example, you have services which take pictures of your face to verify that you're a real human being. But the way this works is that they literally upload your biometrics to a server. There's a lot of potentially problematic things that come with this, including the potential for surveillance. But cryptography gives us a hammer that lets us bypass this fundamental trade-off between privacy and authenticity. That's what I'm really excited about.

**Nathan Labenz** (0:41)
Hello, and welcome to The Cognitive Revolution, where we interview visionary researchers, entrepreneurs, and builders working on the frontier of artificial intelligence. Each week, we'll explore their revolutionary ideas, and together we'll build a picture of how AI technology will transform work, life, and society in the coming years. I'm Nathan Labenz, joined by my co-host, Eric Thornburg. Hello, and welcome back to The Cognitive Revolution. Today, I'm very excited to be speaking with Professor Daniel Kang, Assistant Professor of Computer Science at the University of Illinois, who has done pioneering work bringing zero-knowledge cryptographic proofs to the domain of AI inference. Making it possible for people to prove that a model has been faithfully executed as promised based on certain pre-commitments and cryptographic calculations.
Now, as someone who knows very little about cryptography and the modern crypto space writ large, I have been wondering for some time what it might mean for AI to put the smart in crypto's so-called smart contracts. As someone who has developed some very Byzantine heuristic systems in my day, it always seemed to me that the core limitation of smart contracts has been the fact that everything needs to be fully spelled out with explicit code. So could the use of language models in the context of contract execution and dispute resolution change the potential for smart contracts and perhaps a lot more besides? It's been surprisingly difficult to find someone who could inform my thinking on this question, but Daniel Kang is indeed the perfect person. Motivated by the growing trade-off we face between authenticity and privacy, Daniel set out to develop methods that would allow people to answer key questions without having to reveal the specific inputs used to calculate the answer. Now that's a mouthful, so here's a couple of examples. One, a human might prove their humanity by running an AI model on biometric data collected and analyzed locally on their phone without having to reveal the biometric information itself, or an AI model provider like OpenAI might prove that it ran the model it promised to run and not a smaller, faster, cheaper one instead. And yes, as in my original thought experiment, an AI might be used to resolve disputes where multiple parties can submit private data and all can verify that the model ran properly. Daniel even envisions broad use of so-called attested devices, which cryptographically sign the data they capture immediately on device so that we can trace information such as photographs or audio recordings back to their physical source. The cost and complexity do remain limiting factors for these techniques for now. But Daniel does see a path to making such techniques a seamless part of our future AI infrastructure, much like HTTPS is for web graphic today. And the fact that he undertook this work in the first place shows an unusually forward thinking approach. So I was really excited to ask him not only all about how this technology works, but also about how he expects it to be used as society adapts to and hopefully takes full advantage of AI. To be honest, I'm not sure I ever achieved the level of intuition for the cryptographic math that I might have hoped, but I definitely came away with a much better sense of what sorts of things can be proved, why we might need such proofs in the near term future, and how this technology can be useful in all sorts of as yet undiscovered and likely quite unpredictable ways. As always, if you're finding value in our attempts to understand the near term future, we always appreciate your reviews and comments. One that recently came in on Apple podcasts made my day. The reviewer says, I'm a full stack web developer with an interest in AI. I've followed generative AI loosely for a while, but with Chess GPT and all these amazing products coming out, I felt like I had severely fallen behind. When I found this podcast from the very first episode, I was hooked. Each episode was not only informative but also thought provoking. I went back and listened to all of the podcast episodes from the start, and after each episode, I'd spend time learning about any concepts I didn't quite understand. I've used the podcast as a guide and as of this week, a catalyst for my career growth. I have just accepted a position as a full-time AI engineer at a Fortune 100 organization, where I'll be helping other developers learn and use AI to build AI-powered products. I can't wait to give back to the community. Thanks, Nathan, for being such a great scout through this cognitive revolution. All I can say to that is wow and thank you, and I hope to continue to live up to that review, starting with this conversation about the application of zero-knowledge cryptographic proofs to AI inference with Professor Daniel Kang. Daniel Kang, welcome to The Cognitive Revolution.