Cryptography Will Revolutionize AI Data Privacy with Daniel Kang
"The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis - A podcast by Erik Torenberg, Nathan Labenz
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In this episode, Nathan sits down with Daniel Kang, Assistant Professor of Computer Science at the University of Illinois. Kang has done pioneering work bringing zero knowledge cryptographic proofs to AI. In this episode, they chat about the cryptographic theory behind Daniel's work, how cryptography allows us to balance the tradeoff between privacy and authenticity, and how cryptography usage is needed in a world where LLMs are increasingly embedded into our daily lives. If you're looking for an ERP platform, check out our sponsor, NetSuite: http://netsuite.com/cognitive We're hiring across the board at Turpentine and for Erik's personal team on other projects he's incubating. He's hiring a Chief of Staff, EA, Head of Special Projects, Investment Associate, and more. For a list of JDs, check out: eriktorenberg.com. CALL FOR FEEDBACK: To borrow from a meme… we're in the podcast arena trying stuff. Some will work. Some won't. But we're always learning. http://bit.ly/TCRFeedback Fill out the above form to let us know how we can continue delivering great content to you or sending the feedback on your mind to [email protected]. TIMESTAMPS: (00:00) Episode Preview (00:01:04) Nathan's Introduction (00:07:06) Motivation for bringing zero-knowledge proofs to AI (00:10:27) Verifying model execution without revealing model details (00:12:42) Verifying medical AI services haven't been tampered with (00:15:09) Sponsors: Netsuite | Omneky (00:18:54) Cryptographic hashes for commitments (00:22:42) Assumptions underlying cryptographic hashes (00:25:20) Adding entropy through salting (00:26:24) Z case snarks and the proving process (00:31:00) Using lookup tables for nonlinearities (00:33:35) Floating point vs fixed point calculations (00:34:08) Quantizing models for efficiency (00:35:55) Using polynomials to represent arbitrary computations (00:37:26) What are finite fields? (00:41:23) Toxic waste for cryptographic secrecy (00:45:51) Computational costs (00:47:39) The experience of using a cryptography application to verify model output (00:49:05) Verification key doesn't reveal model weights (00:56:36) What using crypto infrastructure in AI enables and challenges to its implementation (01:01:26) Potential for 10-100x cost reductions (01:04:51) Authenticating images with attested cameras (01:11:56) How cryptography in AI could impact daily life (01:14:25) On-device credential verification (01:15:50) Potential for regulation of hardware authentication (01:18:52) Upcoming work to reduce proof costs LINKS: Daniel's website SPONSORS: Shopify | NetSuite | Omneky Shopify is the global commerce platform that helps you sell at every stage of your business. Shopify powers 10% of ALL eCommerce in the US. And Shopify's the global force behind Allbirds, Rothy's, and Brooklinen, and 1,000,000s of other entrepreneurs across 175 countries.From their all-in-one e-commerce platform, to their in-person POS system – wherever and whatever you're selling, Shopify's got you covered. With free Shopify Magic, sell more with less effort by whipping up captivating content that converts – from blog posts to product descriptions using AI. Sign up for $1/month trial period: https://shopify.com/cognitive NetSuite has 25 years of providing financial software for all your business needs. More than 36,000 businesses have already upgraded to NetSuite by Oracle, gaining visibility and control over their financials, inventory, HR, eCommerce, and more. If you're looking for an ERP platform ✅ head to NetSuite: http://netsuite.com/cognitive and download your own customized KPI checklist. Omneky is an omnichannel creative generation platform that lets you launch hundreds of thousands of ad iterations that actually work customized across all platforms, with a click of a button. Omneky combines generative AI and real-time advertising data. Mention "Cog Rev" for 10% off. Music Credit: GoogleLM