There's also situations where people want to, you know, a bunch of different entities want to learn from their combined data. So these techniques could definitely help allow that to happen. And this can help like kind of protect, let people learn from training data without revealing it to the learner or something like that. Yeah. You know, allowing like the smart contracts to learn from on-chain data,. Like more data goes on chain and they update,you know, their classifier or something. That doesn't necessarily require any privacy, but you know, then you can bring in privacy too.
This week, Anna chats with Justin Thaler, Associate Professor at Georgetown. They cover Justin’s academic history and discuss what led him to working on interactive proofs and SNARKs. They also take a look at several other topics such as the Thaler Book Study Group, his earlier work Spartan, comparing the security of different rollups built with SNARKs and STARKs and more.
Here are some additional links for this episode:
Apply for zkSummit9 here: zkSummit9 Ticket Application.
Check out ingonyama.com to learn more about Zero Knowledge Hardware acceleration.
Aleo is a new Layer-1 blockchain that achieves the programmability of Ethereum, the privacy of Zcash, and the scalability of a rollup.
Interested in building private applications? Check out Aleo’s programming language called Leo by visiting http://developer.aleo.org.
You can also participate in Aleo’s incentivized testnet3 by downloading and running a snarkOS node. No sign-up is necessary to participate.
For questions, join their Discord at aleo.org/discord.
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