I want to be able to preserve privacy while keeping all the great parts of biometric authentication. I mean, it depends on how much you use a stable diffusion thing that keeps a lot of the original data. You've chosen the particular generative output that you're willing to use. That's why we have to attest to the input. Otherwise you could just put a celebrities image as the input. It's funny. In planning for this, I didn't actually think about the tested sensor. And now I'm sort of fascinated with the fact that that doesn't exist normally and that we have to create the thing that makes it tamper proof. This is a true documentation of this moment in
This week, Anna Rose and Tarun Chitra dive back into the topic of ZK ML with guests Yi Sun, co-founder of Axiom, and Daniel Kang, Assistant Professor of computer science at UIUC. They discuss Yi and Daniel’s previous academic work and what led them to get interested in ZK topics and specifically ZK ML. They then dive into a discussion about 2 recent papers which examine the use of ZK within Machine Learning architectures.
Here are some additional links for this episode:
Apply for ZK Hack Lisbon here: ZK Hack application
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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