ZKML aims to generate zero knowledge proofs that a machine learning model has run on specific input data. This method inherits the benefits of zero knowledge proofs such as succinctness, zero knowledge, completeness, and soundness. One practical application of ZKML is to prove the accurate execution of machine learning models, especially when the models are operated behind an API interface. For instance, it can be used to verify that an ML provider is keeping their model weights confidential when processing external data.
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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