guruvamsi-policharla/zkpot
A prototype implementation of zero-knowledge proofs of training introduced in eprint:2023/1345
This project offers an experimental Rust implementation for creating zero-knowledge proofs of training for logistic regression models. It takes in a trained model and produces a cryptographic proof that the model was trained correctly, without revealing the underlying training data or model parameters. This would be used by a cryptographer or researcher exploring advanced privacy-preserving machine learning techniques.
No commits in the last 6 months.
Use this if you are a cryptographer or academic researcher exploring the theoretical and practical aspects of zero-knowledge proofs applied to machine learning model training.
Not ideal if you need a production-ready system for securely deploying or verifying machine learning models, as this is a proof-of-concept and not ready for real-world use.
Stars
18
Forks
5
Language
Rust
License
MIT
Category
Last pushed
Sep 12, 2023
Commits (30d)
0
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