Master of Computer Science, ENS Ulm
A verification framework for privacy-preserving machine learning
Machine learning is known to be hungry for data, which is often private. Recent advances in privacy-preserving machine learning use new cryptographic techniques to avoid exposing private data. However, such cryptographic implementations are error-prone, resulting in information leakage. Therefore, I use the F* software verifier to implement modern multiparty computation protocols, such as SPDZ2k.