Engineering Privacy-Preserving Machine Learning Protocols
Thomas H. Schneider · 2020
Privacy-preserving machine learning (PPML) protocols allow to privately evaluate or even train machine learning (ML) models on sensitive data while simultaneously protecting the data and the model. So far, most of these protocols were built and optimized by hand, which requires expert knowledge in cryptography and also a thorough understanding of the ML models. Moreover, the design space is very large as there are many technologies that can even be combined with several trade-offs. Examples for the underlying cryptographic building blocks include homomorphic encryption (HE) where computation typically is the bottleneck, and secure multi-party computation protocols (MPC) that rely mostly on symmetric key cryptography where communication is often the~bottleneck.