In this lecture, we look into advanced cryptographic protocols that are used for Secure Multiparty Computation (SMPC). We consider in particular secret sharing, oblivious transfer and garbled circuits. We then see how these protocols can be used to enhance the privacy of machine learning models, especially in the setting of MLaaS (Machine Learning as a Service) and federated learning.
- Basics of Secure Multiparty Computation (SMPC)
- Card-based game for the 2-party secure AND computation
- Hard-core predicates and randomized RSA
- 1-2 Oblivious Transfer from RSA
- Garbled Circuits
- Secret Sharing Schemes and their combinatorial characterization
- SMPC for private Machine Learning
- Federated Learning
- Slides of the lecture
- Slides used to introduce secret sharing schemes with Latin squares and orthogonal arrays
Nice and short textbook on SMPC, freely available on the authors’ websites:
- D. Evans, V. Kolesnikov, M. Rosuler: A Pragmatic Introduction to Secure Multi-Party Computation. NOW Publishers, 2018
Survey on privacy-preserving machine learning:
- Runhua Xu, Nathalie Baracaldo, James Joshi: Privacy-Preserving Machine Learning: Methods, Challenges and Directions. CoRR abs/2108.04417 (2021)
The videos are presented below in logical order, although chronologically they have been recorded in different lectures (specifically: introduction to secret sharing at the end of lecture 5, oblivious transfer and garbled circuits in lecture 7, applications to ML in lecture 8).