A Pragmatic Introduction to Secure Multi-Party Computation, co-authored with Vladimir Kolesnikov and Mike Rosulek, is now published by Now Publishers in their Foundations and Trends in Privacy and Security series.
You can download the book for free (we retain the copyright and are allowed to post an open version) from securecomputation.org, or buy an PDF version from the published for $260 (there is also a printed $99 version).
Secure multi-party computation (MPC) has evolved from a theoretical curiosity in the 1980s to a tool for building real systems today.
Bargav Jayaraman presented our work on privacy-preserving machine learning at the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018) in Montreal.
Distributed learning (sometimes known as federated learning) allows a group of independent data owners to collaboratively learn a model over their data sets without exposing their private data. Our approach combines differential privacy with secure multi-party computation to both protect the data during training and produce a model that provides privacy against inference attacks.