Secure Computation | Security Research Group

Chinese Translation of MPC Book

7 June 2021 secure computation, Vladimir Kolesnikov, Mike Rosulek

A Chinese translation of our A Pragmatic Introduction to Secure Multi-Party Computation book (by David Evans, Vladimir Kolesnikov, and Mike Rosulek) is now available!

Thanks to Weiran Liu and Sengchao Ding for all the work they did on the translation.

To order from JD.com: https://item.jd.com/13302742.html

(The English version of the book is still available for free download, from https://securecomputation.org.)

A Pragmatic Introduction to Secure Multi-Party Computation

19 December 2018 secure computation, Vladimir Kolesnikov, Mike Rosulek

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. Over the past decade, MPC has been one of the most active research areas in both theoretical and applied cryptography. This book introduces several important MPC protocols, and surveys methods for improving the efficiency of privacy-preserving applications built using MPC. Besides giving a broad overview of the field and the insights of the main constructions, we overview the most currently active areas of MPC research and aim to give readers insights into what problems are practically solvable using MPC today and how different threat models and assumptions impact the practicality of different approaches.

NeurIPS 2018: Distributed Learning without Distress

8 December 2018 privacy-preserving machine learning, secure computation, Bargav Jayaraman

Bargav Jayaraman presented our work on privacy-preserving machine learning at the 32^nd 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.