Machine learning (ML) is increasingly being adopted in a wide variety of application
domains. Usually, a well-performing ML model relies on a large volume of training data and …

The increasing volume of personal and sensitive data being harvested by data controllers
makes it increasingly necessary to use the cloud not just to store the data, but also to …

Multi-Protocol SPDZ (MP-SPDZ) is a fork of SPDZ-2 (Keller et al., CCS'13), an
implementation of the multi-party computation (MPC) protocol called SPDZ (Damgård et al …

Secure Multi-party Computation (MPC) allows a set of mutually distrusting parties to jointly
evaluate a function on their private inputs while maintaining input privacy. In this work, we …

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 …

Secure computation enables mutually distrusting parties to jointly evaluate a function on
their private inputs without revealing anything but the function's output. Generic secure …

Important decisions about people are increasingly made by algorithms: Votes are counted;
voter rolls are purged; financial aid decisions are made; taxpayers are chosen for audits; air …

This paper presents Prio, a privacy-preserving system for the collection of aggregate
statistics. Each Prio client holds a private data value (eg, its current location), and a small set …

Machine learning has started to be deployed in fields such as healthcare and finance, which
propelled the need for and growth of privacy-preserving machine learning (PPML). We …

Designing an efficient cipher was always a delicate balance between linear and non-linear
operations. This goes back to the design of DES, and in fact all the way back to the seminal …