Speech recordings are a rich source of personal, sensitive data that can be used to support
a plethora of diverse applications, from health profiling to biometric recognition. It is therefore …

The rapid development of cloud computing promotes a wide deployment of data and
computation outsourcing to cloud service providers by resource-limited entities. Based on a …

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 …

We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function
evaluation (SFE) which enables two parties to jointly compute a function without disclosing …

Abstract Secure Multi-Party Computation (SMPC) is a generic cryptographic primitive that
enables distributed parties to jointly compute an arbitrary functionality without revealing their …

Privacy-preserving multi-party machine learning allows multiple organizations to perform
collaborative data analytics while guaranteeing the privacy of their individual datasets …

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 …

We present CrypTFlow, a first of its kind system that converts TensorFlow inference code into
Secure Multi-party Computation (MPC) protocols at the push of a button. To do this, we build …