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 …

Machine learning tools have illustrated their potential in many significant sectors such as
healthcare and finance, to aide in deriving useful inferences. The sensitive and confidential …

We propose a general multiparty computation protocol secure against an active adversary
corrupting up to n-1 of the n players. The protocol may be used to compute securely …

We present a new protocol for computing a circuit which implements the private set
intersection functionality (PSI). Using circuits for this task is advantageous over the usage of …

In a data-driven society, individuals and companies encounter numerous situations where
private information is an important resource. How can parties handle confidential data if they …

Protocols for secure multiparty computation enable a set of parties to compute a function of
their inputs without revealing anything but the output. The security properties of the protocol …

A zk-SNARK is a powerful cryptographic primitive that provides a succinct and efficiently
checkable argument that the prover has a witness to a public NP statement, without …

The concrete efficiency of secure computation has been the focus of many recent works. In
this work, we present concretely-efficient protocols for secure 3-party computation (3PC) …

Secure multi-party computation (MPC) allows a set of parties to jointly compute a function on
their private inputs, and reveals nothing but the output of the function. In the last decade …