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 …

The growth of the Machine-Learning-As-A-Service (MLaaS) market has highlighted clients'
data privacy and security issues. Private inference (PI) techniques using cryptographic …

When two or more parties need to compute a common result while safeguarding their
sensitive inputs, they use secure multiparty computation (SMC) techniques such as garbled …

Secure multi-party computation (MPC) is an essential tool for privacy-preserving machine
learning (ML). However, secure training of large-scale ML models currently requires a …

We introduce Tiny Garble, a novel automated methodology based on powerful logic
synthesis techniques for generating and optimizing compressed Boolean circuits used in …

Many techniques for secure or private execution depend on executing programs in a data-
oblivious way, where the same instructions execute independent of the private inputs which …

We propose introducing modern parallel programming paradigms to secure computation,
enabling their secure execution on large datasets. To address this challenge, we present …

There is a growing need for accurate and efficient real-time state estimation with increasing
complexity, interconnection, and insertion of new devices in power systems. In this paper, a …

Abstract Secure 2-party computation (2PC) enables secure inference that offers protection
for both proprietary machine learning (ML) models and sensitive inputs to them. However …