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 …

Abstract Machine learning classification is used in numerous settings nowadays, such as
medical or genomics predictions, spam detection, face recognition, and financial predictions …

Ridge regression is an algorithm that takes as input a large number of data points and finds
the best-fit linear curve through these points. The algorithm is a building block for many …

Protocols for secure computation enable parties to compute a joint function on their private
inputs without revealing anything but the result. A foundation for secure computation is …

Secure two-party computation enables two parties to evaluate a function cooperatively
without revealing to either party anything beyond the function's output. The garbled-circuit …

Abstract Cryptographic protocols for Private Set Intersection (PSI) are the basis for many
important privacy-preserving applications. Over the past few years, intensive research has …

Large transformer-based models have realized state-of-the-art performance on lots of real-
world tasks such as natural language processing and computer vision. However, with the …

Recommender systems typically require users to reveal their ratings to a recommender
service, which subsequently uses them to provide relevant recommendations. Revealing …