Nowadays, with the development of artificial intelligence (AI), privacy issues attract wide
attention from society and individuals. It is desirable to make the data available but invisible …

This article presents an overview of image transformation with a secret key and its
applications. Image transformation with a secret key enables us not only to protect visual …

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 …

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 …

Efficient zero-knowledge (ZK) proofs for arbitrary boolean or arithmetic circuits have recently
attracted much attention. Existing solutions suffer from either significant prover overhead (ie …

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 …

This work introduces a novel four-party honest-majority MPC protocol with active security
that achieves comparable efficiency to equivalent protocols in the same setting, while having …

Recent progress in interactive zero-knowledge (ZK) proofs has improved the efficiency of
proving large-scale computations significantly. Nevertheless, real-life applications (eg, in the …