Federated learning (FL) and split learning (SL) are two emerging collaborative learning
methods that may greatly facilitate ubiquitous intelligence in the Internet of Things (IoT) …

Motivated by the advancing computational capacity of distributed end-user equipment (UE),
as well as the increasing concerns about sharing private data, there has been considerable …

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 …

Private Set Intersection (PSI) is a cryptographic technique that allows two parties to compute
the intersection of their sets without revealing anything except the intersection. We use fully …

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 …

We present a 2-party private set intersection (PSI) protocol which provides security against
malicious participants, yet is almost as fast as the fastest known semi-honest PSI protocol of …

We describe a novel approach for two-party private set intersection (PSI) with semi-honest
security. Compared to existing PSI protocols, ours has a more favorable balance between …

We consider the problem of securely generating useful instances of two-party correlations,
such as many independent copies of a random oblivious transfer (OT) correlation, using a …

We present a new protocol for two-party private set intersection (PSI) with semi-honest
security in the plain model and one-sided malicious security in the random oracle model …