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 …

Federated learning (FL) is an emerging paradigm that enables multiple organizations to
jointly train a model without revealing their private data to each other. This paper studies {\it …

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 …

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 …

Differential privacy has been a de facto privacy standard in defining privacy and handling
privacy preservation. It has had great success in scenarios of local data privacy and …

Shortly after it was first introduced in 2006, differential privacy became the flagship data
privacy definition. Since then, numerous variants and extensions were proposed to adapt it …

Sensitive personal data are created in many application domains, and there is now an
increasing demand to share, integrate, and link such data within and across organisations in …

Differential privacy (DP) is currently the de-facto standard for achieving privacy in data
analysis, which is typically implemented either in the" central" or" local" model. The local …

Encrypted multi-map (EMM), as a special case of structured encryption, has attracted
extensive attention recently. However, most of EMM constructions reveal the real volumes of …

A private data federation enables clients to query the union of data from multiple data
providers without revealing any extra private information to the client or any other data …