Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

Personal data are often collected and processed in a decentralized fashion, within different
contexts. For instance, with the emergence of distributed applications, several providers are …

At IEEE Security & Privacy 2015, Bos, Costello, Naehrig, and Stebila proposed an
instantiation of Peikert's ring-learning-with-errors–based (Ring-LWE) key exchange protocol …

We present SimplePIR, the fastest single-server private information retrieval scheme known
to date. SimplePIR's security holds under the learning-with-errors assumption. To answer a …

We introduce the SPIRAL family of single-server private information retrieval (PIR) protocols.
SPIRAL relies on a composition of two lattice-based homomorphic encryption schemes: the …

Private information retrieval (PIR) is a key building block in many privacy-preserving
systems. Unfortunately, existing constructions remain very expensive. This paper introduces …

We construct a sublinear-time single-server preprocessing Private Information Retrieval
(PIR) scheme with an optimal tradeoff between client storage and server computation (up to …

Function Secret Sharing (FSS), introduced by Boyle et al.(Eurocrypt 2015), provides a way
for additively secret-sharing a function from a given function family F. More concretely, an m …

The rapidly growing scale and variety of biomedical data repositories raise important privacy
concerns. Conventional frameworks for collecting and sharing human subject data offer …

This paper presents OnionPIR and stateful OnionPIR, two single-server PIR schemes that
significantly improve the response size and computation cost over state-of-the-art schemes …