Privacy is an important research topic due to its implications in society. Among the topics
covered by privacy, we can highlight how to establish anonymous communications. During …

In this paper we propose efficient two-party protocols for obliviously applying a (possibly
random) linear group action to a data set. Our protocols capture various applications such as …

Secure shuffle is an important primitive that finds use in several applications such as secure
electronic voting, oblivious RAMs, secure sorting, to name a few. For time-sensitive shuffle …

In the shuffle model for differential privacy, n users locally randomize their data and submit
the results to a trusted “shuffler” who mixes the results before sending them to a server for …

Privacy-preserving data mining has become an important topic. People have built several
multi-party-computation (MPC)-based frameworks to provide theoretically guaranteed …

Motivated by recent developments in the shuffle model of differential privacy, we propose a
new approximate shuffling functionality called Alternating Shuffle, and provide a protocol …

Private data collection systems such as Prio ensure data privacy by distributing trust among
a set of mutually trusted parties, to allow for aggregate data collection without disclosing any …

Federated learning has successfully mitigated the privacy concerns for deep learning over
distributed data, but suffers from performance decay led by the non-identical distribution of …

Multiparty computation is raising importance because its primary objective is to replace any
trusted third party in the distributed computation. This work presents two multiparty shuffling …

Exploiting participants' data without knowing them is the center topic of secure mobile
sensing data aggregation. In this paper, we study how to improve existing protocols for …