Abstract Secure Multi-party Computation (SMPC) is a family of protocols which allow some
parties to compute a function on their private inputs, obtaining the output at the end and …

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 …

Secure multiparty computation Page 1 86 COMMUNICATIONS OF THE ACM | JANUARY 2021 |
VOL. 64 | NO. 1 review articles DISTRIBUTED COMPUTING CONSIDERS the scenario where a …

Data science is the foundation of our modern world. It underlies applications used by billions
of people every day, providing new tools, forms of entertainment, economic growth, and …

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 …

We present a new protocol for computing a circuit which implements the private set
intersection functionality (PSI). Using circuits for this task is advantageous over the usage of …

Private Set Intersection (PSI) allows two parties, the sender and the receiver, to compute the
intersection of their private sets without revealing extra information to each other. We are …

While there has been a lot of progress in designing efficient custom protocols for computing
Private Set Intersection (PSI), there has been less research on using generic Multi-Party …

Secure Multi-Party Computation (MPC) allows mutually distrusting parties to run joint
computations without revealing private data. Current MPC algorithms scale poorly with data …

Federated analytics (FA) is a privacy-preserving framework for computing data analytics
over multiple remote parties (eg, mobile devices) or silo-ed institutional entities (eg …