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 …

We construct a new polynomial commitment scheme for univariate and multivariate
polynomials over finite fields, with logarithmic size evaluation proofs and verification time …

Federated learning (FL) is an increasingly popular approach for machine learning (ML) in
cases where the training dataset is highly distributed. Clients perform local training on their …

Machine learning tools have illustrated their potential in many significant sectors such as
healthcare and finance, to aide in deriving useful inferences. The sensitive and confidential …

This paper presents a new protocol for solving the private heavy-hitters problem. In this
problem, there are many clients and a small set of data-collection servers. Each client holds …

Zero knowledge proofs are an important building block in many cryptographic applications.
Unfortunately, when the proof statements become very large, existing zero-knowledge proof …

Federated learning (FL) enables clients to collaborate with a server to train a machine
learning model. To ensure privacy, the server performs secure aggregation of updates from …

Performing machine learning (ML) computation on private data while maintaining data
privacy, aka Privacy-preserving Machine Learning (PPML), is an emergent field of research …

A zk-SNARK is a powerful cryptographic primitive that provides a succinct and efficiently
checkable argument that the prover has a witness to a public NP statement, without …

Secure multi-party computation (MPC) allows a set of parties to jointly compute a function on
their private inputs, and reveals nothing but the output of the function. In the last decade …