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 …

Complex machine learning (ML) inference algorithms like recurrent neural networks (RNNs)
use standard functions from math libraries like exponentiation, sigmoid, tanh, and reciprocal …

Secure Two-party Computation (2PC) allows two parties to compute any function on their
private inputs without revealing their inputs to each other. In the offline/on-line model for …

Applications today rely on cloud databases for storing and querying time-series data. While
outsourcing storage is convenient, this data is often sensitive, making data breaches a …

This paper introduces protocols for authenticated private information retrieval. These
schemes enable a client to fetch a record from a remote database server such that (a) the …

A pseudorandom correlation generator (PCG) is a recent tool for securely generating useful
sources of correlated randomness, such as random oblivious transfers (OT) and vector …

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 …

We propose AriaNN, a low-interaction privacy-preserving framework for private neural
network training and inference on sensitive data. Our semi-honest 2-party computation …

Abstract Secure 2-party computation (2PC) enables secure inference that offers protection
for both proprietary machine learning (ML) models and sensitive inputs to them. However …

Secure machine learning (ML) inference can provide meaningful privacy guarantees to both
the client (holding sensitive input) and the server (holding sensitive weights of the ML …