Multi-Protocol SPDZ (MP-SPDZ) is a fork of SPDZ-2 (Keller et al., CCS'13), an
implementation of the multi-party computation (MPC) protocol called SPDZ (Damgård et al …

Federated learning (FL) is an emerging paradigm that enables multiple organizations to
jointly train a model without revealing their private data to each other. This paper studies {\it …

We present CrypTFlow, a first of its kind system that converts TensorFlow inference code into
Secure Multi-party Computation (MPC) protocols at the push of a button. To do this, we build …

This work introduces a novel four-party honest-majority MPC protocol with active security
that achieves comparable efficiency to equivalent protocols in the same setting, while having …

This work introduces novel techniques to improve the translation between arithmetic and
binary data types in secure multi-party computation. We introduce a new approach to …

We implement training of neural networks in secure multi-party computation (MPC) using
quantization commonly used in said setting. We are the first to present an MNIST classifier …

We present EzPC, a secure two-party computation (2PC) framework that generates efficient
2PC protocols from high-level, easy-to-write programs. EzPC provides formal correctness …

Most modern actively-secure multiparty computation (MPC) protocols involve generating
random data that is secret-shared and authenticated, and using it to evaluate arithmetic or …

In this paper, we perform a systematic study of functions f: Z pe→ Z pe and categorize those
functions that can be represented by a polynomial with integer coefficients. More specifically …

At CRYPTO 2018, Cramer et al. introduced a secret-sharing based protocol called SPD Z _
2^ k that allows for secure multiparty computation (MPC) in the dishonest majority setting …