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 …

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 …

Privacy-preserving machine learning (PPML) via Secure Multi-party Computation (MPC) has
gained momentum in the recent past. Assuming a minimal network of pair-wise private …

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

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 …

We introduce and study the notion of fully linear probabilistically checkable proof systems. In
such a proof system, the verifier can make a small number of linear queries that apply jointly …

The concrete efficiency of secure computation has been the focus of many recent works. In
this work, we present concretely-efficient protocols for secure 3-party computation (3PC) …

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 investigate two questions in this paper: First, we ask to what extent" MPC friendly"
models are already supported by major Machine Learning frameworks such as TensorFlow …

Federated learning (FL) has gained widespread popularity in a variety of industries due to its
ability to locally train models on devices while preserving privacy. However, FL systems are …