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 …

Secure multi-party computation (MPC) has evolved from a theoretical curiosity in the 1980s
to a tool for building real systems today. Over the past decade, MPC has been one of the …

Emerging smart contract systems over decentralized cryptocurrencies allow mutually
distrustful parties to transact safely without trusted third parties. In the event of contractual …

Privacy-preserving multi-party machine learning allows multiple organizations to perform
collaborative data analytics while guaranteeing the privacy of their individual datasets …

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 …

Secure computation enables mutually distrusting parties to jointly evaluate a function on
their private inputs without revealing anything but the function's output. Generic secure …

Data privacy is a key concern for smart contracts handling sensitive data. The existing work
zkay addresses this concern by allowing developers without cryptographic expertise to …

Secure multi-party computation (MPC) allows a group of mutually distrustful parties to
compute a joint function on their inputs without revealing any information beyond the result …

We design and develop ObliVM, a programming framework for secure computation. ObliVM
offers a domain specific language designed for compilation of programs into efficient …

Big data drive multidimensional convergence and profound innovations among industries
and provide novel ways of exploring the world. As they significantly create economic and …