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 …

The importance of data is increasing along its inflation in our world today. In the big data era,
data is becoming a main source for innovation, knowledge and insight, as well as a …

Smart contracts are applications that execute on blockchains. Today they manage billions of
dollars in value and motivate visionary plans for pervasive blockchain deployment. While …

We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function
evaluation (SFE) which enables two parties to jointly compute a function without disclosing …

The digital twin is a rather new industrial control and automation systems concept. While the
approach so far has gained interest mainly due to capabilities to make advanced …

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 …

Many systems run rich analytics on sensitive data in the cloud, but are prone to data
breaches. Hardware enclaves promise data confidentiality and secure execution of arbitrary …

Federated learning is a promising learning paradigm that allows collaborative training of
models across multiple data owners without sharing their raw datasets. To enhance privacy …

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 …