Machine learning is widely used in practice to produce predictive models for applications
such as image processing, speech and text recognition. These models are more accurate …

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 …

Recently, there has been a wealth of effort devoted to the design of secure protocols for
machine learning tasks. Much of this is aimed at enabling secure prediction from highly …

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

Abstract Machine learning models trained on sensitive real-world data promise
improvements to everything from medical screening to disease outbreak discovery. In many …

We propose privacy-preserving protocols for computing linear regression models, in the
setting where the training dataset is vertically distributed among several parties. Our main …

Secure Multi-Party Computation (MPC) allows mutually distrusting parties to run joint
computations without revealing private data. Current MPC algorithms scale poorly with data …

A zk-SNARK is a powerful cryptographic primitive that provides a succinct and efficiently
checkable argument that the prover has a witness to a public NP statement, without …

We present a highly-scalable secure computation of graph algorithms, which hides all
information about the topology of the graph or other input values associated with nodes or …