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 …

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) …

Secure Multi-Party Computation (MPC) with small number of parties is an interesting area of
research, primarily due to its ability to model most real-life MPC applications and the …

Multi-Party Computation (MPC) is a technique enabling data from several sources to be
used in a secure computation revealing only the result while protecting the original data …

Multi-Party Computation (MPC) is an important technique used to enable computation over
confidential data from several sources. The public cloud provides a unique opportunity to …

Machine Learning (ML), addresses a multitude of complex issues in multiple disciplines,
including social sciences, finance, and medical research. ML models require substantial …

One of the great advances in computation over the last 20 years is the availability,
connectivity, and growth of datacenters. These advances in the datacenter have motivated …

Big data utilizes large amounts of processing resources requiring either greater efficiency or
more selectivity. The collection and managing of such large pools of data also introduces …

In this work we propose two prediction protocols for a random forest model. The first takes a
traditional approach and requires the trees in the forest to be complete in order to hide …

Aiming at the problem that the time stamp mechanism in the Kerberos protocol cannot
effectively resist replay attacks, this paper proposes an improved kerberos protocol based …