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

Recently, a number of backdoor attacks against Federated Learning (FL) have been
proposed. In such attacks, an adversary injects poisoned model updates into the federated …

We consider how to perform privacy-preserving analyses on private data from different data
providers and containing personal information of many different individuals. We combine …

We present a novel three-party sorting protocol secure against passive adversaries in the
honest majority setting. The protocol can be easily combined with other secure protocols …

We propose GraphOS, a system that allows a client that owns a graph database to
outsource it to an untrusted server for storage and querying. It relies on doubly-oblivious …

Abstract Distributed Oblivious Random Access Memory (DORAM) is a secure multiparty
protocol that allows a group of participants holding a secret-shared array to read and write to …

In this paper, we propose and present secure multiparty computation (SMC) protocols for
single-source shortest distance (SSSD) and all-pairs shortest distance (APSD) in sparse and …

Parallel computation is an important aspect of multi-party computation, not only in terms of
improving efficiency, but also in terms of providing privacy for computation involving …

Secure multi-party computation permits evaluation of any desired functionality on private
data without disclosing the data to the participants. It is gaining its popularity due to …

We propose a secure multi-party computation (MPC) protocol that constructs a secret-shared
decision tree for a given secret-shared dataset. The previous MPC-based decision tree …