Current solutions for privacy-preserving data sharing among multiple parties either depend
on a centralized authority that must be trusted and provides only weakest-link security (eg …

We present the first efficient (ie, polylogarithmic overhead) method for securely and privately
processing large data sets over multiple parties with parallel, distributed algorithms. More …

Byzantine agreement (BA), the task of n parties to agree on one of their input bits in the face
of malicious agents, is a powerful primitive that lies at the core of a vast range of distributed …

Secure multi-party computation (MPC) allows multiple parties to perform secure joint
computations on their private inputs. Today, applications for MPC are growing with …

Tor is a tool for Internet privacy with millions of daily users. The Tor system benefits in many
ways from information gathered about the operation of its network. Measurements guide …

Secure multi-party computation (MPC) allows multiple parties to compute a known function
over inputs held by each party, without any party having to reveal its private input …

Multi-party fair exchange (MFE) and fair secure multi-party computation (fair SMPC) are
under-studied fields of research, with practical importance. We examine MFE scenarios …

In this work, we initiate the study of bottleneck complexity as a new communication efficiency
measure for secure multiparty computation (MPC). Roughly, the bottleneck complexity of an …

In secure multi-party shuffling, multiple parties, each holding an input, want to agree on a
random permutation of their inputs while keeping the permutation secret. This problem is …

Multi-party fair exchange (MFE) and fair secure multi-party computation (fair SMPC) are
under-studied fields of research, with practical importance. In particular, we consider MFE …