In adaptive data analysis, a mechanism gets $ n $ iid samples from an unknown distribution
$\cal {D} $, andis required to provide accurate estimations to a sequence of adaptively …

In the last fifteen years, there has been a steady stream of works combining differential
privacy with various other cryptographic disciplines, particularly that of multi-party …

In this work we revisit the problem of using general-purpose MPC schemes to emulate the
trusted dataholder in differential privacy (DP), to achieve the same accuracy but without the …

Computational differential privacy (CDP) is a natural relaxation of the standard notion of
(statistical) differential privacy (SDP) proposed by Beimel, Nissim, and Omri (CRYPTO 2008) …

We consider protocols where users communicate with multiple servers to perform a
computation on the users' data. An adversary exerts semi-honest control over many of the …

We revisit the fundamental question of formally defining what constitutes a reconstruction
attack. While often clear from the context, our exploration reveals that a precise definition is …

Time-lock puzzles wrap a solution s inside a puzzle P in such a way that “solving” P to find s
requires significantly more time than generating the pair (s, P), even if the adversary has …

Given a multiset of $ n $ items from $\mathcal {D} $, the\emph {profile reconstruction}
problem is to estimate, for $ t= 0, 1,\dots, n $, the fraction $\vec {f}[t] $ of items in $\mathcal …

Given a multiset of $ n $ items from $\mathcal {D} $, the\emph {profile reconstruction}
problem is to estimate, for $ t= 0, 1,\dots, n $, the fraction $\vec {f}[t] $ of items in $\mathcal …

Two party differential privacy allows two parties who do not trust each other, to come
together and perform a joint analysis on their data whilst maintaining individual-level …