8 citations · 24 across the 11 of their papers we have counts for
6 papers · 1 filter
Tighter Bounds on Multi-Party Coin Flipping via Augmented Weak Martingales and Differentially Private Sampling
Amos Beimel, Iftach Haitner, Nikolaos Makriyannis +1
In his seminal work, Cleve [STOC '86] has proved that any -round coin-flipping protocol can be efficiently biased by . This lower bound was met for the two-party case by…
On the Round Complexity of the Shuffle Model
Amos Beimel, Iftach Haitner, Kobbi Nissim +1
The shuffle model of differential privacy was proposed as a viable model for performing distributed differentially private computations. Informally, the model consists of an untrus…
Exploring Differential Obliviousness
Amos Beimel, Kobbi Nissim, Mohammad Zaheri
In a recent paper Chan et al. [SODA '19] proposed a relaxation of the notion of (full) memory obliviousness, which was introduced by Goldreich and Ostrovsky [J. ACM '96] and extens…
Characterizing the Sample Complexity of Private Learners
Amos Beimel, Kobbi Nissim, Uri Stemmer
In 2008, Kasiviswanathan et al. defined private learning as a combination of PAC learning and differential privacy. Informally, a private learner is applied to a collection of labe…
Distributed Private Data Analysis: On Simultaneously Solving How and What
Amos Beimel, Kobbi Nissim, Eran Omri
We examine the combination of two directions in the field of privacy concerning computations over distributed private inputs - secure function evaluation (SFE) and differential pri…
Secure Multiparty Computation with Partial Fairness
Amos Beimel, Eran Omri, Ilan Orlov
A protocol for computing a functionality is secure if an adversary in this protocol cannot cause more harm than in an ideal computation where parties give their inputs to a trusted…