23 citations · 47 across the 15 of their papers we have counts for
6 papers · 1 filter
Differentially-Private Clustering of Easy Instances
Edith Cohen, Haim Kaplan, Yishay Mansour +2
Clustering is a fundamental problem in data analysis. In differentially private clustering, the goal is to identify cluster centers without disclosing information on individual…
FriendlyCore: Practical Differentially Private Aggregation
Eliad Tsfadia, Edith Cohen, Haim Kaplan +2
Differentially private algorithms for common metric aggregation tasks, such as clustering or averaging, often have limited practicality due to their complexity or to the large numb…
On the Complexity of Two-Party Differential Privacy
Iftach Haitner, Noam Mazor, Jad Silbak +1
In distributed differential privacy, the parties perform analysis over their joint data while preserving the privacy for both datasets. Interestingly, for a few fundamental two-par…
An Almost-Optimally Fair Three-Party Coin-Flipping Protocol
Iftach Haitner, Eliad Tsfadia
In a multiparty fair coin-flipping protocol, the parties output a common (close to) unbiased bit, even when some corrupted parties try to bias the output. Cleve [STOC 1986] has sho…
A Tight Parallel Repetition Theorem for Partially Simulatable Interactive Arguments via Smooth KL-Divergence
Itay Berman, Iftach Haitner, Eliad Tsfadia
Hardness amplification is a central problem in the study of interactive protocols. While ``natural'' parallel repetition transformation is known to reduce the soundness error of so…
Fair Coin Flipping: Tighter Analysis and the Many-Party Case
Niv Buchbinder, Iftach Haitner, Nissan Levi +1
In a multi-party fair coin-flipping protocol, the parties output a common (close to) unbiased bit, even when some adversarial parties try to bias the output. In this work we focus…