28 citations · 174 across the 52 of their papers we have counts for
9 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…
A Note on Sanitizing Streams with Differential Privacy
Haim Kaplan, Uri Stemmer
The literature on data sanitization aims to design algorithms that take an input dataset and produce a privacy-preserving version of it, that captures some of its statistical prope…
Dynamic Algorithms Against an Adaptive Adversary: Generic Constructions and Lower Bounds
Amos Beimel, Haim Kaplan, Yishay Mansour +3
A dynamic algorithm against an adaptive adversary is required to be correct when the adversary chooses the next update after seeing the previous outputs of the algorithm. We obtain…
Differentially Private Approximate Quantiles
Haim Kaplan, Shachar Schnapp, Uri Stemmer
In this work we study the problem of differentially private (DP) quantiles, in which given dataset and quantiles , we want to output quantile estim…
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 Sample Complexity of Privately Learning Axis-Aligned Rectangles
Menachem Sadigurschi, Uri Stemmer
We revisit the fundamental problem of learning Axis-Aligned-Rectangles over a finite grid with differential privacy. Existing results show that the sam…