28 citations · 174 across the 52 of their papers we have counts for
7 papers · 1 filter
The Sparse Vector Technique, Revisited
Haim Kaplan, Yishay Mansour, Uri Stemmer
We revisit one of the most basic and widely applicable techniques in the literature of differential privacy - the sparse vector technique [Dwork et al., STOC 2009]. This simple alg…
Differentially Private Weighted Sampling
Edith Cohen, Ofir Geri, Tamas Sarlos +1
Common datasets have the form of elements with keys (e.g., transactions and products) and the goal is to perform analytics on the aggregated form of key and frequency pairs. A weig…
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…
Adversarially Robust Streaming Algorithms via Differential Privacy
Avinatan Hassidim, Haim Kaplan, Yishay Mansour +2
A streaming algorithm is said to be adversarially robust if its accuracy guarantees are maintained even when the data stream is chosen maliciously, by an adaptive adversary. We est…
Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity
Haim Kaplan, Yishay Mansour, Uri Stemmer +1
We present a differentially private learner for halfspaces over a finite grid in with sample complexity , which improves the…
How to Find a Point in the Convex Hull Privately
Haim Kaplan, Micha Sharir, Uri Stemmer
We study the question of how to compute a point in the convex hull of an input set of points in in a differentially private manner. This question, which is…