28 citations · 174 across the 52 of their papers we have counts for
5 papers · 1 filter
The power of synergy in differential privacy: Combining a small curator with local randomizers
Amos Beimel, Aleksandra Korolova, Kobbi Nissim +2
Motivated by the desire to bridge the utility gap between local and trusted curator models of differential privacy for practical applications, we initiate the theoretical study of…
Privately Learning Thresholds: Closing the Exponential Gap
Haim Kaplan, Katrina Ligett, Yishay Mansour +2
We study the sample complexity of learning threshold functions under the constraint of differential privacy. It is assumed that each labeled example in the training data is the inf…
Locally Private k-Means Clustering
Uri Stemmer
We design a new algorithm for the Euclidean -means problem that operates in the local model of differential privacy. Unlike in the non-private literature, differentially private…
Private Center Points and Learning of Halfspaces
Amos Beimel, Shay Moran, Kobbi Nissim +1
We present a private learner for halfspaces over an arbitrary finite domain with sample complexity . The building block for t…
Differentially Private Learning of Geometric Concepts
Haim Kaplan, Yishay Mansour, Yossi Matias +1
We present differentially private efficient algorithms for learning union of polygons in the plane (which are not necessarily convex). Our algorithms achieve -PAC learning a…