51 citations · 160 across the 19 of their papers we have counts for
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cs.DS2019
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…
cs.DS2019
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians
Gautam Kamath, Or Sheffet, Vikrant Singhal +1
Learning the parameters of Gaussian mixture models is a fundamental and widely studied problem with numerous applications. In this work, we give new algorithms for learning the par…