8 citations · 24 across the 11 of their papers we have counts for
4 papers · 1 filter
Closure Properties for Private Classification and Online Prediction
Noga Alon, Amos Beimel, Shay Moran +1
Let~$\cH$ be a class of boolean functions and consider a {\it composed class} $\cH'$ that is derived from~$\cH$ using some arbitrary aggregation rule (for example, $\cH'$ may be th…
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…
Private Learning and Sanitization: Pure vs. Approximate Differential Privacy
Amos Beimel, Kobbi Nissim, Uri Stemmer
We compare the sample complexity of private learning [Kasiviswanathan et al. 2008] and sanitization~[Blum et al. 2008] under pure -differential privacy [Dwork et al. TCC 2006] a…
Learning Privately with Labeled and Unlabeled Examples
Amos Beimel, Kobbi Nissim, Uri Stemmer
A private learner is an algorithm that given a sample of labeled individual examples outputs a generalizing hypothesis while preserving the privacy of each individual. In 2008, Kas…