8 citations · 24 across the 11 of their papers we have counts for
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cs.LG2014★ 4 cited
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…
cs.LG2014
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…
cs.CR2014★ 2 cited
Characterizing the Sample Complexity of Private Learners
Amos Beimel, Kobbi Nissim, Uri Stemmer
In 2008, Kasiviswanathan et al. defined private learning as a combination of PAC learning and differential privacy. Informally, a private learner is applied to a collection of labe…