91 citations · 441 across the 36 of their papers we have counts for
4 papers · 1 filter
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…
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…
Redrawing the Boundaries on Purchasing Data from Privacy-Sensitive Individuals
Kobbi Nissim, Salil Vadhan, David Xiao
We prove new positive and negative results concerning the existence of truthful and individually rational mechanisms for purchasing private data from individuals with unbounded and…