39 citations · 43 across the 3 of their papers we have counts for
3 papers
cs.DS2017★ 39 cited
Practical Locally Private Heavy Hitters
Raef Bassily, Kobbi Nissim, Uri Stemmer +1
We present new practical local differentially private heavy hitters algorithms achieving optimal or near-optimal worst-case error and running time -- TreeHist and Bitstogram. In bo…
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…