39 citations · 48 across the 7 of their papers we have counts for
8 papers
Properties of Effective Information Anonymity Regulations
Aloni Cohen, Micah Altman, Francesca Falzon +2
A firm seeks to analyze a dataset and to release the results. The dataset contains information about individual people, and the firm is subject to some regulation that forbids the…
Private Everlasting Prediction
Moni Naor, Kobbi Nissim, Uri Stemmer +1
A private learner is trained on a sample of labeled points and generates a hypothesis that can be used for predicting the labels of newly sampled points while protecting the privac…
On Differentially Private Online Predictions
Haim Kaplan, Yishay Mansour, Shay Moran +2
In this work we introduce an interactive variant of joint differential privacy towards handling online processes in which existing privacy definitions seem too restrictive. We stud…
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…
Private Incremental Regression
Shiva Prasad Kasiviswanathan, Kobbi Nissim, Hongxia Jin
Data is continuously generated by modern data sources, and a recent challenge in machine learning has been to develop techniques that perform well in an incremental (streaming) set…
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…