39 citations · 48 across the 12 of their papers we have counts for
7 papers · 1 filter
Private Truly-Everlasting Robust-Prediction
Uri Stemmer
Private Everlasting Prediction (PEP), recently introduced by Naor et al. [2023], is a model for differentially private learning in which the learner never publicly releases a hypot…
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…
Concurrent Shuffle Differential Privacy Under Continual Observation
Jay Tenenbaum, Haim Kaplan, Yishay Mansour +1
We introduce the concurrent shuffle model of differential privacy. In this model we have multiple concurrent shufflers permuting messages from different, possibly overlapping, batc…
Differentially-Private Clustering of Easy Instances
Edith Cohen, Haim Kaplan, Yishay Mansour +2
Clustering is a fundamental problem in data analysis. In differentially private clustering, the goal is to identify cluster centers without disclosing information on individual…
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…