28 citations · 174 across the 52 of their papers we have counts for
7 papers · 1 filter
Hot PATE: Private Aggregation of Distributions for Diverse Task
Edith Cohen, Benjamin Cohen-Wang, Xin Lyu +3
The Private Aggregation of Teacher Ensembles (PATE) framework enables privacy-preserving machine learning by aggregating responses from disjoint subsets of sensitive data. Adaptati…
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…
Adaptive Data Analysis in a Balanced Adversarial Model
Kobbi Nissim, Uri Stemmer, Eliad Tsfadia
In adaptive data analysis, a mechanism gets i.i.d. samples from an unknown distribution , and is required to provide accurate estimations to a sequence of adaptively chosen…
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…
On Differential Privacy and Adaptive Data Analysis with Bounded Space
Itai Dinur, Uri Stemmer, David P. Woodruff +1
We study the space complexity of the two related fields of differential privacy and adaptive data analysis. Specifically, (1) Under standard cryptographic assumptions, we show that…
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…