26 citations · 63 across the 28 of their papers we have counts for
6 papers · 1 filter
Machine Unlearning as Private Retroactive Algorithms
Haim Kaplan, Refael Kohen, Yishay Mansour +2
Machine unlearning typically aims to emulate retraining from scratch: upon a deletion request, the unlearning algorithm should produce an outcome that would have been obtained had…
Is Randomness Necessary for Adaptive Data Analysis?
Edith Cohen, Haim Kaplan, Yishay Mansour +2
The Adaptive Data Analysis (ADA) problem formalizes the challenge of preventing false discovery and overfitting when a dataset is repeatedly reused. Formally, our input is a datase…
Bayesian Perspective on Memorization and Reconstruction
Haim Kaplan, Yishay Mansour, Kobbi Nissim +1
We introduce a new Bayesian perspective on the concept of data reconstruction, and leverage this viewpoint to propose a new security definition that, in certain settings, provably…
Data Reconstruction: When You See It and When You Don't
Edith Cohen, Haim Kaplan, Yishay Mansour +4
We revisit the fundamental question of formally defining what constitutes a reconstruction attack. While often clear from the context, our exploration reveals that a precise defini…
Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries
Edith Cohen, Xin Lyu, Jelani Nelson +2
One of the most basic problems for studying the "price of privacy over time" is the so called private counter problem, introduced by Dwork et al. (2010) and Chan et al. (2010). In…
On the Round Complexity of the Shuffle Model
Amos Beimel, Iftach Haitner, Kobbi Nissim +1
The shuffle model of differential privacy was proposed as a viable model for performing distributed differentially private computations. Informally, the model consists of an untrus…