4 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…
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…
Differentially Private Quasi-Concave Optimization: Bypassing the Lower Bound and Application to Geometric Problems
Kobbi Nissim, Eliad Tsfadia, Chao Yan
We study the sample complexity of differentially private optimization of quasi-concave functions. For a fixed input domain , Cohen et al. (STOC 2023) proved that any g…
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…