23 citations · 47 across the 15 of their papers we have counts for
17 papers
Private Component-by-Component Learning
Dvir Karni, Eliad Tsfadia
We study differentially private learning problems in the realizable setting, where a hypothesis is specified by components. A direct iteration of private component learners is…
Computationally Efficient Replicable Learning of Parities and Applications
Moshe Noivirt, Jessica Sorrell, Eliad Tsfadia
We study the computational relationship between replicability (Impagliazzo et al. [STOC `22], Ghazi et al. [NeurIPS `21]) and other stability notions. Specifically, we focus on rep…
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…
Computationally Differentially Private Inner Product Protocols Imply Oblivious Transfer
Iftach Haitner, Noam Mazor, Jad Silbak +2
In distributed differential privacy, multiple parties collaborate to analyze their combined data while each party protects the confidentiality of its data from the others. Interest…
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…
On Differentially Private Subspace Estimation in a Distribution-Free Setting
Eliad Tsfadia
Private data analysis faces a significant challenge known as the curse of dimensionality, leading to increased costs. However, many datasets possess an inherent low-dimensional str…