4 papers
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…
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…
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…