69 citations · 116 across the 31 of their papers we have counts for
6 papers · 1 filter
Contraction of -Divergence and Its Applications to Privacy
Shahab Asoodeh, Mario Diaz, Flavio P. Calmon
We investigate the contraction coefficients derived from strong data processing inequalities for the -divergence. By generalizing the celebrated Dobrushin's coefficient from t…
Bottleneck Problems: Information and Estimation-Theoretic View
Shahab Asoodeh, Flavio Calmon
Information bottleneck (IB) and privacy funnel (PF) are two closely related optimization problems which have found applications in machine learning, design of privacy algorithms, c…
Three Variants of Differential Privacy: Lossless Conversion and Applications
Shahab Asoodeh, Jiachun Liao, Flavio P. Calmon +2
We consider three different variants of differential privacy (DP), namely approximate DP, Rényi DP (RDP), and hypothesis test DP. In the first part, we develop a machinery for opti…
Differentially Private Mechanisms for Count Queries
Parastoo Sadeghi, Shahab Asoodeh, Flavio du Pin Calmon
In this paper, we consider the problem of responding to a count query (or any other integer-valued queries) evaluated on a dataset containing sensitive attributes. To protect the p…
Privacy Amplification of Iterative Algorithms via Contraction Coefficients
Shahab Asoodeh, Mario Diaz, Flavio P. Calmon
We investigate the framework of privacy amplification by iteration, recently proposed by Feldman et al., from an information-theoretic lens. We demonstrate that differential privac…
A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via -Divergences
Shahab Asoodeh, Jiachun Liao, Flavio P. Calmon +2
We derive the optimal differential privacy (DP) parameters of a mechanism that satisfies a given level of Rényi differential privacy (RDP). Our result is based on the joint range o…