70 citations · 293 across the 70 of their papers we have counts for
10 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…
On Perfect Obfuscation: Local Information Geometry Analysis
Behrooz Razeghi, Flavio. P. Calmon, Deniz Gunduz +1
We consider the problem of privacy-preserving data release for a specific utility task under perfect obfuscation constraint. We establish the necessary and sufficient condition to…
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…
CPR: Classifier-Projection Regularization for Continual Learning
Sungmin Cha, Hsiang Hsu, Taebaek Hwang +2
We propose a general, yet simple patch that can be applied to existing regularization-based continual learning methods called classifier-projection regularization (CPR). Inspired b…
Privacy-Preserving Image Sharing via Sparsifying Layers on Convolutional Groups
Sohrab Ferdowsi, Behrooz Razeghi, Taras Holotyak +2
We propose a practical framework to address the problem of privacy-aware image sharing in large-scale setups. We argue that, while compactness is always desired at scale, this need…
To Split or Not to Split: The Impact of Disparate Treatment in Classification
Hao Wang, Hsiang Hsu, Mario Diaz +1
Disparate treatment occurs when a machine learning model yields different decisions for individuals based on a sensitive attribute (e.g., age, sex). In domains where prediction acc…
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…