Melek Onen | Searcharxiv

arXiv2023

Information-Theoretic Approaches to Differential Privacy

This tutorial studies relationships between differential privacy and various information-theoretic measures by using several selective articles. In particular, we present how these connections can provide new interpretations for the privacy guarantee in systems that deploy differential privacy in an information-theoretic framework. To this end, the tutorial provides an extensive summary on the existing literature that makes use of information-theoretic measures and tools such as mutual information, min-entropy, Kullback-Leibler divergence and rate-distortion function for quantification and characterization of differential privacy in various settings.

cs.IT↗

arXiv2022

Adversarial Classification under Gaussian Mechanism: Calibrating the Attack to Sensitivity

This work studies anomaly detection under differential privacy (DP) with Gaussian perturbation using both statistical and information-theoretic tools. In our setting, the adversary aims to modify the content of a statistical dataset by inserting additional data without being detected by using the DP guarantee to her own benefit. To this end, we characterize information-theoretic and statistical thresholds for the first and second-order statistics of the adversary's attack, which balances the privacy budget and the impact of the attack in order to remain undetected. Additionally, we introduce a new privacy metric based on Chernoff information for classifying adversaries under differential privacy as a stronger alternative to $(ε, δ)-$ and Kullback-Leibler DP for the Gaussian mechanism. Analytical results are supported by numerical evaluations.

cs.IT↗