Technical Program

Paper Detail

Paper Title Information-Theoretic Privacy Watchdogs
Paper IdentifierMO4.R3.4
Authors Hsiang Hsu, Harvard University, United States; Shahab Asoodeh, University of Chicago, United States; Flavio P. Calmon, Harvard University, United States
Session Privacy
Location Monge, Level 3
Session Time Monday, 08 July, 16:40 - 18:00
Presentation Time Monday, 08 July, 17:40 - 18:00
Manuscript  Click here to download the manuscript
Abstract Given a dataset comprised of individual-level data, we consider the problem of identifying samples that may be disclosed without incurring a privacy risk. We address this challenge by designing a mapping that assigns a "privacy-risk score" to each sample. This mapping, called the privacy watchdog, is based on a sample-wise information leakage measure called the information density, deemed here lift privacy. We show that lift privacy is closely related to well-known information-theoretic privacy metrics. Moreover, we demonstrate how the privacy watchdog can be implemented using the Donsker-Varadhan representation of KL-divergence. Finally, we illustrate this approach on a real-world dataset.