Information-theoretic metrics for Local Differential Privacy protocols

Abstract

Local Differential Privacy (LDP) protocols allow an aggregator to obtain population statistics about sensitive data of a userbase, while protecting the privacy of the individual users. To understand the tradeoff between aggregator utility and user privacy, we introduce new information-theoretic metrics for utility and privacy. Contrary to other LDP metrics, these metrics highlight the fact that the users and the aggregator are interested in fundamentally different domains of information. We show how our metrics relate to $\epsilon$-LDP, the \emph{de facto} standard privacy metric, giving an information-theoretic interpretation to the latter. Furthermore, we use our metrics to quantitatively study the privacy-utility tradeoff for a number of popular protocols.

References in corpus (9)

• Renyi Differential Privacy
• Extremal Mechanisms for Local Differential Privacy
• Differential Privacy as a Mutual Information Constraint
• Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12
• Differential Privacy: on the trade-off between Utility and Information Leakage
• Mutual Information Optimally Local Private Discrete Distribution Estimation
• Cruising The Simplex: Hamiltonian Monte Carlo and the Dirichlet Distribution
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• Distributed and Secure ML with Self-tallying Multi-party Aggregation

Cited by in corpus (6)

• Privacy-Preserving Distributed Optimization via Subspace Perturbation: A General Framework
• Data Sanitisation Protocols for the Privacy Funnel with Differential Privacy Guarantees
• The Privacy Funnel from the viewpoint of Local Differential Privacy
• Privacy-Preserving Distributed Processing: Metrics, Bounds, and Algorithms
• Detecting Anomalous LAN Activities under Differential Privacy
• Improving Frequency Estimation under Local Differential Privacy