Utility-Privacy Tradeoff in Databases: An Information-theoretic Approach

Abstract

Ensuring the usefulness of electronic data sources while providing necessary privacy guarantees is an important unsolved problem. This problem drives the need for an analytical framework that can quantify the safety of personally identifiable information (privacy) while still providing a quantifable benefit (utility) to multiple legitimate information consumers. This paper presents an information-theoretic framework that promises an analytical model guaranteeing tight bounds of how much utility is possible for a given level of privacy and vice-versa. Specific contributions include: i) stochastic data models for both categorical and numerical data; ii) utility-privacy tradeoff regions and the encoding (sanization) schemes achieving them for both classes and their practical relevance; and iii) modeling of prior knowledge at the user and/or data source and optimal encoding schemes for both cases.

Revised following submission to the IEEE Transactions on Information Forensics and Security: Special Issue on Privacy and Trust Management in Cloud and Distributed Systems; updated with missing references

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• Competitive Privacy in the Smart Grid: An Information-theoretic Approach
• On the relation between Differential Privacy and Quantitative Information Flow
• On Privacy-Preserving Histograms
• Discriminatory Lossy Source Coding: Side Information Privacy

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