An Information-Theoretic Approach to Inference Attacks on Random Data Perturbation and a Related Privacy Measure

• Published in: IEEE transactions on information theory Vol. 53; no. 8; pp. 2971 - 2977
• Main Author: Vora, P.L.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.08.2007; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Access control; Applied sciences; Asymptotic properties; Computer science; Cryptography; Cybersecurity; Data mining; data perturbation; Data privacy; Data security; Entropy; Exact sciences and technology; Gain measurement; Inference; Information security; Information theory; Information, signal and communications theory; information-theoretic security; Lower bounds; noisy channel; Perturbation; Privacy; Queries; Remuneration; Security; Signal and communications theory; statistical database security; Statistical inference; Statistics; Telecommunications and information theory; Private life; Lower bound; Data privacy; Query; Data processing; privacy; Entropy; data perturbation; Perturbation; Data mining; Information protection; Noisy channel; Database; information-theoretic security; Safety; statistical database security; Information theory
• ISSN: 0018-9448; 1557-9654
• DOI: 10.1109/TIT.2007.901183

Random data perturbation (RDP) has been in use for several years in statistical databases and public surveys as a means of providing privacy to individuals while collecting information on groups, and has recently gained popularity as a privacy technique in data mining. This correspondence provides an information-theoretic framework for all inference attacks on RDP. The framework is used to demonstrate the existence of a tight asymptotic lower bound on the number of queries required per bit of entropy for all inference attacks with zero asymptotic error and bounded average power in the query sequence. A privacy measure based on security against inference attacks is proposed.