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Privacy Against a Hypothesis Testing Adversary
KTH, School of Electrical Engineering and Computer Science (EECS).
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0002-0036-9049
Imperial Coll London, Dept Elect & Elect Engn, London SW7 2BT, England..
2019 (English)In: IEEE Transactions on Information Forensics and Security, ISSN 1556-6013, E-ISSN 1556-6021, Vol. 14, no 6, p. 1567-1581Article in journal (Refereed) Published
Abstract [en]

Privacy against an adversary (AD) that tries to detect the underlying privacy-sensitive data distribution is studied. The original data sequence is assumed to come from one of the two known distributions, and the privacy leakage is measured by the probability of error of the binary hypothesis test carried out by the AD. A management unit (MU) is allowed to manipulate the original data sequence in an online fashion while satisfying an average distortion constraint. The goal of the MU is to maximize the minimal type II probability of error subject to a constraint on the type I probability of error assuming an adversarial Neyman-Pearson test, or to maximize the minimal error probability assuming an adversarial Bayesian test. The asymptotic exponents of the maximum minimal type II probability of error and the maximum minimal error probability are shown to be characterized by a Kullback-Leibler divergence rate and a Chernoff information rate, respectively. Privacy performances of particular management policies, the memoryless hypothesis-aware policy and the hypothesis-unaware policy with memory, are compared. The proposed formulation can also model adversarial example generation with minimal data manipulation to fool classifiers. At last, the results are applied to a smart meter privacy problem, where the user's energy consumption is manipulated by adaptively using a renewable energy source in order to hide user's activity from the energy provider.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2019. Vol. 14, no 6, p. 1567-1581
Keywords [en]
Neyman-Pearson test, Bayesian test, information theory, large deviations, privacy-enhancing technology
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-247798DOI: 10.1109/TIFS.2018.2882343ISI: 000460659400001Scopus ID: 2-s2.0-85058482049OAI: oai:DiVA.org:kth-247798DiVA, id: diva2:1301178
Note

QC 20190401

Available from: 2019-04-01 Created: 2019-04-01 Last updated: 2019-04-01Bibliographically approved

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Oechtering, Tobias J.

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