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Privacy against a hypothesis testing adversary

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Title: Privacy against a hypothesis testing adversary
Authors: Li, Z
Oechtering, TJ
Gunduz, D
Item Type: Journal Article
Abstract: 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. Lastly, 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.
Issue Date: 20-Nov-2018
Date of Acceptance: 1-Nov-2018
URI: http://hdl.handle.net/10044/1/64613
DOI: https://dx.doi.org/10.1109/tifs.2018.2882343
ISSN: 1556-6013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Journal / Book Title: IEEE Transactions on Information Forensics and Security
Copyright Statement: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Engineering & Physical Science Research Council (E
Funder's Grant Number: EP/N021738/1
Keywords: 08 Information And Computing Sciences
09 Engineering
Strategic, Defence & Security Studies
Publication Status: Published online
Online Publication Date: 2018-11-20
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering



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