Optimal utility-privacy trade-off with total variation distance as a privacy measure
File(s) RG_TIFS19.pdf (536.1 KB)
Accepted version
Author(s)
Rassouliy, Borzoo
Gunduz, Deniz
Type
Journal Article
Abstract
The total variation distance is proposed as a privacy measure in an information disclosure scenario when the goal is to reveal some information about available data in return of utility, while retaining the privacy of certain sensitive latent variables from the legitimate receiver. The total variation distance is introduced as a measure of privacy-leakage by showing that: i) it satis?es the post-processing and linkage inequalities, which makes it consistent with an intuitive notion of a privacy measure; ii) the optimal utility-privacy trade-off can be solved through a standard linear program when total variation distance is employed as the privacy measure; iii) it provides a bound on the privacy-leakage measured by mutual information, maximal leakage, or the improvement in an inference attack with a bounded cost function.
Date Issued
2019-03-07
Date Acceptance
2019-02-22
Citation
IEEE Transactions on Information Forensics and Security, 2019, 15, pp.594-603
ISSN
1556-6013
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Start Page
594
End Page
603
Journal / Book Title
IEEE Transactions on Information Forensics and Security
Volume
15
Copyright Statement
© 2019 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
Engineering & Physical Science Research Council (E
Identifier
https://ieeexplore.ieee.org/document/8662695
Grant Number
EP/N021738/1
Subjects
08 Information and Computing Sciences
09 Engineering
Strategic, Defence & Security Studies
Publication Status
Published
Date Publish Online
2019-03-07
