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)
Rassouli, B
Gündüz, D
Type
Conference Paper
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 order to receive utility, while preserving the privacy of sensitive data from the legitimate receiver. The total variation distance is motivated as a measure of privacy-leakage by showing that: i) it satisfies 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 an arbitrary bounded cost function.
Date Issued
2019-01-17
Date Acceptance
2018-11-25
Citation
2018 IEEE Information Theory Workshop, ITW 2018, 2019
Publisher
IEEE
Journal / Book Title
2018 IEEE Information Theory Workshop, ITW 2018
Copyright Statement
© 2018 IEEE Information Theory Workshop, ITW 2018. All rights reserved. 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
Commission of the European Communities
Grant Number
EP/N021738/1
677854
Source
2018 IEEE Information Theory Workshop (ITW)
Subjects
Science & Technology
Technology
Computer Science, Theory & Methods
Computer Science
Privacy
total variation distance
utility-privacy trade-off
cs.IT
cs.IT
math.IT
Publication Status
Published
Start Date
2018-11-25
Finish Date
2018-11-29
Coverage Spatial
Guangzhou, China
Date Publish Online
2019-01-17