On perfect privacy

File Description SizeFormat 
RG_ISIT18.pdfAccepted version321.3 kBAdobe PDFView/Open
Title: On perfect privacy
Authors: Rassouli, B
Gunduz, D
Item Type: Conference Paper
Abstract: For a pair of (dependent) random variables (X, Y), the following problem is addressed: What is the maximum information that can be revealed about Y, while disclosing no information about X? Assuming that a Markov kernel maps Y to the revealed information U, it is shown that the maximum mutual information between Y and U, i.e., I(Y; U), can be obtained as the solution of a standard linear program, when X and U are required to be independent, called perfect privacy. The resulting quantity is shown to be greater than or equal to the non-private information about X carried by Y. For jointly Gaussian (X, Y), it is shown that perfect privacy is not possible if the kernel is applied to only Y; whereas perfect privacy can be achieved if the mapping is from both X and Y; that is, if the private variables can also be observed at the encoder. Finally, it is shown that when Y is not a deterministic function of X, perfect privacy is always feasible when the mapping has access to both X and Y.1
Issue Date: 16-Aug-2018
Date of Acceptance: 1-Aug-2018
URI: http://hdl.handle.net/10044/1/62569
DOI: https://dx.doi.org/10.1109/ISIT.2018.8437481
ISBN: 9781538647806
ISSN: 2157-8117
Publisher: IEEE
Start Page: 2551
End Page: 2555
Journal / Book Title: IEEE International Symposium on Information Theory - Proceedings
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
Commission of the European Communities
Funder's Grant Number: EP/N021738/1
Conference Name: 2018 IEEE International Symposium on Information Theory (ISIT)
Publication Status: Published
Start Date: 2018-06-17
Finish Date: 2018-06-22
Conference Place: ail, CO, USA
Online Publication Date: 2018-08-16
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commons