Imperial College London
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ProfessorDenizGunduz

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor in Information Processing
 
 
 
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Contact

 

+44 (0)20 7594 6218d.gunduz Website

 
 
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Assistant

 

Ms Joan O'Brien +44 (0)20 7594 6316

 
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Location

 

1016Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Rassouli:2018:10.1109/ISIT.2018.8437481,
author = {Rassouli, B and Gunduz, D},
doi = {10.1109/ISIT.2018.8437481},
pages = {2551--2555},
publisher = {IEEE},
title = {On perfect privacy},
url = {http://dx.doi.org/10.1109/ISIT.2018.8437481},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - 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
AU  - Rassouli,B
AU  - Gunduz,D
DO  - 10.1109/ISIT.2018.8437481
EP  - 2555
PB  - IEEE
PY  - 2018///
SN  - 2157-8117
SP  - 2551
TI  - On perfect privacy
UR  - http://dx.doi.org/10.1109/ISIT.2018.8437481
UR  - http://hdl.handle.net/10044/1/62569
ER  -
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