Fundamental limits of perfect privacy
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Medard_Fundamental limits.pdf
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189.77 KB
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Author(s)
Calmon, Flavio du Pin
Makhdoumi Kakhaki, Ali
Medard, Muriel
Date Issued
October 2015
Journal
2015 IEEE International Symposium on Information Theory (ISIT)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Calmon, Flavio P., et al. "Fundamental Limits of Perfect Privacy." 2015 IEEE International Symposium on Information Theory (ISIT), 14-19 June 2015, Hong Kong, China, IEEE, 2015, pp. 1796–800.
Version
Author's final manuscript
Abstract
We investigate the problem of intentionally disclosing information about a set of measurement points X (useful information), while guaranteeing that little or no information is revealed about a private variable S (private information). Given that S and X are drawn from a finite set with joint distribution pS,X, we prove that a non-trivial amount of useful information can be disclosed while not disclosing any private information if and only if the smallest principal inertia component of the joint distribution of S and X is 0. This fundamental result characterizes when useful information can be privately disclosed for any privacy metric based on statistical dependence. We derive sharp bounds for the tradeoff between disclosure of useful and private information, and provide explicit constructions of privacy-assuring mappings that achieve these bounds.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/ISIT.2015.7282765
