Journal article

Development and Analysis of Deterministic Privacy-Preserving Policies Using Non- Stochastic Information Theory

F Farokhi

IEEE Transactions on Information Forensics and Security | IEEE | Published : 2019

Abstract

A deterministic privacy metric using non-stochastic information theory is developed. Particularly, maximin information is used to construct a measure of information leakage, which is inversely proportional to the measure of privacy. Anyone can submit a query to a trusted agent with access to a non-stochastic uncertain private dataset. Optimal deterministic privacy-preserving policies for responding to the submitted query are computed by maximizing the measure of privacy subject to a constraint on the worst-case quality of the response (i.e., the worst-case difference between the response by the agent and the output of the query computed on the private dataset). The optimal privacy-preserving..

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Grants

Funding Acknowledgements

This work was supported in part by the University of Melbourne through the McKenzie Fellowship and in part by the Victorian State Government through the VESKI Victoria Fellowship. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Aris Gkoulalas Divanis.