Non-Stochastic Hypothesis Testing with Application to Privacy Against Hypothesis-Testing Adversary
Farokhi, Farhad
2019-12-14
Conference Material
The 58th IEEE Conference on Decision and Control, Nice, France, December 11-13, 2019
n/a
In this paper, we consider privacy against hypothesis testing adversaries within a non-stochastic framework. We develop a theory of non-stochastic hypothesis testing by borrowing the notion of uncertain variables from non-stochastic information theory. We define tests as binary-valued mappings on uncertain variables and prove a fundamental bound on the best performance of tests in non-stochastic hypothesis testing. We use this bound to develop a measure of privacy. We then construct reporting policies with prescribed privacy and utility guarantees. The utility of a reporting policy is measured by the distance between the reported and original values. We illustrate the effects of using such privacy-preserving reporting polices on a publicly-available practical dataset of preferences and demographics of young individuals, aged between 15-30, with Slovakian nationality.
IEEE
Information Engineering and Theory
EP191718
Conference Paper - Refereed
English
Farokhi, Farhad. Non-Stochastic Hypothesis Testing with Application to Privacy Against Hypothesis-Testing Adversary. In: The 58th IEEE Conference on Decision and Control; December 11-13, 2019; Nice, France. IEEE; 2019. n/a. csiro:EP191718. http://hdl.handle.net/102.100.100/143637?index=1
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