  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.
