  We initiate the study of differentially private hypothesis testing in the
local-model, under both the standard (symmetric) randomized-response mechanism
(Warner, 1965, Kasiviswanathan et al, 2008) and the newer (non-symmetric)
mechanisms (Bassily and Smith, 2015, Bassily et al, 2017). First, we study the
general framework of mapping each user's type into a signal and show that the
problem of finding the maximum-likelihood distribution over the signals is
feasible. Then we discuss the randomized-response mechanism and show that, in
essence, it maps the null- and alternative-hypotheses onto new sets, an affine
translation of the original sets. We then give sample complexity bounds for
identity and independence testing under randomized-response. We then move to
the newer non-symmetric mechanisms and show that there too the problem of
finding the maximum-likelihood distribution is feasible. Under the mechanism of
Bassily et al (2007) we give identity and independence testers with better
sample complexity than the testers in the symmetric case, and we also propose a
$\chi^2$-based identity tester which we investigate empirically.
