  We study goodness-of-fit and independence testing of discrete distributions
in a setting where samples are distributed across multiple users. The users
wish to preserve the privacy of their data while enabling a central server to
perform the tests. Under the notion of local differential privacy, we propose
simple, sample-optimal, and communication-efficient protocols for these two
questions in the noninteractive setting, where in addition users may or may not
share a common random seed. In particular, we show that the availability of
shared (public) randomness greatly reduces the sample complexity. Underlying
our public-coin protocols are privacy-preserving mappings which, when applied
to the samples, minimally contract the distance between their respective
probability distributions.
