  The local model for differential privacy is emerging as the reference model
for practical applications collecting and sharing sensitive information while
satisfying strong privacy guarantees. In the local model, there is no trusted
entity which is allowed to have each individual's raw data as is assumed in the
traditional curator model for differential privacy. So, individuals' data are
usually perturbed before sharing them.
  We explore the design of private hypothesis tests in the local model, where
each data entry is perturbed to ensure the privacy of each participant.
Specifically, we analyze locally private chi-square tests for goodness of fit
and independence testing, which have been studied in the traditional, curator
model for differential privacy.
