  We study statistical risk minimization problems under a privacy model in
which the data is kept confidential even from the learner. In this local
privacy framework, we establish sharp upper and lower bounds on the convergence
rates of statistical estimation procedures. As a consequence, we exhibit a
precise tradeoff between the amount of privacy the data preserves and the
utility, as measured by convergence rate, of any statistical estimator or
learning procedure.
