  We consider how to privately share the personalized privacy losses incurred
by objective perturbation, using per-instance differential privacy (pDP).
Standard differential privacy (DP) gives us a worst-case bound that might be
orders of magnitude larger than the privacy loss to a particular individual
relative to a fixed dataset. The pDP framework provides a more fine-grained
analysis of the privacy guarantee to a target individual, but the per-instance
privacy loss itself might be a function of sensitive data. In this paper, we
analyze the per-instance privacy loss of releasing a private empirical risk
minimizer learned via objective perturbation, and propose a group of methods to
privately and accurately publish the pDP losses at little to no additional
privacy cost.
