Per-instance Differential Privacy

Understand

We consider a refinement of differential privacy --- per instance differential privacy (pDP), which captures the privacy of a specific individual with respect to a fixed data set.

• We show that this is a strict generalization of the standard DP and inherits all its desirable properties, e.g., composition, invariance to side information and closedness to postprocessing, except that they all hold for every instance separately.
• When the data is drawn from a distribution, we show that per-instance DP implies generalization.
• Moreover, we provide explicit calculations of the per-instance DP for the output perturbation on a class of smooth learning problems.