Privately Publishable Per-instance Privacy
arXiv:2111.02281
Abstract
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.
arXiv commentTo appear at NeurIPS 2021
References in corpus (4)
- Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12
- Distribution of the largest eigenvalue for real Wishart and Gaussian random matrices and a simple approximation for the Tracy-Widom distribution
- Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM
- Individual Privacy Accounting via a Renyi Filter