Timezone: »

 
Poster
Privately Publishable Per-instance Privacy
Rachel Redberg · Yu-Xiang Wang

Thu Dec 09 08:30 AM -- 10:00 AM (PST) @
in Poster Session 6 »

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.

Keywords: Privacy

Author Information

Rachel Redberg (UC Santa Barbara)
Yu-Xiang Wang (UC Santa Barbara)

More from the Same Authors