REVIEW 3 cited by
Bayesian and Frequentist Semantics for Common Variations of Differential Privacy: Applications to the 2020 Census
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
abstract
The purpose of this paper is to guide interpretation of the semantic privacy guarantees for some of the major variations of differential privacy, which include pure, approximate, R\'enyi, zero-concentrated, and $f$ differential privacy. We interpret privacy-loss accounting parameters, frequentist semantics, and Bayesian semantics (including new results). The driving application is the interpretation of the confidentiality protections for the 2020 Census Public Law 94-171 Redistricting Data Summary File released August 12, 2021, which, for the first time, were produced with formal privacy guarantees.
Forward citations
Cited by 3 Pith papers
-
Interpreting Differential Privacy in Terms of Disclosure Risk
Shows that (epsilon,delta)-differential privacy bounds an adversary's posterior probability, posterior-to-prior ratio, and posterior-to-prior difference with high probability.
-
Towards Better Attribute Inference Vulnerability Measures
A precision-recall composite measure with an original-data baseline labels over 25% of attacks on weakly anonymized microdata as at risk that the prior accuracy-only approach called safe.
-
Setting the Privacy Budget in Differential Privacy by Bounding Adversaries' Odds of Learning Sensitive Information
Agencies can set the DP privacy budget ε by specifying an odds-risk profile on posterior-to-posterior disclosure odds ratios and taking ε_min = (1/2) log of the tightest allowed multiplicative increase.
Discussion (0). Sign in to comment.