Property Testing for Differential Privacy
Anna Gilbert, Audra McMillan

TL;DR
This paper investigates the feasibility of property testing for differential privacy, revealing fundamental limitations and trade-offs in verifying privacy guarantees through black-box access.
Contribution
It establishes lower bounds and algorithms for testing differential privacy, highlighting that verification often compromises either privacy guarantees or access to information.
Findings
Efficient verification of privacy guarantees is generally infeasible.
There are fundamental lower bounds on query complexity for testing differential privacy.
Verification requires trade-offs, either weakening privacy or access to the algorithm.
Abstract
We consider the problem of property testing for differential privacy: with black-box access to a purportedly private algorithm, can we verify its privacy guarantees? In particular, we show that any privacy guarantee that can be efficiently verified is also efficiently breakable in the sense that there exist two databases between which we can efficiently distinguish. We give lower bounds on the query complexity of verifying pure differential privacy, approximate differential privacy, random pure differential privacy, and random approximate differential privacy. We also give algorithmic upper bounds. The lower bounds obtained in the work are infeasible for the scale of parameters that are typically considered reasonable in the differential privacy literature, even when we suppose that the verifier has access to an (untrusted) description of the algorithm. A central message of this work is…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
