Property Testing For Differential Privacy
By Anna C. Gilbert, Audra McMillan. 2018
38 citations recorded by the source corpus.
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 that verifying privacy requires compromise by either the verifier or the algorithm owner. Either the verifier has to be satisfied with a weak privacy guarantee, or the algorithm owner has to compromise on side information or access to the algorithm.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Guanhong Wang
- Yuxin Wang
- Zeyu Ding
- Danfeng Zhang
- Daniel Kifer
- Ashwin Machanavajjhala
- Kamalika Chaudhuri
- Adam D. Smith
Citations in the corpus
- Proving differential privacy with shadow execution (2019, background): “t is that the transformed program can also be analyzed by standard symbolic executors. This appears to be an important property in light of recent work on detecting counterexamples for buggy programs [12, 18, 25, 26]. Producing a transformed program that can be used for verification of correct progr”