Property Testing for Differential Privacy

06/17/2018
by   Anna Gilbert, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/01/2019

Lower Bounds for Locally Private Estimation via Communication Complexity

We develop lower bounds for estimation under local privacy constraints--...
research
02/21/2020

Privately Learning Markov Random Fields

We consider the problem of learning Markov Random Fields (including the ...
research
12/09/2022

Lower Bounds for Rényi Differential Privacy in a Black-Box Setting

We present new methods for assessing the privacy guarantees of an algori...
research
02/11/2023

On Differential Privacy and Adaptive Data Analysis with Bounded Space

We study the space complexity of the two related fields of differential ...
research
01/31/2023

Tight Data Access Bounds for Private Top-k Selection

We study the top-k selection problem under the differential privacy mode...
research
08/20/2021

Uniformity Testing in the Shuffle Model: Simpler, Better, Faster

Uniformity testing, or testing whether independent observations are unif...
research
05/24/2019

Minimax Rates of Estimating Approximate Differential Privacy

Differential privacy has become a widely accepted notion of privacy, lea...

Please sign up or login with your details

Forgot password? Click here to reset