Property Testing for Differential Privacy
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Abstract (arXiv)
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.
Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Jun 2018.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 4; 2024: 4; 2025: 2; 2026: 3.
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- 2022Reproducibility in Learningcited by 44
- 2023One-shot Empirical Privacy Estimation for Federated Learningcited by 36
- 2023Have it your way: Individualized Privacy Assignment for DP-SGDcited by 21
- 2023Unleashing the Power of Randomization in Auditing Differentially Private MLcited by 20
- 2021On a Utilitarian Approach to Privacy Preserving Text Generationcited by 17
- 2023A Randomized Approach for Tight Privacy Accountingcited by 11
- 2024Auditing Differential Privacy Guarantees Using Density Estimationcited by 6
- 2024BadGD: A unified data-centric framework to identify gradient descent vulnerabilitiescited by 5
- 2025Sequentially Auditing Differential Privacycited by 5
- 2022Provable Membership Inference Privacycited by 4
- 2024Personalized Differential Privacy for Ridge Regressioncited by 3
- 2024Regulation Games for Trustworthy Machine Learningcited by 3
4 more
- 2026Regularized f-Divergence Kernel Testscited by 2
- 2025Efficient Public Verification of Private ML via Regularizationcited by 1
- 2026When Are Trade-Off Functions Testable from Finite Samples?cited by 0
- 2026Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Datacited by 0
- 2026Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data15 Jun 2026
- 2026When Are Trade-Off Functions Testable from Finite Samples?11 May 2026
- 2026Regularized f-Divergence Kernel Tests27 Jan 2026
- 2025Efficient Public Verification of Private ML via Regularization3 Dec 2025
- 2025Sequentially Auditing Differential Privacy8 Sep 2025
- 2024Auditing Differential Privacy Guarantees Using Density Estimation7 Jun 2024
- 2024BadGD: A unified data-centric framework to identify gradient descent vulnerabilities24 May 2024
- 2024Regulation Games for Trustworthy Machine Learning5 Feb 2024
- 2024Personalized Differential Privacy for Ridge Regression30 Jan 2024
- 2023Unleashing the Power of Randomization in Auditing Differentially Private ML29 May 2023
- 2023A Randomized Approach for Tight Privacy Accounting17 Apr 2023
- 2023Have it your way: Individualized Privacy Assignment for DP-SGD29 Mar 2023
4 more
- 2023One-shot Empirical Privacy Estimation for Federated Learning6 Feb 2023
- 2022Provable Membership Inference Privacy12 Nov 2022
- 2022Reproducibility in Learning20 Jan 2022
- 2021On a Utilitarian Approach to Privacy Preserving Text Generation23 Apr 2021
- 2024BadGD: A unified data-centric framework to identify gradient descent vulnerabilitiescited by 5
- 2022Provable Membership Inference Privacycited by 4
- 2026Regularized f-Divergence Kernel Testscited by 2
- 2026Regularized f-Divergence Kernel Tests27 Jan 2026
- 2024BadGD: A unified data-centric framework to identify gradient descent vulnerabilities24 May 2024
- 2022Provable Membership Inference Privacy12 Nov 2022
- 2022Reproducibility in Learningcited by 44
- 2023One-shot Empirical Privacy Estimation for Federated Learningcited by 36
- 2023Have it your way: Individualized Privacy Assignment for DP-SGDcited by 21
- 2023Unleashing the Power of Randomization in Auditing Differentially Private MLcited by 20
- 2021On a Utilitarian Approach to Privacy Preserving Text Generationcited by 17
- 2023A Randomized Approach for Tight Privacy Accountingcited by 11
- 2024Auditing Differential Privacy Guarantees Using Density Estimationcited by 6
- 2025Sequentially Auditing Differential Privacycited by 5
- 2024Personalized Differential Privacy for Ridge Regressioncited by 3
- 2024Regulation Games for Trustworthy Machine Learningcited by 3
- 2025Efficient Public Verification of Private ML via Regularizationcited by 1
- 2026When Are Trade-Off Functions Testable from Finite Samples?cited by 0
- 2026When Are Trade-Off Functions Testable from Finite Samples?11 May 2026
- 2025Efficient Public Verification of Private ML via Regularization3 Dec 2025
- 2025Sequentially Auditing Differential Privacy8 Sep 2025
- 2024Auditing Differential Privacy Guarantees Using Density Estimation7 Jun 2024
- 2024Regulation Games for Trustworthy Machine Learning5 Feb 2024
- 2024Personalized Differential Privacy for Ridge Regression30 Jan 2024
- 2023Unleashing the Power of Randomization in Auditing Differentially Private ML29 May 2023
- 2023A Randomized Approach for Tight Privacy Accounting17 Apr 2023
- 2023Have it your way: Individualized Privacy Assignment for DP-SGD29 Mar 2023
- 2023One-shot Empirical Privacy Estimation for Federated Learning6 Feb 2023
- 2022Reproducibility in Learning20 Jan 2022
- 2021On a Utilitarian Approach to Privacy Preserving Text Generation23 Apr 2021