CheckDP: An Automated and Integrated Approach for Proving Differential Privacy or Finding Precise Counterexamples
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Abstract (arXiv)
We propose CheckDP, the first automated and integrated approach for proving or disproving claims that a mechanism is differentially private. CheckDP can find counterexamples for mechanisms with subtle bugs for which prior counterexample generators have failed. Furthermore, it was able to automatically generate proofs for correct mechanisms for which no formal verification was reported before. CheckDP is built on static program analysis, allowing it to be more efficient and more precise in catching infrequent events than existing counterexample generators (which run mechanisms hundreds of thousands of times to estimate their output distribution). Moreover, its sound approach also allows automatic verification of correct mechanisms. When evaluated on standard benchmarks and newer privacy mechanisms, CheckDP generates proofs (for correct mechanisms) and counterexamples (for incorrect mechanisms) within 70 seconds without any false positives or false negatives.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Aug 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 2; 2025: 1; 2026: 1.
7 papers.
- 2022How reparametrization trick broke differentially-private text representation learningcited by 10
- 2022PPA: Preference Profiling Attack Against Federated Learningcited by 8
- 2024Revisiting Differentially Private Hyper-parameter Tuningcited by 5
- 2025The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditingcited by 4
- 2024QueryCheetah: Fast Automated Discovery of Attribute Inference Attacks Against Query-Based Systemscited by 3
- 2021Learning Differentially Private Mechanismscited by 0
- 2026Sequential Auditing for f-Differential Privacycited by 0
- 2026Sequential Auditing for f-Differential Privacy6 Feb 2026
- 2025The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditing20 Jun 2025
- 2024QueryCheetah: Fast Automated Discovery of Attribute Inference Attacks Against Query-Based Systems3 Sep 2024
- 2024Revisiting Differentially Private Hyper-parameter Tuning20 Feb 2024
- 2022How reparametrization trick broke differentially-private text representation learning24 Feb 2022
- 2022PPA: Preference Profiling Attack Against Federated Learning10 Feb 2022
- 2021Learning Differentially Private Mechanisms4 Jan 2021