CheckDP: An Automated and Integrated Approach for Proving Differential Privacy or Finding Precise Counterexamples
Yuxin Wang, Zeyu Ding, Daniel Kifer, Danfeng Zhang
摘要
We propose CheckDP, an 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 precise in catching infrequent events than sampling based 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.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了最后一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper22
- DP-Sniper: Black-Box Discovery of Differential Privacy Violations using ClassifiersBenjamin Bichsel, Samuel Steffen, Ilija Bogunovic, Martin T. VechevS&P 2021 · 被引用 53 次
- Learning Differentially Private MechanismsSubhajit Roy, Justin Hsu, Aws AlbarghouthiS&P 2021 · 被引用 20 次
- Statistical Quantification of Differential Privacy: A Local ApproachÖnder Askin, Tim Kutta, Holger DetteS&P 2022 · 被引用 19 次
- Eureka: A General Framework for Black-box Differential Privacy EstimatorsYun Lu, Malik Magdon-Ismail, Yu Wei, Vassilis ZikasS&P 2024 · 被引用 16 次
- Group and Attack: Auditing Differential PrivacyJohan Lokna, Anouk Paradis, Dimitar I. Dimitrov, Martin T. VechevCCS 2023 · 被引用 10 次
它引用的顶会 Paper5
- Detecting Violations of Differential PrivacyZeyu Ding, Yuxin Wang, Guanhong Wang, Danfeng Zhang 等CCS 2018 · 被引用 156 次
- DP-Finder: Finding Differential Privacy Violations by Sampling and OptimizationBenjamin Bichsel, Timon Gehr, Dana Drachsler-Cohen, Petar Tsankov 等CCS 2018 · 被引用 82 次
- Advanced Probabilistic Couplings for Differential PrivacyGilles Barthe, Noémie Fong, Marco Gaboardi, Benjamin Grégoire 等CCS 2016 · 被引用 67 次
- Deciding Differential Privacy for Programs with Finite Inputs and OutputsGilles Barthe, Rohit Chadha, Vishal Jagannath, A. Prasad Sistla 等LICS 2020 · 被引用 24 次
- Free Gap Information from the Differentially Private Sparse Vector and Noisy Max MechanismsZeyu Ding, Yuxin Wang, Danfeng Zhang, Dan KiferVLDB 2020 · 被引用 14 次
相关 Paper
- SuperDP: Differential Privacy Refutation via SupermartingalesKrishnendu Chatterjee, Ehsan Kafshdar Goharshady, Dorde ZikelicPLDI 2026
- Testing differential privacy with dual interpretersHengchu Zhang, Edo Roth, Andreas Haeberlen, Benjamin C. Pierce 等OOPSLA 2020 · 被引用 15 次
- Verified Foundations for Differential PrivacyMarkus de Medeiros, Muhammad Naveed, Tancrède Lepoint, Temesghen Kahsai 等PLDI 2025 · 被引用 7 次
- Interactive Proofs For Differentially Private CountingAri Biswas, Graham CormodeCCS 2023 · 被引用 10 次
- Approximate Algorithms for Verifying Differential Privacy with Gaussian DistributionsBishnu Bhusal, Rohit Chadha, A. Prasad Sistla, Mahesh ViswanathanCCS 2025
