Auditing Differential Privacy in the Black-Box Setting
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909574714359808 |
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| author | Shi, Kaining Ma, Cong |
| author_facet | Shi, Kaining Ma, Cong |
| contents | This paper introduces a novel theoretical framework for auditing differential privacy (DP) in a black-box setting. Leveraging the concept of $f$-differential privacy, we explicitly define type I and type II errors and propose an auditing mechanism based on conformal inference. Our approach robustly controls the type I error rate under minimal assumptions. Furthermore, we establish a fundamental impossibility result, demonstrating the inherent difficulty of simultaneously controlling both type I and type II errors without additional assumptions. Nevertheless, under a monotone likelihood ratio (MLR) assumption, our auditing mechanism effectively controls both errors. We also extend our method to construct valid confidence bands for the trade-off function in the finite-sample regime. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_12045 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Auditing Differential Privacy in the Black-Box Setting Shi, Kaining Ma, Cong Methodology Cryptography and Security Machine Learning This paper introduces a novel theoretical framework for auditing differential privacy (DP) in a black-box setting. Leveraging the concept of $f$-differential privacy, we explicitly define type I and type II errors and propose an auditing mechanism based on conformal inference. Our approach robustly controls the type I error rate under minimal assumptions. Furthermore, we establish a fundamental impossibility result, demonstrating the inherent difficulty of simultaneously controlling both type I and type II errors without additional assumptions. Nevertheless, under a monotone likelihood ratio (MLR) assumption, our auditing mechanism effectively controls both errors. We also extend our method to construct valid confidence bands for the trade-off function in the finite-sample regime. |
| title | Auditing Differential Privacy in the Black-Box Setting |
| topic | Methodology Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2503.12045 |