Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.08704 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908530547621888 |
|---|---|
| author | Xiang, Zihang Wang, Tianhao Xiao, Hanshen Tian, Yuan Wang, Di |
| author_facet | Xiang, Zihang Wang, Tianhao Xiao, Hanshen Tian, Yuan Wang, Di |
| contents | In this paper, we study the problem of privacy audit in one run and show that our method achieves tight audit results for various differentially private protocols. This includes obtaining tight results for auditing $(\varepsilon,δ)$-DP algorithms where all previous work fails to achieve in any parameter setups. We first formulate a framework for privacy audit \textit{in one run} with refinement compared with previous work. Then, based on modeling privacy by the $f$-DP formulation, we study the implications of our framework to obtain a theoretically justified lower bound for privacy audit. In the experiment, we compare with previous work and show that our audit method outperforms the rest in auditing various differentially private algorithms. We also provide experiments that give contrasting conclusions to previous work on the parameter settings for privacy audits in one run. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_08704 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tight Privacy Audit in One Run Xiang, Zihang Wang, Tianhao Xiao, Hanshen Tian, Yuan Wang, Di Cryptography and Security In this paper, we study the problem of privacy audit in one run and show that our method achieves tight audit results for various differentially private protocols. This includes obtaining tight results for auditing $(\varepsilon,δ)$-DP algorithms where all previous work fails to achieve in any parameter setups. We first formulate a framework for privacy audit \textit{in one run} with refinement compared with previous work. Then, based on modeling privacy by the $f$-DP formulation, we study the implications of our framework to obtain a theoretically justified lower bound for privacy audit. In the experiment, we compare with previous work and show that our audit method outperforms the rest in auditing various differentially private algorithms. We also provide experiments that give contrasting conclusions to previous work on the parameter settings for privacy audits in one run. |
| title | Tight Privacy Audit in One Run |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2509.08704 |