Adversarial Sample-Based Approach for Tighter Privacy Auditing in Final Model-Only Scenarios
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913704405106688 |
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| author | Yoon, Sangyeon Jeung, Wonje No, Albert |
| author_facet | Yoon, Sangyeon Jeung, Wonje No, Albert |
| contents | Auditing Differentially Private Stochastic Gradient Descent (DP-SGD) in the final model setting is challenging and often results in empirical lower bounds that are significantly looser than theoretical privacy guarantees. We introduce a novel auditing method that achieves tighter empirical lower bounds without additional assumptions by crafting worst-case adversarial samples through loss-based input-space auditing. Our approach surpasses traditional canary-based heuristics and is effective in final model-only scenarios. Specifically, with a theoretical privacy budget of $\varepsilon = 10.0$, our method achieves empirical lower bounds of $4.914$, compared to the baseline of $4.385$ for MNIST. Our work offers a practical framework for reliable and accurate privacy auditing in differentially private machine learning. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_01756 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adversarial Sample-Based Approach for Tighter Privacy Auditing in Final Model-Only Scenarios Yoon, Sangyeon Jeung, Wonje No, Albert Cryptography and Security Machine Learning Auditing Differentially Private Stochastic Gradient Descent (DP-SGD) in the final model setting is challenging and often results in empirical lower bounds that are significantly looser than theoretical privacy guarantees. We introduce a novel auditing method that achieves tighter empirical lower bounds without additional assumptions by crafting worst-case adversarial samples through loss-based input-space auditing. Our approach surpasses traditional canary-based heuristics and is effective in final model-only scenarios. Specifically, with a theoretical privacy budget of $\varepsilon = 10.0$, our method achieves empirical lower bounds of $4.914$, compared to the baseline of $4.385$ for MNIST. Our work offers a practical framework for reliable and accurate privacy auditing in differentially private machine learning. |
| title | Adversarial Sample-Based Approach for Tighter Privacy Auditing in Final Model-Only Scenarios |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2412.01756 |