Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917980169830400 |
|---|---|
| author | Lebeda, Christian Janos Regehr, Matthew Kamath, Gautam Steinke, Thomas |
| author_facet | Lebeda, Christian Janos Regehr, Matthew Kamath, Gautam Steinke, Thomas |
| contents | We consider the problem of computing tight privacy guarantees for the composition of subsampled differentially private mechanisms. Recent algorithms can numerically compute the privacy parameters to arbitrary precision but must be carefully applied.
Our main contribution is to address two common points of confusion. First, some privacy accountants assume that the privacy guarantees for the composition of a subsampled mechanism are determined by self-composing the worst-case datasets for the uncomposed mechanism. We show that this is not true in general. Second, Poisson subsampling is sometimes assumed to have similar privacy guarantees compared to sampling without replacement. We show that the privacy guarantees may in fact differ significantly between the two sampling schemes. In particular, we give an example of hyperparameters that result in $\varepsilon \approx 1$ for Poisson subsampling and $\varepsilon > 10$ for sampling without replacement. This occurs for some parameters that could realistically be chosen for DP-SGD. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_20769 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition Lebeda, Christian Janos Regehr, Matthew Kamath, Gautam Steinke, Thomas Cryptography and Security Data Structures and Algorithms Machine Learning We consider the problem of computing tight privacy guarantees for the composition of subsampled differentially private mechanisms. Recent algorithms can numerically compute the privacy parameters to arbitrary precision but must be carefully applied. Our main contribution is to address two common points of confusion. First, some privacy accountants assume that the privacy guarantees for the composition of a subsampled mechanism are determined by self-composing the worst-case datasets for the uncomposed mechanism. We show that this is not true in general. Second, Poisson subsampling is sometimes assumed to have similar privacy guarantees compared to sampling without replacement. We show that the privacy guarantees may in fact differ significantly between the two sampling schemes. In particular, we give an example of hyperparameters that result in $\varepsilon \approx 1$ for Poisson subsampling and $\varepsilon > 10$ for sampling without replacement. This occurs for some parameters that could realistically be chosen for DP-SGD. |
| title | Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition |
| topic | Cryptography and Security Data Structures and Algorithms Machine Learning |
| url | https://arxiv.org/abs/2405.20769 |