Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.12520 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909582208532480 |
|---|---|
| author | Hehir, Jonathan Niu, Xiaoyue Slavkovic, Aleksandra |
| author_facet | Hehir, Jonathan Niu, Xiaoyue Slavkovic, Aleksandra |
| contents | How do we interpret the differential privacy (DP) guarantee for network data? We take a deep dive into a popular form of network DP ($\varepsilon$--edge DP) to find that many of its common interpretations are flawed. Drawing on prior work for privacy with correlated data, we interpret DP through the lens of adversarial hypothesis testing and demonstrate a gap between the pairs of hypotheses actually protected under DP (tests of complete networks) and the sorts of hypotheses implied to be protected by common claims (tests of individual edges). We demonstrate some conditions under which this gap can be bridged, while leaving some questions open. While some discussion is specific to edge DP, we offer selected results in terms of abstract DP definitions and provide discussion of the implications for other forms of network DP. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_12520 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interpreting Network Differential Privacy Hehir, Jonathan Niu, Xiaoyue Slavkovic, Aleksandra Statistics Theory Computers and Society How do we interpret the differential privacy (DP) guarantee for network data? We take a deep dive into a popular form of network DP ($\varepsilon$--edge DP) to find that many of its common interpretations are flawed. Drawing on prior work for privacy with correlated data, we interpret DP through the lens of adversarial hypothesis testing and demonstrate a gap between the pairs of hypotheses actually protected under DP (tests of complete networks) and the sorts of hypotheses implied to be protected by common claims (tests of individual edges). We demonstrate some conditions under which this gap can be bridged, while leaving some questions open. While some discussion is specific to edge DP, we offer selected results in terms of abstract DP definitions and provide discussion of the implications for other forms of network DP. |
| title | Interpreting Network Differential Privacy |
| topic | Statistics Theory Computers and Society |
| url | https://arxiv.org/abs/2504.12520 |