Improving the Utility of Differentially Private Clustering through Dynamical Processing
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910572405063680 |
|---|---|
| author | Byun, Junyoung Choi, Yujin Lee, Jaewook |
| author_facet | Byun, Junyoung Choi, Yujin Lee, Jaewook |
| contents | This study aims to alleviate the trade-off between utility and privacy of differentially private clustering. Existing works focus on simple methods, which show poor performance for non-convex clusters. To fit complex cluster distributions, we propose sophisticated dynamical processing inspired by Morse theory, with which we hierarchically connect the Gaussian sub-clusters obtained through existing methods. Our theoretical results imply that the proposed dynamical processing introduces little to no additional privacy loss. Experiments show that our framework can improve the clustering performance of existing methods at the same privacy level. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_13886 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Improving the Utility of Differentially Private Clustering through Dynamical Processing Byun, Junyoung Choi, Yujin Lee, Jaewook Machine Learning Cryptography and Security This study aims to alleviate the trade-off between utility and privacy of differentially private clustering. Existing works focus on simple methods, which show poor performance for non-convex clusters. To fit complex cluster distributions, we propose sophisticated dynamical processing inspired by Morse theory, with which we hierarchically connect the Gaussian sub-clusters obtained through existing methods. Our theoretical results imply that the proposed dynamical processing introduces little to no additional privacy loss. Experiments show that our framework can improve the clustering performance of existing methods at the same privacy level. |
| title | Improving the Utility of Differentially Private Clustering through Dynamical Processing |
| topic | Machine Learning Cryptography and Security |
| url | https://arxiv.org/abs/2304.13886 |