FastLloyd: Federated, Accurate, Secure, and Tunable $k$-Means Clustering with Differential Privacy
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909645391527936 |
|---|---|
| author | Diaa, Abdulrahman Humphries, Thomas Kerschbaum, Florian |
| author_facet | Diaa, Abdulrahman Humphries, Thomas Kerschbaum, Florian |
| contents | We study the problem of privacy-preserving $k$-means clustering in the horizontally federated setting. Existing federated approaches using secure computation suffer from substantial overheads and do not offer output privacy. At the same time, differentially private (DP) $k$-means algorithms either assume a trusted central curator or significantly degrade utility by adding noise in the local DP model. Naively combining the secure and central DP solutions results in a protocol with impractical overhead. Instead, our work provides enhancements to both the DP and secure computation components, resulting in a design that is faster, more private, and more accurate than previous work. By utilizing the computational DP model, we design a lightweight, secure aggregation-based approach that achieves five orders of magnitude speed-up over state-of-the-art related work. Furthermore, we not only maintain the utility of the state-of-the-art in the central model of DP, but we improve the utility further by designing a new DP clustering mechanism. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_02437 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FastLloyd: Federated, Accurate, Secure, and Tunable $k$-Means Clustering with Differential Privacy Diaa, Abdulrahman Humphries, Thomas Kerschbaum, Florian Cryptography and Security Machine Learning We study the problem of privacy-preserving $k$-means clustering in the horizontally federated setting. Existing federated approaches using secure computation suffer from substantial overheads and do not offer output privacy. At the same time, differentially private (DP) $k$-means algorithms either assume a trusted central curator or significantly degrade utility by adding noise in the local DP model. Naively combining the secure and central DP solutions results in a protocol with impractical overhead. Instead, our work provides enhancements to both the DP and secure computation components, resulting in a design that is faster, more private, and more accurate than previous work. By utilizing the computational DP model, we design a lightweight, secure aggregation-based approach that achieves five orders of magnitude speed-up over state-of-the-art related work. Furthermore, we not only maintain the utility of the state-of-the-art in the central model of DP, but we improve the utility further by designing a new DP clustering mechanism. |
| title | FastLloyd: Federated, Accurate, Secure, and Tunable $k$-Means Clustering with Differential Privacy |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2405.02437 |