PE-means: Improved Differentially Private $k$-means Clustering through Private Evolution
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Computer Science > Machine Learning
Title:PE-means: Improved Differentially Private $k$-means Clustering through Private Evolution
Abstract:We study the problem of differentially private (DP) $k$-means clustering in Euclidean space. Previous solutions rely on summing the private data directly, which induces a sensitivity proportional to the domain. We introduce PE-means, an extension of the private evolution (PE) algorithm (an increasingly popular method for synthetic data generation), to the problem of $k$-means clustering. The key advantage of PE is that it only computes a private histogram with constant sensitivity to guide the evolution. Our adaptation of PE includes new evolutionary operators for clustering, as well as other algorithmic improvements of independent interest. Overall, PE-means achieves an average improvement of 20% in clustering loss over state-of-the-art baselines.
| Subjects: | Machine Learning (cs.LG); Cryptography and Security (cs.CR); Databases (cs.DB) |
| Cite as: | arXiv:2606.00342 [cs.LG] |
| (or arXiv:2606.00342v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.00342
arXiv-issued DOI via DataCite (pending registration)
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