{"id":1175027,"date":"2026-06-08T15:07:14","date_gmt":"2026-06-08T22:07:14","guid":{"rendered":"https:\/\/find.codeghost.online\/en-us\/research\/publication\/pe-means-improved-differentially-private-k-means-clustering-through-private-evolution\/"},"modified":"2026-07-10T14:32:41","modified_gmt":"2026-07-10T21:32:41","slug":"pe-means-improved-differentially-private-k-means-clustering-through-private-evolution","status":"publish","type":"msr-research-item","link":"https:\/\/find.codeghost.online\/en-us\/research\/publication\/pe-means-improved-differentially-private-k-means-clustering-through-private-evolution\/","title":{"rendered":"PE-means: Improved Differentially Private $k$-means Clustering through Private Evolution"},"content":{"rendered":"\n\n\n<p class=\"wp-block-paragraph\">We study the problem of differentially private (DP) <math><mi>k<\/mi><\/math>-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 <math><mi>k<\/mi><\/math>-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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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. 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