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- W4287684470 abstract "This paper studies the problem of clustering in metric spaces while preserving the privacy of individual data. Specifically, we examine differentially private variants of the k-medians and Euclidean k-means problems. We present polynomial algorithms with constant multiplicative error and lower additive error than the previous state-of-the-art for each problem. Additionally, our algorithms use a clustering algorithm without differential privacy as a black-box. This allows practitioners to control the trade-off between runtime and approximation factor by choosing a suitable clustering algorithm to use." @default.
- W4287684470 created "2022-07-26" @default.
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- W4287684470 date "2020-08-27" @default.
- W4287684470 modified "2023-09-28" @default.
- W4287684470 title "Differentially Private Clustering via Maximum Coverage" @default.
- W4287684470 doi "https://doi.org/10.48550/arxiv.2008.12388" @default.
- W4287684470 hasPublicationYear "2020" @default.
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