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- W4226317026 abstract "Clustering is an essential primitive in unsupervised machine learning. We bring forth the problem of sublinear-time differentially-private clustering as a natural and well-motivated direction of research. We combine the $k$-means and $k$-median sublinear-time results of Mishra et al. (SODA, 2001) and of Czumaj and Sohler (Rand. Struct. and Algorithms, 2007) with recent results on private clustering of Balcan et al. (ICML 2017), Gupta et al. (SODA, 2010) and Ghazi et al. (NeurIPS, 2020) to obtain sublinear-time private $k$-means and $k$-median algorithms via subsampling. We also investigate the privacy benefits of subsampling for group privacy." @default.
- W4226317026 created "2022-05-05" @default.
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- W4226317026 date "2021-12-27" @default.
- W4226317026 modified "2023-10-16" @default.
- W4226317026 title "Differentially-Private Sublinear-Time Clustering" @default.
- W4226317026 doi "https://doi.org/10.48550/arxiv.2112.13751" @default.
- W4226317026 hasPublicationYear "2021" @default.
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