Abstract
Data privacy has become one of the most important concerns in the big data era. Because of its broad applications in machine learning and data analysis, many algorithms and theoretical results have been established for privacy clustering problems, such as k-means and k-median problems with privacy protection. However, there is little work on privacy protection in k-center clustering. Our research focuses on the k-center problem, its distributed variant, and the distributed k-center problem under differential privacy constraints. These problems model the concept of safeguarding the privacy of individual input elements, with the integration of differential privacy aimed at ensuring the security of individual information during data processing and analysis. We propose three approximation algorithms for these problems, respectively, and achieve a constant factor approximation ratio.
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We do not analyse or generate any datasets, because our work proceeds within a theoretical and mathematical approach.
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Acknowledgements
The first two authors are supported by National Natural Science Foundation of China (No. 12131003). The third author is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) grant 06446, and Natural Science Foundation of China (Nos. 11771386, 11728104). The fourth author is supported by Natural Science Foundation of Shandong Province of China (No. ZR2020MA029).
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A preliminary version (two-page extended abstract) of this paper appeared in Proceedings of the 9th International Conference on Computational Data and Social Networks (CSoNet), 2020, pp. xviii-xx.
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Yuan, F., Xu, D., Du, D. et al. Differentially private k-center problems. Optim Lett (2024). https://doi.org/10.1007/s11590-023-02090-w
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DOI: https://doi.org/10.1007/s11590-023-02090-w