Abstract
Until now, a lot of clustering algorithms for differential privacy (DP) have been proposed. Practically, there still exist difficulties in implementing these algorithms in a big data platform. In this paper, we proposed a clustering algorithm for privacy preservation on MapReduce. The algorithm is implemented from two aspects. Firstly, the optimized Canopy algorithm is implemented to get the optimal number of clusters and the initial center points on MapReduce. Secondly, the DP K-means algorithm is implemented to get the final clusters on MapReduce. As a result, the proposed algorithm can generate the optimal clustering number that is same with the standard classified data set and can achieve better accuracy of the clusters with the suitable privacy budget \(\varepsilon \).
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Acknowledgment
Project supported by the National Key Research and Development Program of China (No. 2016YFC1000307) and the National Natural Science Foundation of China (No. 61571024) for valuable helps.
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Zhao, Z., Shang, T., Liu, J., Guan, Z. (2018). Clustering Algorithm for Privacy Preservation on MapReduce. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11064. Springer, Cham. https://doi.org/10.1007/978-3-030-00009-7_56
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DOI: https://doi.org/10.1007/978-3-030-00009-7_56
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