Abstract
In recent years, the cloud computation has provided great convenience for users to outsource data for storage and computation. But when data stored in cloud, it’s out of control from user, and there is a risk of private data leakage. In this paper, we propose a framework of secure K-means (CKKSKM) based on homomorphic encryption. The proposed scheme encrypts outsourcing data which can avoid to reveal the private information. We preprocess the data first and uses the Euclidean distance to calculate similarity, which can ensure the data accuracy and reduce the computing overhead. Based on CKKS homomorphic encryption, this scheme can solve the privacy security and reduce overhead of the user’s outsourcing data in cloud for storage and calculation.
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Acknowledgments
This work is supported by Natural Science Basic Research Plan in Shaanxi Province of China [No. 2018JM6028], the Foundation of Guizhou Provincial Key Laboratory of Public Big Data [No. 2019BDKFJJ008], Engineering University of PAP’s Funding for Scientific Research Innovation Team [No. KYTD201805] and Engineering University of PAP’s Funding for Key Researcher [No. KYGG202011].
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Tu, Z., Wang, X.A., Su, Y., Li, Y., Liu, J. (2022). Toward Secure K-means Clustering Based on Homomorphic Encryption in Cloud. In: Barolli, L., Kulla, E., Ikeda, M. (eds) Advances in Internet, Data & Web Technologies. EIDWT 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 118. Springer, Cham. https://doi.org/10.1007/978-3-030-95903-6_7
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DOI: https://doi.org/10.1007/978-3-030-95903-6_7
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