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http://dx.doi.org/10.13089/JKIISC.2018.28.6.1401

Privacy-Preserving k-means Clustering of Encrypted Data  

Jeong, Yunsong (Graduate School of Information Security, Korea University)
Kim, Joon Sik (Graduate School of Information Security, Korea University)
Lee, Dong Hoon (Graduate School of Information Security, Korea University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.28, no.6, 2018 , pp. 1401-1414 More about this Journal
Abstract
The k-means clustering algorithm groups input data with the number of groups represented by variable k. In fact, this algorithm is particularly useful in market segmentation and medical research, suggesting its wide applicability. In this paper, we propose a privacy-preserving clustering algorithm that is appropriate for outsourced encrypted data, while exposing no information about the input data itself. Notably, our proposed model facilitates encryption of all data, which is a large advantage over existing privacy-preserving clustering algorithms which rely on multi-party computation over plaintext data stored on several servers. Our approach compares homomorphically encrypted ciphertexts to measure the distance between input data. Finally, we theoretically prove that our scheme guarantees the security of input data during computation, and also evaluate our communication and computation complexity in detail.
Keywords
Privacy-preserving clustering; Fully homomorphic encryption; k-means clustering;
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