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DOI: 10.14569/IJACSA.2013.040932
PDF

Privacy-Preserving Clustering Using Representatives over Arbitrarily Partitioned Data

Author 1: Yu Li
Author 2: Sheng Zhong

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 4 Issue 9, 2013.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: The challenge in privacy-preserving data mining is avoiding the invasion of personal data privacy. Secure computa- tion provides a solution to this problem. With the development of this technique, fully homomorphic encryption has been realized after decades of research; this encryption enables the computing and obtaining results via encrypted data without accessing any plaintext or private key information. In this paper, we propose a privacy-preserving clustering using representatives (CURE) algorithm over arbitrarily partitioned data using fully homomor- phic encryption. Our privacy-preserving CURE algorithm allows cooperative computation without revealing users’ individual data. The method used in our algorithm enables the data to be arbitrarily distributed among different parties and to receive accurate clustering result simultaneously.

Keywords:

Yu Li and Sheng Zhong, “Privacy-Preserving Clustering Using Representatives over Arbitrarily Partitioned Data” International Journal of Advanced Computer Science and Applications(IJACSA), 4(9), 2013. http://dx.doi.org/10.14569/IJACSA.2013.040932

@article{Li2013,
title = {Privacy-Preserving Clustering Using Representatives over Arbitrarily Partitioned Data},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2013.040932},
url = {http://dx.doi.org/10.14569/IJACSA.2013.040932},
year = {2013},
publisher = {The Science and Information Organization},
volume = {4},
number = {9},
author = {Yu Li and Sheng Zhong}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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