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Privacy-Preserving Clustering to Uphold Business Collaboration: A Dimensionality Reduction-Based Transformation Approach

Full Text: ijisp.pdf PDF

The sharing of data has been proven bene cial in data mining applications. However, privacy regulations and other privacy concerns may prevent data owners from sharing information for data analysis. To resolve this challenging problem, data owners must design a solution that meets privacy requirements and guarantees valid data clustering results. To achieve this dual goal, we introduce a new method for privacy-preserving clustering, called Dimensionality Reduction-Based Transformation (DRBT). This method relies on the intuition behind random projection to protect the underlying attribute values subjected to cluster analysis. The major features of this method are: a) it is independent of distance-based clustering algorithms; b) it has a sound mathematical foundation; and c) it does not require CPU-intensive operations. We show analytically and empirically that transforming a dataset using DRBT, a data owner can achieve privacy preservation and get accurate clustering with a little overhead of communication cost.

Citation

S. Oliveira, O. Zaiane. "Privacy-Preserving Clustering to Uphold Business Collaboration: A Dimensionality Reduction-Based Transformation Approach". International Journal of Information Security and Privacy , 1(2), pp 13-36, August 2007.

Keywords: Privacy preserving data mining, clustering
Category: In Journal

BibTeX

@article{Oliveira+Zaiane:IJISP07,
  author = {Stanley R. Oliveira and Osmar R. Zaiane},
  title = {Privacy-Preserving Clustering to Uphold Business Collaboration: A
    Dimensionality Reduction-Based Transformation Approach},
  Volume = "1",
  Number = "2",
  Pages = {13-36},
  journal = {International Journal of Information Security and Privacy },
  year = 2007,
}

Last Updated: August 17, 2009
Submitted by Osmar Zaiane

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