A Dimensionality Reduction-Based Transformation to Support Business Collaboration

A Dimensionality Reduction-Based Transformation to Support Business Collaboration

Stanley R.M. Oliveira, Osmar R. Zaiane
ISBN13: 9781605662107|ISBN10: 1605662100|EISBN13: 9781605662114
DOI: 10.4018/978-1-60566-210-7.ch006
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MLA

Oliveira, Stanley R.M., and Osmar R. Zaiane. "A Dimensionality Reduction-Based Transformation to Support Business Collaboration." Techniques and Applications for Advanced Information Privacy and Security: Emerging Organizational, Ethical, and Human Issues, edited by Hamid Nemati, IGI Global, 2009, pp. 79-102. https://doi.org/10.4018/978-1-60566-210-7.ch006

APA

Oliveira, S. R. & Zaiane, O. R. (2009). A Dimensionality Reduction-Based Transformation to Support Business Collaboration. In H. Nemati (Ed.), Techniques and Applications for Advanced Information Privacy and Security: Emerging Organizational, Ethical, and Human Issues (pp. 79-102). IGI Global. https://doi.org/10.4018/978-1-60566-210-7.ch006

Chicago

Oliveira, Stanley R.M., and Osmar R. Zaiane. "A Dimensionality Reduction-Based Transformation to Support Business Collaboration." In Techniques and Applications for Advanced Information Privacy and Security: Emerging Organizational, Ethical, and Human Issues, edited by Hamid Nemati, 79-102. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-210-7.ch006

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Abstract

While the sharing of data is known to be beneficial in data mining applications and widely acknowledged as advantageous in business, this information sharing can become controversial and thwarted by privacy regulations and other privacy concerns. Data clustering for instance could be more accurate if more information is available, hence the data sharing. Any solution needs to balance the clustering requirements and the privacy issues. Rather than simply hindering data owners from sharing information for data analysis, a solution could be designed to meet privacy requirements and guarantee valid data clustering results. To achieve this dual goal, this chapter introduces a 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. It is shown analytically and empirically that transforming a dataset using DRBT, a data owner can achieve privacy preservation and get accurate clustering with little overhead of communication cost. Such a method presents the following advantages: it is independent of distance-based clustering algorithms; it has a sound mathematical foundation; and it does not require CPU-intensive operations.

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