Hershey, Pennsylvania
New York, New YorkBeijing, China
Privacy-Preserving Clustering to Uphold Business Collaboration: A Dimensionality Reduction Based Transformation Approach

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

Stanley R.M. Oliveira (Embrapa Informática Agropecuária, Brazil) and Osmar R. Zaïane (University of Alberta, Canada)
Copyright: © 2007 |Pages: 24
DOI: 10.4018/jisp.2007040102
OnDemand:
(Individual Articles)
Available
$37.50
Current Special Offers
No Current Special Offers
TOTAL SAVINGS: $37.50

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 article 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.

Complete Article List

Search this Journal:
Reset
Volume 19: 1 Issue (2025)
Volume 18: 1 Issue (2024)
Volume 17: 1 Issue (2023)
Volume 16: 4 Issues (2022): 2 Released, 2 Forthcoming
Volume 15: 4 Issues (2021)
Volume 14: 4 Issues (2020)
Volume 13: 4 Issues (2019)
Volume 12: 4 Issues (2018)
Volume 11: 4 Issues (2017)
Volume 10: 4 Issues (2016)
Volume 9: 4 Issues (2015)
Volume 8: 4 Issues (2014)
Volume 7: 4 Issues (2013)
Volume 6: 4 Issues (2012)
Volume 5: 4 Issues (2011)
Volume 4: 4 Issues (2010)
Volume 3: 4 Issues (2009)
Volume 2: 4 Issues (2008)
Volume 1: 4 Issues (2007)
View Complete Journal Contents Listing