02192naa a2200253 a 450000100080000000500110000800800410001910000260006024501490008626000090023550000450024452013180028965000210160765300270162865300200165565300160167565300290169165300250172065300160174565300340176165300220179570000190181777301020183610033352020-01-17       2007    bl uuuu     u00u1 u    #d1 aOLIVEIRA, S. R. de M.  aPrivacy-preserving clustering to uphold business collaborationba dimensionality reduction-based transformation approach.h[electronic resource]  c2007  aNa publicação: Stanley R. M. Oliveira.  aWhile 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.  aCluster analysis  aBusiness collaboration  aClusterização  aData mining  aDimensionality reduction  aMineração de dados  aPrivacidade  aPrivacy-preserving clustering  aRandom projection1 aZAÏANE, O. R.  tInternational Journal of Information Security and Privacygv. 1, n. 2, p. 13-36, Apr./June, 2007.