01560nam a2200181 a 450000100080000000500110000800800410001910000260006024501440008626002050023050000450043552007900048065000210127065300240129165300160131565300310133170000160136210090072020-01-17       2004    bl uuuu     u00u1 u    #d1 aOLIVEIRA, S. R. de M.  aPrivacy-preserving clustering by object similarity-based representation and dimensionality reduction transformation.h[electronic resource]  aIn: WORKSHOP ON PRIVACY AND SECURITY ASPECTS OF DATA MINING; IEEE INTERNATIONAL CONFERENCE ON DATA MINING, 4., 2004, Brighton, UK. Proceedings... Brighton: IEEE Computer Society, 2004. p. 21-30.c2004  aNa publicação: Stanley R. M. Oliveira.  aPreserving privacy of individuals when data are shared for clustering is a challenging problem. Data owners must not only meet privacy requirements but also guarantee valid clustering results. In this paper, we show that this dual goal can be achieved by transforming a database using two simple and effective data transformations: Object Similarity-Based Representation (OSBR) and Dimensionality Reduction-Based Transformation (DRBT). The former relies on the idea behind the similarity between objects, and the latter relies on the intuition behind random projection. The major features of our data transformations are: a) they are independent of distance-based clustering algorithms; b) they have a sound mathematical foundation; and c) they do not require CPU-intensive operations.  aCluster analysis  aPattern recognition  aPrivacidade  aReconhecimento de padrões1 aZAÏANE, O.