Privacy Preserving in Clustering using Fuzzy Sets

Data mining techniques, in spite of their benefit in a wide range of applications have also raised threat to privacy and data security. This paper addresses the problem of preserving privacy of individuals when data is shared. Sharing the entire data not only provides irrelevant information to the miner but also makes the data vulnerable to privacy violation. Dimensionality of data is reduced as per the request for data, which is a small quantity of the entire database. The confidential attributes in the data to be released are transformed to fuzzy so as to meet privacy requirements and to simultaneously preserve the meaning of the data making it necessarily informative. Experimental results demonstrate that the proposed transformation provides accuracy of clustering results better than the earlier proposed transformations for preserving privacy.

• Type: Conference
• Year: 2006
• Where: DMIN
• Authors: S. Srinivasa Rao 0002, K. V. S. V. N. Raju, P. Kusuma Kumari
• Keywords: Data Mining Techniques, DMIN 2006, DMIN 2007, Entire Data, Privacy