Privacy preserving technique for Euclidean distance based mining algorithms using a wavelet related transform

Authors:
Mohammad Ali Kadampur;D. V. L. N. Somayajulu
Affiliations:
National Institute of Technology, Warangal, A.P., India;National Institute of Technology, Warangal, A.P., India
Venue:
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
Year:
2010

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Abstract

Privacy preserving data mining is an art of knowledge discovery without revealing the sensitive data of the data set. In this paper a data transformation technique using wavelets is presented for privacy preserving data mining. Wavelets use well known energy compaction approach during data transformation and only the high energy coefficients are published to the public domain instead of the actual data proper. It is found that the transformed data preserves the Eucleadian distances and the method can be used in privacy preserving clustering. Wavelets offer the inherent improved time complexity.