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.
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Kadampur, M.A., D.V.L.N., S. (2010). Privacy Preserving Technique for Euclidean Distance Based Mining Algorithms Using a Wavelet Related Transform. In: Fyfe, C., Tino, P., Charles, D., Garcia-Osorio, C., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2010. IDEAL 2010. Lecture Notes in Computer Science, vol 6283. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15381-5_25
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DOI: https://doi.org/10.1007/978-3-642-15381-5_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15380-8
Online ISBN: 978-3-642-15381-5
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