Abstract
In order to improve privacy preservation and accuracy, we present a new association rule mining scheme based on data distortion. It consists of two steps: First, the original data are distorted by a new randomization method. Then, the mining algorithm is implemented to find frequent itemsets from the distorted data, and generate association rules. With reasonable selection for the random parameters, our scheme can simultaneously provide a higher privacy preserving level to the users and retain a higher accuracy in the mining results.
This work is supported by the National Natural Science Foundation of China under Grant No.60403041.
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© 2005 Springer-Verlag Berlin Heidelberg
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Zhang, P., Tong, Y., Tang, S., Yang, D. (2005). Mining Association Rules from Distorted Data for Privacy Preservation. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553939_187
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DOI: https://doi.org/10.1007/11553939_187
Publisher Name: Springer, Berlin, Heidelberg
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