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Preserving Privacy in Mining Quantitative Associations Rules

Preserving Privacy in Mining Quantitative Associations Rules

Madhu V. Ahluwalia, Aryya Gangopadhyay, Zhiyuan Chen
Copyright: © 2009 |Volume: 3 |Issue: 4 |Pages: 17
ISSN: 1930-1650|EISSN: 1930-1669|ISSN: 1930-1650|EISBN13: 9781616920685|EISSN: 1930-1669|DOI: 10.4018/jisp.2009100101
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MLA

Ahluwalia, Madhu V., et al. "Preserving Privacy in Mining Quantitative Associations Rules." IJISP vol.3, no.4 2009: pp.1-17. http://doi.org/10.4018/jisp.2009100101

APA

Ahluwalia, M. V., Gangopadhyay, A., & Chen, Z. (2009). Preserving Privacy in Mining Quantitative Associations Rules. International Journal of Information Security and Privacy (IJISP), 3(4), 1-17. http://doi.org/10.4018/jisp.2009100101

Chicago

Ahluwalia, Madhu V., Aryya Gangopadhyay, and Zhiyuan Chen. "Preserving Privacy in Mining Quantitative Associations Rules," International Journal of Information Security and Privacy (IJISP) 3, no.4: 1-17. http://doi.org/10.4018/jisp.2009100101

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Abstract

Association rule mining is an important data mining method that has been studied extensively by the academic community and has been applied in practice. In the context of association rule mining, the state-of-the-art in privacy preserving data mining provides solutions for categorical and Boolean association rules but not for quantitative association rules. This article fills this gap by describing a method based on discrete wavelet transform (DWT) to protect input data privacy while preserving data mining patterns for association rules. A comparison with an existing kd-tree based transform shows that the DWT-based method fares better in terms of efficiency, preserving patterns, and privacy.

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