An Improved Multi-Parameters Perturbation Privacy Preserving Association Rules Mining Algorithm

Jie Liu; Tian Qi Li; Jian Pei Zhang

doi:10.4028/www.scientific.net/AMM.130-134.2629

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 130-134An Improved Multi-Parameters Perturbation Privacy...

An Improved Multi-Parameters Perturbation Privacy Preserving Association Rules Mining Algorithm

Abstract:

Multi-parameters data perturbation method is a kind of original data perturbation methods for privacy preserving association rules mining. However, the time-efficiency of restoring the frequent itemsets in multi-parameters perturbation algorithm is still not high.One method is proposed in this paper to improve the time efficiency of multi-parameters randomized perturbation algorithm according to the characteristics of the model to restore frequent itemsets. The method improves the time efficiency by getting the elements of the first line of the inversed matrix of transformation matrix. Finally, both theoretical analysis and experimental results show that the improved algorithm is more time-efficient and space-efficient than the original algorithm.

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Periodical:

Applied Mechanics and Materials (Volumes 130-134)

Pages:

2629-2632

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.130-134.2629

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Online since:

October 2011

Authors:

Jie Liu, Tian Qi Li, Jian Pei Zhang

Keywords:

Association Rule Mining, Multi-Parameters Perturbation, Privacy Preserving, Transformation Matrix

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References

[1] Rizvi SJ, Haritsa JR. Maintaining data privacy in association rule mining. In: Bernstein PA, Ioannidis YE, Ramakrishnan R, Papadias D, eds. Proc. of the 28th Int'1 Conf. on Very Large Data Bases. Hong Kong: Morgan Kaufmann Publishers, 2002: 682-693.

DOI: 10.1016/b978-155860869-6/50066-4

Google Scholar

[2] Saygin Y, Verykios VS, Clifton C. Using unknowns to prevent discovery of association rules. ACM SIGMOD Record, 2001, 30(4): 45-54.

DOI: 10.1145/604264.604271

Google Scholar

[3] Zhang Peng, Tong Yun-Hai, Tang Shi-Wei, Yang Dong-Qing, Ma Xiu-Li. An effective method for privacy preserving association rule mining. Journal of Software (In Chinese). 2006: 1764-1774.

Google Scholar

[4] S. Agrawal and J. Haritsa. On addressing efficiency concerns in privacy-preserving mining. DASFAA 2004: 113-124.

DOI: 10.1007/978-3-540-24571-1_9

Google Scholar

[5] Wang Rui, Liu Jie. Research of Privacy Preserving Association Rules Mining Algorithm. Computer Engineer and Applications, 2009, 45(26): 126-128. （In Chinese）.

Google Scholar