Abstract
Many business applications use data mining techniques. Small organizations collaborate with each other to develop few applications to run their business smoothly in competitive world. While developing an application the organization wants to share data among themselves. So, it leads to the privacy issues of the individual customers, like personal information. This paper proposes a method which combines Walsh Hadamard Transformation (WHT) and existing data perturbation techniques to ensure privacy preservation for business applications. The proposed technique transforms original data into a new domain that achieves privacy related issues of individual customers of an organization. Experiments were conducted on two real data sets. From the observations it is concluded that the proposed technique gives acceptable accuracy with K-Nearest Neighbour (K-NN) classifier. Finally, the calculation of data distortion measures were done.
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References
J.F. Traub, Y. Yemini, and H. Wozniakowski, “The StatisticalSecurity of a Statistical Database,” ACM Trans. Database Systems, vol. 9, no. 4, pp. 672–679, 1984.
C.C. Aggarwal and P.S. Yu, “A Condensation Approach to Privacy Preserving Data Mining,” Proc. Ninth Int’l Conf. Extending Database Technology, pp. 183–199, 2004.
D. Defays and P. Nanopoulos, “Panels of Enterprises andConfidentiality: The Small Aggregates Method,” Proc. Statistics Canada Symp. 92 Design and Analysis of Longitudinal Surveys, pp. 195–204, 1993.
J. Domingo-Ferrer and J.M. Mateo-Sanz, “Practical Data-Oriented Microaggregation for Statistical Disclosure Control,” IEEE Trans. Knowledge and Data Eng., vol. 14, no. 1, pp. 189–201, 2002.
C.C. Yao, “How to generate and Exchange Secrets”, IEEE, 1986.
M. Kantarcioglu and C. Clifton. “Privately computing a distributed k-nn classifier”. PKDD, v. 3202, LNCS, pp. 279–290, 2004.
Z. Yang, S. Zhong, R. Wright, “Privacy-preserving Classification of Customer Data without Loss of Accuracy”, In: Proceedings of the Fifth SIAM International Conference on Data Mining, pp. 92–102, NewportBeach, CA, April 21–23, 2005.
L. Xiong, S. Chitti and L. Liu. k Nearest Neighbor Classification across Multiple Private Databases. CIKM’06, pp. 840–841, Arlington, Virginia, USA, November 5–11, 2006.
M. Kantarcioglu and J. Vaidya. Privacy preserving naïve Bayes classifierfor horizontally partitioned data. In IEEE ICDM Workshop on Privacy Preserving Data Mining, Melbourne, FL, pp. 3–9, November 2003.
ZhuojiaXu, Xun Yi, “Classification of Privacy-preserving Distributed Data Mining Protocols”, IEEE, 2011.
Jen-Wei Huang, Jun-Wei Su and Ming-Syan Chen, “FISIP: A Distance and Correlation Preserving Transformation for Privacy Preserving Data Mining” IEEE, 2011.
Shibnath Mukharjee, Zhiyuan Chen, Aryya Gangopadhyay, “A Privacy-preserving technique for Euclidean distance-based mining algorithms using Fourier-related transforms”, the VLDB Journal, pp (293–315), 2006.
ShutingXu, Jun Zhang, Dianwei Han, and Jie Wang, (2005) “Data distortion for privacy protection in a terrorist Analysis system”, P. Kantor et al (Eds.): ISI 2005, LNCS 3495, pp. 459–464.
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© 2016 Springer India
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Jalla, H.R., Girija, P.N. (2016). A Novel Approach for Horizontal Privacy Preserving Data Mining. In: Satapathy, S.C., Mandal, J.K., Udgata, S.K., Bhateja, V. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 434. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2752-6_9
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DOI: https://doi.org/10.1007/978-81-322-2752-6_9
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