SECURE CIVIC DATA BY PERTURBETED DATA METHOD USING WITH CLUSTERING
Keywords:
random distortion, Regenerate of Data, distribution reconstruction, information privacy, Perturbation Data, recovered dataAbstract
A key element in preserving privacy With the wide placement of public cloud computing infrastructures and confidentiality of sensitive data is the ability to evaluate the extent of all potential disclosure for such data using clouds to host data query facilities has become an appealing solution for the rewards on scalability and cost-saving.. In other words, we need to be able to answer to what extent confidential information in a perturbed database can be compromised by attackers or snoopers. That sensitive data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed. Several randomized techniques have been proposed for privacy preserving data mining of continuous data. This paper propose the clustering base data perturbation method to provide secure and effective range query services for protected data in the cloud. These approaches generally attempt to hide the sensitive data by randomly adapting the data values using some preservative noise and aim to rebuild the original distribution closely at an aggregate level. Secured query service should still provide effective query processing and significantly reduce the in-house workload to fully realize the assistances of cloud computing. The main contribution of this paper deceits in the algorithm to accurately perturbation and reconstruct the civic joint density given the perturbed multidimensional stream data information. Our research objective is to determine whether the distributions of the original and recovered data are close enough to each other despite the nature of the noise applied. Extensive experiments have been conducted to show the advantages of this approach on Efficiency and security. As the tool for the algorithm implementations we chose the “language of choice in industrial world” – MATLAB.
References
M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. K., and Andy Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, and M. Zaharia, “Above the clouds: A berkeley view of cloud computing,” Technical Report, University of Berkeley, 2009.
Z. Huang, W. Du, and B. Chen, “Deriving private information from randomized data,” in Proceedings of the ACM SIGMOD Conference on Management of Data, Baltimore, MA, 2005.
Mahdi Zamanighomi, Mostafa Zamanian, Michael Kimber, Zhengdao Wang, “Gene regulatory network inference from perturbed time-series expression data via ordered dynamical expansion of non-steady state actors,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, Sep. 29, 2014.
Fei Yu, Stephen E. Fienberg, Aleksandra B. Slavkovic, Caroline Uhler, “Scalable privacy-preserving data sharing methodology for genome-wide association studies,” Journal of Biomedical Informatics, Elsevier, pp. 133–141, 2014.
Yang, W. J., “Privacy protection by matrix transformation,” IEICE Transactions on Information and Systems, E92-D(4), pp. 740–741, 2009.
Liu, K., Kargupta, H., and Ryan, J., “Random projection based multiplicative data perturbation for privacy preserving distributed data mining,” IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 1, pp. 92–106, 2006.
Su, C. H., Zhan, J., and Sakurai, K., “Importance of Data Standardization in Privacy-Preserving K-Means Clustering,” in Proceedings of International Workshops on Database Systems for Advanced Applications, Brisbane, QLD, Australia, pp. 276–286, 2009.
Chong, Z. H., Ni, W. W., Liu, T. T., and Zhang, Y., “A privacy-preserving data publishing algorithm for clustering application,” Computer Research and Development, vol. 47, no. 12, pp. 2083–2089, 2010.
Raaele Giancarlo, Giosue Lo Bosco, Luca Pinello, “Distance functions, clustering algorithms and microarray data analysis,” in Proceedings of the 4th International Conference on Learning and Intelligent Optimization, Venice, Italy, pp. 125–138, 2010.
Liu, K., Giannella, C., and Kargupta, H., “A survey of Attack Techniques on Privacy-Preserving Data Perturbation Methods,” in Privacy-Preserving Data Mining: Models and Algorithms, 2008.
Giannella, C., and Liu, K., “On the Privacy of Euclidean Distance Preserving Data Perturbation,” Computer Science - Cryptography and Security, 2009.
Chen, K., Sun, G., and Liu, L., “Towards attack-resilient geometric data perturbation,” in Proceedings of the 2007 SIAM International Conference on Data Mining, Minneapolis, MN, 2007.
Huiqi Xu, Shumin Guo, Keke Chen, “Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation,” IEEE Transactions on Knowledge and Data Engineering, December 2012.
Mohammad, A. K., and Somayajulu, D.V.L.N., “A Noise Addition Scheme in Decision Tree for Privacy Preserving Data Mining,” Journal of Computing, vol. 2, no. 1, pp. 2151–9617, 2010.
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