Abstract
Privacy preservation data is an emerging field of research in the data security. Numerous anonymization approaches have been proposed for privacy preservation such as generalization and bucketization. Recent works show that both the techniques are not suitable for high-dimensional data publishing. Several other challenges for data publishing are speed and computational complexity. Slicing is a novel anonymization technique which partitions the data both horizontally and vertically and solves the problem of high-dimensional complexity. To overcome the other challenges of speed and computational complexity, a new advanced clustering algorithm is used with slicing. This advanced clustering technique is used to partition the attributes into vertical columns and tuple grouping algorithm for horizontal partitioning. The experimental results confirm that the advanced clustering algorithm improves the speed of clustering accuracy, reduces the computational complexity, and the outcome of this work is resilient to membership, identity, and attributes disclosure.
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References
Machanavajjhala A, Kifer D, Gehrke J, Venkitasubramaniam ML (2007) Diversity: privacy beyond k-anonymity. ACM Trans Knowl Discov Data 1(1)
Sweeney L (2002) Achieving k-anonymity privacy protection using generalization and suppression. Int J Uncertain Fuzziness Knowl Based Syst 10(6):571–588
Li T, Li N, Zhang J,(2012) Slicing: a new approach for privacy preserving data publishing. In: Proceedings of IEEE transaction on knowledge and data mining engineering, vol 24(3), pp 561–574
Cramt’er H (1948) Mathematical methods of statistics. Princeton
MacQueen JB (1987) Some methods for classification and analysis of multivariate observation. In: Proceedings of 5th Berkeley symposium on mathematical statistics and probability, vol 1. University of California Press, Berkeley, pp 281–297
Kaufman L, Rousseeuw PJ (1990) Finding groups in data: an introduction to cluster analysis. Wiley, New York
Han J, Kamber M (2000) Data mining concepts and techniques. The Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann
Toor A (2014). An advanced clustering algorithm (ACA) for clustering large data set to achieve high dimensionality. Glob J Comput Sci Technol Softw Data Eng 14(2) (Version 1.0)
Asuncion A, Newman D (2007) UCI machine learning repository
Samarati P (2007) Protecting respondents’ identities in microdata release. IEEE Trans Knowl Data Eng 13(6):1010–1027
Xiao X, Tao Y (2006) Anatomy: simple and effective privacy preservation. In: Proceedings of the 32nd international conference on very large data bases, ser. VLDB, pp 139–150
Kifer D, Gehrke J (2006) Injecting utility into anonymized datasets. In: SIGMOD, pp 217–228
Nergiz ME, Atzori M, Clifton C (2007) Hiding the presence of individuals from shared databases. In SIGMOD, pp 665–676
Susan VS, Dr. Christopher T (2014) Slicing based privacy preserving of the micro data using fuzzy based clustering. Int J Appl Eng Res 9.
ROCK (2000) A robust clustering algorithm for categorical attributes. Inf Syst 255:345–366
Guha S, Rastogi R, Shim K (1988) CURE: an efficient clustering algorithm for large databases. In: Proceedings of ACM SIGMOD international conference management of data, pp 73–84
Karypis G, Han E, Kumar V (1999) Chameleon: hierarchical clustering using dynamic modeling. IEEE Comput 32(8):68–75
Zhang T, Ramakrishnan R, Livny M (1996) BIRCH: an efficient data clustering method for very large databases. In: Proceedings of ACM SIGMOD conference management of data, pp 103–114
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Shyamala Susan, V., Christopher, T. (2016). Advanced Cluster-Based Attribute Slicing: A New Approach for Privacy Preservation. In: Suresh, L., Panigrahi, B. (eds) Proceedings of the International Conference on Soft Computing Systems. Advances in Intelligent Systems and Computing, vol 398. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2674-1_21
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DOI: https://doi.org/10.1007/978-81-322-2674-1_21
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