Abstract
Data anonymization techniques enable publication of detailed information, while providing the privacy of sensitive information in the data against a variety of attacks. Anonymized data describes a set of possible worlds that include the original data. Generalization and suppression have been the most commonly used techniques for achieving anonymization. Some algorithms to protect privacy in the publication of set-valued data were developed by Terrovitis et al.,[16]. The concept of k-anonymity was introduced by Samarati and Sweeny [15], so that every tuple has at least (k-1) tuples identical with it. This concept was modified in [16] in order to introduce K m-anonymity, to limit the effects of the data dimensionality. This approach depends upon generalisation instead of suppression. To handle this problem two heuristic algorithms; namely the DA-algorithm and the AA-algorithm were developed by them.These alogorithms provide near optimal solutions in many cases.In this paper,we improve DA such that undesirable duplicates are not generated and using a FP-growth we display the anonymized data.We illustrate through suitable examples,the efficiency of our proposed algorithm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aggarwal, G., Feder, G., Kenthapadi, K., Khuller, S., Panigrahy, R., Thomas, D., Zhu, A.: Achieving Anonymity via Clustering. In: Proc. of ACM PODS, pp. 153–162 (2006)
Aggarwal, G., Feder, G., Kenthapadi, R., Motwani, R., Panigrahy, R., Thomas, D., Zhu, A.: Approximation Algorithms for k-Anonymity. Journal of Privacy Technology (2005)
Atzori, M., Bonchi, F., Giannotti, F., Pedreschi, D.: Anonymity Preserving Pattern Discovery. VLDB Journal (2008) (accepted for publication)
Bayardo, R.J., Agrawal, R.: Data Privacy through Optimal k-Anonymization. Proc. of ICDE, pp. 217–228 (2005)
Ghinita, G., Karras, F.P., Kalnis, P., Mamoulis, N.: Fast Data Anonymization with Low Information Loss. In: VLDB, pp. 758–769 (2007)
Ghinita, G., Tao, Y., Kalnis, P.: On the Anonymization of Sparse High-Dimensional Data. In: Proceedings of ICDE (2008)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proc. of ACM SIGMOD, pp. 1–12 (2000)
Iyengar, V.S.: Transforming Data to Satisfy Privacy Constraints. In: Proceedings of SIGKDD, pp. 279–288 (2002)
LeFevre, K., DeWitt, D.J., Ramakrishnan, R.: Incognito: Efficient Full-domain k-anonymity. In: Proceedings of ACM SIGMOD, pp. 49–60 (2005)
Li, N., Li, T., Venktasubramanian, S.: t-closeness Privacy Beyond k-anonymity and l-diversity. In: Proceedings of ICDE, pp. 106–115 (2007)
Machanavajjhala, A., Gehrke, J., Kifer, D., Venkitasubramaniam, S.: l-diversity: Privacy Beyond k-Anonymity. In: Proceedings of ICDE (2006)
Meyerson, A., Williams, R.: On the Complexity of Optimal k-Anonymity. In: Proceedings of ACM PODS, pp. 223–228 (2004)
Park, H., Shim, K.: Approximate algorithms for k-Anonymity. In: Proceedings of the ACM SIGMOD, pp. 67–78 (2007)
Samarati, P.: Protecting Respondents Identities in Microdata Release. IEEE TKDE 13(6), 1010–1027 (2001)
Sweeney, L.: K-Anonymity: A Model for Protecting Privacy. International Journal of Uncertainty. Fuzziness and Knowledge-Based Systems 10(5), 557–570 (2002)
Terrovitis, M., Mamoulis, N., Kalnis, P.: Privacy Preserving Anonymization of Set-Valued Data. In: PVLDB 2008, Auckland, New Zeland, pp. 115–125 (2008)
Tripathy, B.K., Devineni, H., Jayasri, K.J., Bhargava, M.: An Efficient Clustering Algorithm for l-diversity. In: Proceedings of the International Conference on Advances and Emerging Trends in Computing Technologies, ICAET 2010, June 21-24, pp. 76–81. SRM university (2010)
Tripathy, B.K., Panda, G.K., Kumaran, K.: A Rough Set Approach to develop an efficient l-diversity Algorithm based on Clustering. In: Proc. of the 2nd IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence, January 8-9, p. 34 (2011)
Tripathy, B.K., Panda, G.K., Kumaran, K.: A Fast l - Diversity Anonymisation Algorithm. In: Proc. of the Third International Conference on Computer Modelling and Simulation, ICCMS 2011, Mumbai, January 7-9, pp. V2-648–652(2011)
Tripathy, B.K., Maity, A., Ranajit, B., Chowdhuri, D.: A fast p-sensitive l-diversity Anonymisation algorithm. In: Proceedings of the RAICS IEEE Conference, Kerala, September 21-23, pp. 741–744 (2011)
Xiao, X., Tao, Y.: Anatomy: Simple and Effective Privacy Preservation. In: Proceedings of VLDB, pp. 139–150 (2006)
Zhang, Q., Koudas, N., Srivastava, D., Yu, T.: Aggregate Query Answering on Anonymised Tables. In: Proceedings of ICDE, pp. 116–125 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tripathy, B.K., Jayaram Reddy, A., Manusha, G.V., Mohisin, G.S. (2013). Improved Algorithms for Anonymization of Set-Valued Data. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol 177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31552-7_59
Download citation
DOI: https://doi.org/10.1007/978-3-642-31552-7_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31551-0
Online ISBN: 978-3-642-31552-7
eBook Packages: EngineeringEngineering (R0)