Abstract
Use of published organizational data for a variety of purposes has the chance of violation of leakage of individual secret information. Preliminary efforts in this direction are susceptible to leakage of valuable information through quasi identifiers. Over the past few years, several algorithms based upon the concept of k-anonymity [1-2] have been developed, to handle such problems. A better privacy model, called l-diversity [3] was proposed to handle some of the problems in k-anonymity. Our main contribution in this paper is to improve the clustering phase of the OKA algorithm [4] so that it takes care of k-anonymity and l-diversity to a considerable extent and in combination with the improved second and third phases of the algorithm in [5] leads to an efficient l-diversity algorithm. We also show that all the three stages of the algorithm are necessary in order to cover different situations.
This is a preview of subscription content, log in via an institution to check access.
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
Agrawal, R., Bayardo, R.: Data Privacy through Optimal k-Anonymization. In: Proc. of the 21st International Conference on Data Engineering, pp. 217–218 (2005)
Chiu, C.C., Tsai, C.Y.: A k-Anonymity Clustering Method for Effective Data Privacy Preservation. In: Third International Conference on Advanced Data Mining and Applications, ADMA (2007)
Machanavajjhala, A., Gehrke, J., Kifer, D., Venkitasubramaniam, M.: l-Diversity: Privacy beyond k-Anonymity. In: Proc. 22nd International Conference Data Engineering (ICDE), vol. 24 (2006)
Byun, J.W., Kamra, A., Bertino, E., Li, N.: Efficient k-Anonymization using Clustering Techniques. In: Kotagiri, R., Radha Krishna, P., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 188–200. Springer, Heidelberg (2007)
Tripathy, B.K., Panda, G.K., Kumaran, K.: A Fast l - Diversity Anonymisation Algorithm. In: Proc. of the Third International Conference on Computer Modeling and Simulation (ICCMS 2011), Mumbai, January 7-9, vol. 2, pp. 648–652 (2011)
Samarati, P., Foresti, S., Vimercati, S.D.C.D., Ciriani, V.: k-Anonymity. In: Advances in Information Security, Springer, Heidelberg (2007)
Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining, pp. 487–559. Addison-Wesley, Boston (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
B.K., T., K., K., G.K., P. (2011). An Improved l-Diversity Anonymisation Algorithm. In: Venugopal, K.R., Patnaik, L.M. (eds) Computer Networks and Intelligent Computing. ICIP 2011. Communications in Computer and Information Science, vol 157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22786-8_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-22786-8_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22785-1
Online ISBN: 978-3-642-22786-8
eBook Packages: Computer ScienceComputer Science (R0)