Abstract
The exponential growth of databases containing personal information has rendered the task of extracting high quality information from collections of such databases very important. This task is hindered by the security concerns that arise, due to the confidentiality of the data records, and the reluctance of the organizations to disclose their data. This paper proposes a clustering algorithmic scheme that ensures privacy and confidentiality of the data without compromising the effectiveness of the clustering algorithm nor imposing high communication costs.
Partially supported by the “Archimedes” research programme awarded by the Greek Ministry of Education and Religious Affairs and the European Union.
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
Adam, N.R., Worthmann, J.C.: Security-control methods for statistical databases: a comparative study. ACM Comput. Surv. 21(4), 515–556 (1989)
Agrawal, R., Evfimievski, A., Srikant, R.: Information sharing across private databases. In: Proc. of the ACM SIGMOD International Conference on Management of Data, pp. 86–97 (2003)
Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: Proc. of the ACM SIGMOD Conference on Management of Data, pp. 439–450. ACM Press, New York (2000)
Domingo-Ferrer, J., Mateo-Sanz, J.M.: Current directions in statistical data protection. Research in Official Statistics 1(2), 105–112 (1998)
Evfimievski, A., Srikant, R., Agarwal, R., Gehrke, J.: Privacy preserving mining of association rules. Inf. Syst. 29(4), 343–364 (2004)
Freedman, M., Nissim, K., Pinkas, B.: Efficient private matching and set intersection. In: Advances in Cryptology – EUROCRYPT 2004 (2004)
Goldreich, O.: Secure multi-party computation. Working Draft, Ver.1.4 (2002)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, Heidelberg (2001)
Kantarcioglu, M., Vaidya, J.: An architecture for privacy-preserving mining of client information. In: Clifton, C., Estivill-Castro, V. (eds.) IEEE International Conference on Data Mining Workshop on Privacy, Security, and Data Mining, vol. 14, pp. 37–42. Australian Computer Society (2002)
Lindell, Y., Pinkas, B.: Privacy preserving data mining. In: Bellare, M. (ed.) CRYPTO 2000. LNCS, vol. 1880, pp. 36–54. Springer, Heidelberg (2000)
Merugu, S., Ghosh, J.: Privacy-preserving distributed clustering using generative models. In: Third IEEE International Conference on Data Mining (2003)
Naor, M., Pinkas, B.: Oblivious transfer and polynomial evaluation. In: Proc. of the 31st Annual ACM Symposium on Theory of Computing, pp. 245–254 (1999)
Oliveira, S.R.M., Zaane, O.R.: Privacy preserving clustering by data transformation. In: 18th Brazilian Symposium on Databases, pp. 304–318 (2003)
Preparata, F., Shamos, M.: Computational Geometry. Springer, Heidelberg (1985)
Tasoulis, D.K., Vrahatis, M.N.: Unsupervised distributed clustering. In: Proc. of the IASTED International Conference on Parallel and Distributed Computing and Networks, Innsbruck, Austria, pp. 347–351 (2004)
Tasoulis, D.K., Vrahatis, M.N.: Unsupervised clustering on dynamic databases. Pattern Recognition Letters 26(13), 2116–2127 (2005)
Tasoulis, D.K., Vrahatis, M.N.: Novel approaches to unsupervised clustering through the k-windows algorithm. In: Sirmakessis, S. (ed.) Knowledge Mining. Series Studies in Fuzziness and Soft Computing, vol. 185. Springer, Heidelberg (2005)
Vaidya, J., Clifton, C.: Privacy preserving association rule mining in vertically partitioned data. In: The Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, pp. 639–644 (2002)
Vaidya, J., Clifton, C.: Privacy preserving k-means clustering over vertically partitioned data. In: The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2003)
Vrahatis, M.N., Boutsinas, B., Alevizos, P., Pavlides, G.: The new k-windows algorithm for improving the k-means clustering algorithm. Journal of Complexity 18, 375–391 (2002)
Yao, A.C.: Protocols for secure computations. In: Proceedings of the 23rd Annual IEEE Symposium on Foundations of Computer Science, pp. 160–164 (1982)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tasoulis, D.K., Laskari, E.C., Meletiou, G.C., Vrahatis, M.N. (2006). Privacy Preserving Unsupervised Clustering over Vertically Partitioned Data. In: Gavrilova, M.L., et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3984. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751649_70
Download citation
DOI: https://doi.org/10.1007/11751649_70
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34079-9
Online ISBN: 978-3-540-34080-5
eBook Packages: Computer ScienceComputer Science (R0)