Abstract
Recently, privacy issues have becomes important in data mining, especially when data is horizontally or vertically partitioned. For the vertically partitioned case, many data mining problems can be reduced to securely computing the scalar product. Among these problems, we can mention association rule mining over vertically partitioned data. Efficiency of a secure scalar product can be measured by the overhead of communication needed to ensure this security. Several solutions have been proposed for privacy preserving association rule mining in vertically partitioned data. But the main drawback of these solutions is the excessive overhead communication needed for ensuring data privacy. In this paper we propose a new secure scalar product with the aim to reduce the overhead communication.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Vaidya, J.S., 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, Alberta, Canada, July 23-26, pp. 639–644 (2002)
Oliveira, S.R.M., Zaiane, O., Saygin, Y.: Secure Association Rule Sharing. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS, vol. 3056, pp. 74–85. Springer, Heidelberg (2004)
Saygin, Y., Verykios, V., Clifton, C.: Using Unknowns to prevent discovery of Association Rules. ACM SIGMOD Record 30(4) (2001)
Guo, Y.H., Tong, Y.H., Tang, S.W., Yang, D.Q.: Knowledge hiding in database. Journal of Software 18(11), 2782–2799 (2007)
Yao Andrew, C.C.: Protocols for secure computations. In: Proc. of the 23rd Annual IEEE Symposium on Foundations of Computer Science, Chicago, Illinois, November 1982, pp. 160–164 (1982)
Yao, A.C.C.: How to generate and exchange secrets. In: Proc. of the 27th Symposium on Foundations of Computer Science (FOCS), Toronto, Canada, October 1986, pp. 162–167 (1986)
Goldreich, O.: Secure multi-party computation - working draft (2000), http://citeseer.ist.psu.edu/goldreich98secure.html
Du, W., Atallah, M.J.: Secure multi-party computation problems and their applications: a review and open problems. In: New Security Paradigms Workshop, Cloudcroft, New Mexico, September 2001, pp. 11–20 (2001)
Pinkas, B.: Cryptographic Techniques for Privacy-Preserving Data Mining. ACM SIGKDD Explorations 4(2) (2002)
Clifton, C., Kantarcioglu, M.: Tools for privacy preserving distributed data mining. SIGKDD Explorations 4(2), 28–34 (2003)
Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) Proceedings of the ACM SIGMOD International Conference on Management of Data, Washington, D.C., May 1993, pp. 207–216 (1993)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, Santiago, Chile, September 1994, pp. 487–499 (1994)
Goethals, B., Laur, S., Lipmaa, H., Mielikäinen, T.: On private scalar product computation for privacy-preserving data mining. In: Park, C.-s., Chee, S. (eds.) ICISC 2004. LNCS, vol. 3506, pp. 104–120. Springer, Heidelberg (2005)
Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592, pp. 223–238. Springer, Heidelberg (1999)
Amirbekyan, A., Estivill-Castro, V.: A New Efficient Privacy-Preserving Scalar Product Protocol. In: Proc. Sixth Australasian Data Mining Conference (AusDM 2007), Gold Coast, Australia (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Camara, F., Ndiaye, S., Slimani, Y. (2009). A Secure Protocol to Maintain Data Privacy in Data Mining. In: Huang, R., Yang, Q., Pei, J., Gama, J., Meng, X., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2009. Lecture Notes in Computer Science(), vol 5678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03348-3_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-03348-3_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03347-6
Online ISBN: 978-3-642-03348-3
eBook Packages: Computer ScienceComputer Science (R0)