Abstract
As a result of advances in technology, large amounts of data can be collected and stored automatically. Significant development of the Internet and easier access to it have contributed to collecting large amounts of information about users’ characteristics. Along with these changes, concerns about privacy of data have emerged. Several methods of preserving privacy for association rules mining have been proposed in literature: MASK scheme and its optimizations. This paper provides new solutions concerning efficiency for this scheme and considers different methods of distorting data using randomization techniques. Effectiveness of these solutions has been tested and presented in this paper.
Research has been supported by grant No 3 T11C 002 29 received from Polish Ministry of Education and Science.
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.
Similar content being viewed by others
References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) VLDB’94. Proceedings of 20th International Conference on Very Large Data Bases, Santiago de Chile, Chile, September 12-15, 1994, pp. 487–499. Morgan Kaufmann, San Francisco (1994)
Agrawal, S., Krishnan, V., Haritsa, J.R.: On addressing efficiency concerns in privacy preserving data mining. CoRR, cs.DB/0310038 (2003)
Atallah, M.J., Bertino, E., Elmagarmid, A.K., Ibrahim, M., Verykios, V.S.: Disclosure limitation of sensitive rules. In: Proceedings of the IEEE Knowledge and Data Engineering Workshop, pp. 45–52. IEEE Computer Society Press, Los Alamitos (1999)
Dasseni, E., Verykios, V.S., Elmagarmid, A.K., Bertino, E.: Hiding association rules by using confidence and support. In: Moskowitz, I.S. (ed.) Information Hiding. LNCS, vol. 2137, pp. 369–383. Springer, Heidelberg (2001)
Evfimievski, A., Srikant, R., Agrawal, R., Gehrke, J.: Privacy preserving mining of association rules. In: KDD ’02. Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 217–228. ACM Press, New York (2002)
Kantarcioglu, M., Clifton, C.: Privacy-preserving distributed mining of association rules on horizontally partitioned data. In: DMKD (2002)
Oates, T., Jensen, D.: Large datasets lead to overly complex models: An explanation and a solution. In: KDD, pp. 294–298 (1998)
Rizvi, S., Haritsa, J.R.: Maintaining data privacy in association rule mining. In: VLDB, pp. 682–693. Morgan Kaufmann, San Francisco (2002)
Saygin, Y., Verykios, V.S., Elmagarmid, A.K.: Privacy preserving association rule mining. In: RIDE, pp. 151–158 (2002)
Srikant, R., Agrawal, R.: Mining generalized association rules. In: Dayal, U., Gray, P.M.D., Nishio, S. (eds.) VLDB, pp. 407–419. Morgan Kaufmann, San Francisco (1995)
Srikant, R., Agrawal, R.: Mining quantitative association rules in large relational tables. In: Jagadish, H.V., Mumick, I.S. (eds.) SIGMOD Conference, pp. 1–12. ACM Press, New York (1996)
Vaidya, J., Clifton, C.: Privacy preserving association rule mining in vertically partitioned data. In: KDD ’02. Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 639–644. ACM Press, New York (2002)
Verykios, V.S., Bertino, E., Fovino, I.N., Provenza, L.P., Saygin, Y., Theodoridis, Y.: State-of-the-art in privacy preserving data mining. SIGMOD Record 33(1), 50–57 (2004)
Xia, Y., Yang, Y., Chi, Y.: Mining association rules with non-uniform privacy concerns. In: Das, G., Liu, B., Yu, P.S. (eds.) DMKD, pp. 27–34. ACM Press, New York (2004)
Yücel Saygin, C.C., Verykios, V.S.: Using unknowns to prevent discovery of association rules. SIGMOD Record 30(4), 45–54 (2001)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Andruszkiewicz, P. (2007). Optimization for MASK Scheme in Privacy Preserving Data Mining for Association Rules. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds) Rough Sets and Intelligent Systems Paradigms. RSEISP 2007. Lecture Notes in Computer Science(), vol 4585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73451-2_49
Download citation
DOI: https://doi.org/10.1007/978-3-540-73451-2_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73450-5
Online ISBN: 978-3-540-73451-2
eBook Packages: Computer ScienceComputer Science (R0)