Abstract
Privacy preserving association rule mining can extract important rules from distributed data with limited privacy breaches. Protecting privacy in incremental maintenance for distributed association rule mining is necessary since data are frequently updated. In privacy preserving data mining, scanning all the distributed data is very costly. This paper proposes a new incremental protocol for privacy preserving association rule mining using negative border concept. The protocol scans old databases at most once, and therefore reducing the I/O time. We also conduct experiments to show the efficiency of our protocol over existing ones.
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
References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proceedings of 20th International Conference on Very Large Databases, VLDB, pp. 487–499. Morgan Kaufmann (1994)
Ayan, N., Tansel, A., Arkun, E.: An efficient algorithm to update large itemsets with early pruning. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 287–291. ACM (1999)
Cheung, D., Han, J., Ng, V., Fu, A., Fu, Y.: A fast distributed algorithm for mining association rules. In: Fourth International Conference on Parallel and Distributed Information Systems, pp. 31–42. IEEE (1996)
Cheung, D., Han, J., Ng, V., Wong, C.: Maintenance of discovered association rules in large databases: an incremental updating technique. In: Proceedings of the Twelfth International Conference on Data Engineering, pp. 106–114. IEEE (1996)
Cheung, D.W., Lee, S.D., Kao, B.: A general incremental technique for maintaining discovered association rules. In: Proceedings of the Fifth International Conference On Database Systems For Advanced Applications, pp. 185–194 (1997)
Clifton, C., Kantarcioglu, M., Vaidya, J., Lin, X., Zhu, M.Y.: Tools for privacy preserving distributed data mining. SIGKDD Explor. Newsl. 4(2), 28–34 (2002)
Duan, Y., Canny, J.F., Zhan, J.Z.: Efficient privacy-preserving association rule mining: p4p style. In: CIDM, pp. 654–660. IEEE (2007)
Goldreich, O.: Secure multi-party computation. Manuscript (2002)
Han, S., Ng, W.-K.: Privacy-preserving genetic algorithms for rule discovery. In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2007. LNCS, vol. 4654, pp. 407–417. Springer, Heidelberg (2007)
Ioannidis, I., Grama, A.: An efficient protocol for yao’s millionaires’ problem. In: Proceedings of the 36th Annual Hawaii International Conference on System Sciences, p. 6, January 2003
Kantarcioglu, M., Clifton, C.: Privacy-preserving distributed mining of association rules on horizontally partitioned data. IEEE Trans. Knowl. Data Eng. 16(9), 1026–1037 (2004)
Lee, C., Lin, C., Chen, M.: Sliding-window filtering: an efficient algorithm for incremental mining. In: Proceedings of the tenth international conference on Information and knowledge management, pp. 263–270. ACM (2001)
Mannila, H., Toivonen, H.: On an algorithm for finding all interesting sentences. In: Cybernetics and Systems, Volume II, The Thirteenth European Meeting on Cybernetics and Systems Research. Citeseer (1996)
Park, J.S., Chen, M.-S., Yu, P.S.: An effective hash-based algorithm for mining association rules. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 1995, pp. 175–186. ACM, New York (1995)
Tassa, T.: Secure mining of association rules in horizontally distributed databases. IEEE Trans. Knowl. Data Eng. 26(4), 970–983 (2014)
Thomas, S., Bodagala, S., Alsabti, K., Ranka, S.: An efficient algorithm for the incremental updation of association rules in large databases. In: Knowledge Discovery and Data Mining, pp. 263–266 (1997)
Vaidya, J., Clifton, C.: Secure set intersection cardinality with application to association rule mining. J. Comput. Secur. 13(4), 593–622 (2005)
Veloso, A., Meira Jr., W., De Carvalho, M., Pôssas, B., Parthasarathy, S., Zaki, M.: Mining frequent itemsets in evolving databases. In: SIAM International Conference on Data Mining (2002)
Wong, W.K., Cheung, D.W., Hung, E., Liu, H.: Protecting privacy in incremental maintenance for distributed association rule mining. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 381–392. Springer, Heidelberg (2008)
Yao, A.C.: Protocols for secure computations. In: SFCS 1982: Proceedings of the 23rd Annual Symposium on Foundations of Computer Science, pp. 160–164. IEEE Computer Society, Washington (1982)
Yao, A.C.: How to generate and exchange secrets. In: Proceedings of the Annual IEEE Symposium on Foundations of Computer Science, pp. 162–167 (1986)
Acknowledgments
This research is supported by the Singapore Maritime Institute.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Tran, D.H., Ng, W.K., Wong, Y.D., Thai, V.V. (2016). Incremental Privacy-Preserving Association Rule Mining Using Negative Border. In: Chau, M., Wang, G., Chen, H. (eds) Intelligence and Security Informatics. PAISI 2016. Lecture Notes in Computer Science(), vol 9650. Springer, Cham. https://doi.org/10.1007/978-3-319-31863-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-31863-9_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-31862-2
Online ISBN: 978-3-319-31863-9
eBook Packages: Computer ScienceComputer Science (R0)