Abstract
This paper presents an index-based algorithm named SSAPP for exploring frequent sequential patterns in a distributed environment with privacy preservation. The SSAPP algorithm uses an equivalent form of a sequential pattern to reduce the number of cryptographic operations, such as decryption and encryption. In order to improve the efficiency of sequential pattern mining, the SSAPP algorithm keeps track of patterns in a tree data structure called SS-Tree. This tree is used to compress and represent sequences from a sequence database. Moreover, a SS-Tree allows one to obtain frequent sequential patterns without generation of candidate sequences. The conducted experiments show the effectiveness of the proposed approach. The SSAPP algorithm greatly reduces the number of cryptographic operations and it has good scalability.
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References
Goldreich, O., Micali, S., Wigderson, A.: How to Play any Mental Game or A Completeness Theorem for Protocols with Honest Majority. In: STOC, pp. 218–229 (1987)
Vaidya, J., Clifton, C.: Privacy preserving association rule mining in vertically partitioned data. In: KDD, pp. 639–644 (2002)
Clifton, C., Kantarcioglu, M., Vaidya, J., Lin, X., Zhu, M.Y.: Tools for Privacy Preserving Data Mining. SIGKDD Explorations, 28–34 (2002)
Kantarcioglu, M., Clifton, C.: Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data. In: DMKD, pp. 1026–1037 (2002)
Gorawski, M., Siedlecki, Z.: Optimization of Privacy Preserving Mechanisms in Homogeneous Collaborative Association Rules Mining. In: ARES, pp. 347–352 (2011)
Gorawski, M., Jureczek, P.: Optimization of privacy preserving mechanisms in mining continuous patterns. In: Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J. (eds.) New Results in Dependability & Comput. Syst. AISC, vol. 224, pp. 183–194. Springer, Heidelberg (2013)
Gorawski, M., Jureczek, P.: Extensions for Continuous Pattern Mining. In: Yin, H., Wang, W., Rayward-Smith, V. (eds.) IDEAL 2011. LNCS, vol. 6936, pp. 194–203. Springer, Heidelberg (2011)
Gorawski, M., Jureczek, P.: Continuous Pattern Mining Using the FCPGrowth Algorithm in Trajectory Data Warehouses. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds.) HAIS 2010, Part I. LNCS, vol. 6076, pp. 187–195. Springer, Heidelberg (2010)
Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: ICDE, pp. 3–14 (1995)
Qu, M.: Standards for efficient cryptography sec 2: Recommended elliptic curve domain parameters (2010)
Gorawski, M., Jureczek, P.: Regions of Interest in Trajectory Data Warehouse. In: Nguyen, N.T., Le, M.T., Świątek, J. (eds.) ACIIDS 2010, Part I. LNCS, vol. 5990, pp. 74–81. Springer, Heidelberg (2010)
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Gorawski, M., Jureczek, P. (2014). An Efficient Algorithm for Sequential Pattern Mining with Privacy Preservation. In: Swiątek, J., Grzech, A., Swiątek, P., Tomczak, J. (eds) Advances in Systems Science. Advances in Intelligent Systems and Computing, vol 240. Springer, Cham. https://doi.org/10.1007/978-3-319-01857-7_15
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DOI: https://doi.org/10.1007/978-3-319-01857-7_15
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01856-0
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