Abstract
In the real world, most of the entities are involved with space and time, from any starting point to the end point of the space. The conventional data mining process is extended to the mining knowledge of the spatiotemporal databases. The major knowledge is to mine the association rules in the spatiotemporal databases; the traditional approaches are not sufficient to do mining in the spatiotemporal databases. While mining the association rules, the privacy is the main concern. This paper proposed privacy preserved data mining technique for spatiotemporal databases based on the mining negative association rules and cryptography with low storage and communication cost. In the proposed approach first, the partial support for all the distributed sites is calculated, and then finally, the actual support was calculated to achieve privacy preserve data mining. The mathematical calculation was done and proved that this approach is best for mining association rules for spatiotemporal databases.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Getta, J.R., McKerrow, L., McKerrow, P.J.: The application of database mining techniques to data fusion in spatial databases. In: Proceeding of 1st Australian Data Fusion Symposium, pp. 135–140. IEEE (1996)
Sahu, A.K., Kumar, R., Rahim, N.: Mining negative association rules in distributed environment. In: Proceedings International Conference on Computational Intelligence and Communication Networks (CICN), pp. 934–937. IEEE (2015)
Zhang, X., Su, F., Du, Y., Shi, Y.: Association rule mining on spatio-temporal processes. In: Proceedings 4th International Conference on Wireless Communications, Networking and Mobile Computing, pp. 1–4. IEEE (2008)
Cheung, D.W., Ng, V.T., Fu, A.W., Fu, Y.: Efficient mining of association rules in distributed databases. IEEE Trans. Knowl. Data Eng. 1(6), 911–922 (1996)
Neerugatti, V., Reddy, R.M.: A survey on secure connectivity techniques for internet of things environment. Int. J. Eng. Res. Comput. Sci. Eng. (IJERCSE) 4(3) (2017)
Cheung, D.W., Han, J., Ng, V.T., Fu, A.W., 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)
Chen, M.S., Han, J., Yu, P.S.: Data mining: an overview from a database perspective. IEEE Trans. Knowl. Data Eng. 8(6), 866–883 (1996)
Abraham, T., Roddick, J.F.: Survey of spatio-temporal databases. GeoInformatica 3(1), 61–99 (1999)
Wang, C., Huang, H., Li, H.: A fast distributed mining algorithm for association rules with item constraints. In: SMC 2000 Conference Proceedings. 2000 IEEE International Conference on Systems, Man and Cybernetics’ Cybernetics Evolving to Systems, Humans, Organizations, and Their Complex Interactions, vol. 3(1), pp. 1900–1905. IEEE (2000)
Verykios, V.S., Bertino, E., Fovino, I.N., Provenza, L.P., Saygin, Y., Theodoridis, Y.: State-of-the-art in privacy preserving data mining. ACM Sigmod Rec. 33(1), 50–57 (2004)
Bertino, E., Fovino, I.N., Provenza, L.P.: A framework for evaluating privacy preserving data mining algorithms. Data Min. Knowl. Disc. 11(2), 121–154 (2005)
Chang, C.C., Yeh, J.S., Li, Y.C.: Privacy-preserving mining of association rules on distributed databases (2006)
Kotsiantis, S., Kanellopoulos, D.: Association rules mining: A recent overview. GESTS Int. Trans. Comput. Sci. Eng. 32(1), 71–82 (2006)
Neerugatti, V., Reddy, R.M.: An introduction, reference models, applications, open challenges in Internet of Things. Int. J. Mod. Sci. Eng. Technol. (IJMSET) (2017)
Andrienko, G., Malerba, D., May, M., Teisseire, M.: Mining spatio-temporal data. J. Intell. Inf. Syst. 27(3), 187–190 (2006)
Wang, L., Xie, K., Chen, T., Ma, X.: Efficient discovery of multilevel spatial association rules using partitions. Inf. Softw. Technol. 47(13), 829–840 (2005)
Wang, J., Luo, Y., Zhao, Y., Le, J.: A survey on privacy preserving data mining. In: Proceeding First International Workshop on Database Technology and Applications, pp. 111–114. IEEE (2009)
Gurevich, A., Gudes, E.: Privacy preserving data mining algorithms without the use of secure computation or perturbation. In: Proceeding 10th International Database Engineering and Applications Symposium (IDEAS’06), pp. 121–128. IEEE (2006)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings VLDB Conference, Santiago, pp. 487–499. IEEE (1994)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ranjith, K.S., Geetha Mary, A. (2020). Privacy-Preserving Data Mining in Spatiotemporal Databases Based on Mining Negative Association Rules. In: Venkata Krishna, P., Obaidat, M. (eds) Emerging Research in Data Engineering Systems and Computer Communications. Advances in Intelligent Systems and Computing, vol 1054. Springer, Singapore. https://doi.org/10.1007/978-981-15-0135-7_32
Download citation
DOI: https://doi.org/10.1007/978-981-15-0135-7_32
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0134-0
Online ISBN: 978-981-15-0135-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)