Abstract
Sharing of data provides mutual benefits for collaborating organizations. Data mining techniques have allowed regimented discovery of knowledge from huge databases. Conversely, in the case of sharing the data with others, knowledge discovery raises the possibility of revealing the sensitive knowledge. The need of privacy prompted the growth of numerous privacy-preserving data mining techniques. In order to deal with privacy concerns, the database is to be transformed into another database in such a way that the sensitive knowledge is concealed. One subarea of privacy-preserving data mining, which got attention in retail businesses, is privacy-preserving association rule mining. A significant feature of privacy-preserving association rule mining is attaining a balance between privacy and precision, which is characteristically conflicting, and refining the one generally reduces the other one. In this paper, the problem has been planned in the perspective of protecting association rules which are sensitive by prudently amending the transactions of the database. To moderate the loss of non-sensitive association rules and to improve the quality of the transformed database, the proposed approach competently estimates the impact of an alteration to the database. The proposed method selects the transactions for alterations using the binary TLBO optimization technique during the concealing process. Experimental outcomes exhibit the efficiency of the proposed algorithm.
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
Similar content being viewed by others
References
A. Amiri. Dare to share: Protecting sensitive knowledge with data sanitization. Decision Support Systems, 2007.
M.V.P. Chandra Sekhar Rao. Ch Aparna. Dr B Raveendra Babu. Dr A Damodaram. An improved multi-perceptron neural network model to classify software defects. International Journal on Computer Science and Information Security (IJCSIS), 2011.
V S Verkios. E Bertino. L P Provenza. Y Saygin. E. Dasseni. Association rule hiding. IEEE Transactions on on Knowledge and Data Engineering, 2004.
Agrawal R Gehrke. J Evfimievski. A, Srikant.R. Privacy preserving mining of association rules. In: Proceedings of SIGKDD Conference, 2002.
Yang Kuo-Tung Wang Shyue-Liang Hong Tzung-Pei, Lin Chun-Wei. Using tf-idf to hide sensitive itemsets. Applied Intelligence, 2013.
Le Hai Quoc. Arch int Sojit. Nguyen Huy Xuan. Association rule hiding in risk management for retail supply chain collaboration. Computer in Industry, 2013.
J Bonam. A R Reddy. G Kalyani. Privacy preserving in association rule mining by data distortion using pso. Proc, ICT Critical Infrastruct., 2014.
R Venkata Rao. Review of applications of tlbo algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems. Decision Science Letters, GrowingScience, 2016.
R V Rao. V J Savsani. Teacher learning-basd optimization: a novel method for constrained mechanical design opttimization problems. Comput. Aided Des., 2011.
Xun Yi, Fang-Yu Rao, Elisa Bertino, and Athman Bouguettaya. Privacy-preserving association rule mining in cloud computing. In Proceedings of the 10th ACM Symposium on Information, Computer and Communications Security., 2015.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kalyani, G., Chandra Sekhara Rao, M.V.P., Janakiramaiah, B. (2017). Privacy-Preserving Association Rule Mining Using Binary TLBO for Data Sharing in Retail Business Collaboration. In: Satapathy, S., Bhateja, V., Udgata, S., Pattnaik, P. (eds) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications . Advances in Intelligent Systems and Computing, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-10-3153-3_19
Download citation
DOI: https://doi.org/10.1007/978-981-10-3153-3_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3152-6
Online ISBN: 978-981-10-3153-3
eBook Packages: EngineeringEngineering (R0)