Abstract
Privacy preservation is a big challenge in data mining. The protection of sensitive information becomes a critical issue when releasing data to outside parties. Association rule mining could be very useful in such situations. It could be used to identify all the possible ways by which ‘non-confidential’ data can reveal ‘confidential’ data, which is commonly known as ‘inference problem’. This issue is solved using Association Rule Hiding (ARH) techniques in Privacy Preserving Data Mining (PPDM). Association rule hiding aims to conceal these association rules so that no sensitive information can be mined from the database.
This paper proposes a model for hiding sensitive association rules. The model is implemented with a Fast Hiding Sensitive Association Rule (FHSAR) algorithm using the java eclipse framework. The implemented algorithm is integrated with a Weka open source data mining tool. Model analysis and evaluation shows its efficiency by balancing the trade-off between utility and privacy preservation in data mining with minimal side effects.
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References
Han, J., Kamber, M., Pei, J.: Data mining: concepts and techniques: concepts and techniques. Elsevier, New York (2011)
Parmar, S., Gupta, M.P., Sharma, M.P.: A comparative study and literature survey on privacy preserving data mining techniques. IJCSMC 4, 480–486 (2015)
Giannotti, F., Lakshmanan, L.V., Monreale, A., Pedreschi, D., Wang, H.: Privacy-preserving mining of association rules from outsourced transaction databases. Syst. J. IEEE 7(3), 385–395 (2013)
Upadhyay, N., Tripathi, K., Mishra, A.: A survey of association rule hiding approaches. IJCSITS 5, 190–195 (2015)
Ranjan, J.: Business intelligence: Concepts, components, techniques and benefits. J. Theoret. Appl. Inf. Technol. 9(1), 60–70 (2009)
Aggarwal, C.C., Han, J.: Frequent Pattern Mining. Springer, Switzerland (2014)
Chhatrapati, M., Serasiya, S.A.: Research on Privacy Preserving Data Mining Using Heuristic Approach. IJRST 1, 113–117 (2015)
Chopra, J., Satav, S.: Privacy preservation techniques in data mining. IJRET 2, 537–541 (2013)
Kaur, K., Bansal, M.: A review on various techniques of hiding association rules in privacy preservation data mining. IJECS 4(6), 12947–12951 (2015)
Ghalehsefidi, N.J., Dehkordi, M.N.: A survey on privacy preserving association rule mining (2015)
Weng, C.-C., Chen, S.-T., Lo, H.-C.: A novel algorithm for completely hiding sensitive association rules. In: IEEE (2008)
Shah, K., Thakkar, A., Ganatra, A.: A study on association rule hiding approaches. IJEAT Int. J. Eng. Adv. Technol. 3, 72–76 (2012)
Acknowledgments
We deeply thank the University of Khartoum and special thanks to the Faculty of Mathematical Sciences for their unwavering support.
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Mogtaba, S., Kambal, E. (2016). Association Rule Hiding for Privacy Preserving Data Mining. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2016. Lecture Notes in Computer Science(), vol 9728. Springer, Cham. https://doi.org/10.1007/978-3-319-41561-1_24
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DOI: https://doi.org/10.1007/978-3-319-41561-1_24
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