Abstract
Distribution of data in the organizations which are having cooperative business is a common scenario for getting the benefits in the business. Modern technology in data mining has permitted to extract the unknown patterns from the repositories of enormous data. On the other hand, it raises problem of revealing the confidential patterns when the data is shared to the others. Privacy-preserving data mining is an emerging area for the research in the domain of security to deal with the need privacy for concerns of confidential patterns. The original database is to be transformed to conceal the confidential patterns. Along with concealing the confidential patterns, another important parameter that is to be addressed is attaining the balance between privacy and utility of the database which are generally inversely proportional to each other. Another challenging aspect in the transformation process is reducing the side effects, miss cost, and false rules that may occur by mining the transformed database. In this paper, a new method has been projected for concealing of association rules that are sensitive by carefully selecting the transactions for transformation using computational intelligence technique social group optimization. The outcome of the proposed approach is measured against the existing techniques based on computational intelligence methods to demonstrate the comparison of side effects with the proposed method.
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References
Amiri, A.: Dare to share: protecting sensitive knowledge with data sanitization. Decis. Support Syst. 43(1), 181–191 (2007)
Askari, M., Safavi-Naini, R., Barker, K.: An information theoretic privacy and utility measure for data sanitization mechanisms. In: Proceedings of the second ACM conference on Data and Application Security and Privacy, pp. 283–294. ACM (2012)
Atallah, M., Bertino, E., Elmagarmid, A., Ibrahim, M., Verykios, V.: Disclosure limitation of sensitive rules. In: Knowledge and Data Engineering Exchange, 1999.(KDEX’99) Proceedings. 1999 Workshop on, pp. 45–52. IEEE (1999)
Bonam, J., Reddy, A.R. Kalyani, G.: Privacy preserving in association rule mining by data distortion using pso. In: ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India-Vol II, pp. 551–558. Springer (2014)
Bonam, J., Reddy, R.: Balanced approach for hiding sensitive association rules in data sharing environment. Int. J. Inf. Sec. Priv. 8(3), 39–62 (2014)
Chang L., Moskowitz, I.S.: Parsimonious downgrading and decision trees applied to the inference problem. In: Proceedings of the 1998 workshop on New security paradigms, pp. 82–89. ACM (1998)
Dasseni, E., Verykios, V.S. Ahmed K Elmagarmid, and Elisa Bertino. Hiding association rules by using confidence and support. In International Workshop on Information Hiding, pp. 369–383. Springer (2001)
Evfimievski, A., Srikant, R., Agrawal, R., Gehrke, J.: Privacy preserving mining of association rules. Inf. Syst. 29(4), 343–364 (2004)
Kalyani, G., Chandra Sekhara Rao, M.V.P., Janakiramaiah, B.: Privacy-preserving classification rule mining for balancing data utility and knowledge privacy using adapted binary firefly algorithm. Arabian J. Sci. Eng. (2017)
Kalyani, G., Chandra Sekhara Rao, M.V.P., Janakiramaiah, B.: Decision tree based data reconstruction for privacy preserving classification rule mining. Informatica 41(3) (2017)
Kalyani, G., Chandra Sekhara Rao, M.V.P., Janakiramaiah, B.: Particle swarm intelligence and impact factor-based privacy preserving association rule mining for balancing data utility and knowledge privacy. Arabian J. Sci. Eng. 1–18 (2017)
Lin, Chun-Wei, Hong, Tzung-Pei, Yang, Kuo-Tung, Wang, Shyue-Liang: The ga-based algorithms for optimizing hiding sensitive itemsets through transaction deletion. Appl. Intell. 42(2), 210–230 (2015)
Lin, C.-W., Zhang, B., Yang, K.-T., Hong, T.-P.: Efficiently hiding sensitive itemsets with transaction deletion based on genetic algorithms. Sci. World J. (2014)
Oliveira, S.R.M., Zaïane, O.R.: Protecting sensitive knowledge by data sanitization. In: Data Mining, 2003. ICDM 2003. Third IEEE International Conference on, pp. 613–616. IEEE (2003)
Satapathy, Suresh, Naik, Anima: Social group optimization (sgo): a new population evolutionary optimization technique. Complex & Intell. Syst. 2(3), 173–203 (2016)
Saygin, Yücel, Verykios, V.S., Clifton, C.: Using unknowns to prevent discovery of association rules. Acm. Sigmod Rec. 30(4), 45–54 (2001)
Saygin, Y., Verykios, V.S., Elmagarmid, A.K.: Privacy preserving association rule mining. In Research Issues in Data Engineering: Engineering E-Commerce/E-Business Systems, 2002. RIDE-2EC 2002. Proceedings. Twelfth International Workshop on, pp. 151–158. IEEE (2002)
Verykios, V.S., Elmagarmid, A.K., Bertino, E., Saygin, Y., Dasseni, E.: Association rule hiding. IEEE Trans. Knowledge Data Eng. 16(4), 434–447 (2004)
Wu, J.M.-T., Zhan, J., Lin, C.-W.: Ant colony system sanitization approach to hiding sensitive itemsets. IEEE Access (2017)
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Janakiramaiah, B., Kalyani, G., Chittineni, S., Narendra Kumar Rao, B. (2019). An Unbiased Privacy Sustaining Approach Based on SGO for Distortion of Data Sets to Shield the Sensitive Patterns in Trading Alliances. In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 105. Springer, Singapore. https://doi.org/10.1007/978-981-13-1927-3_17
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DOI: https://doi.org/10.1007/978-981-13-1927-3_17
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