Abstract
In recent days, enormous data are generated from departmental stores, hospitals, social media, banks, etc. These datasets are associated with different association rules for monitoring the business operations. During this process, to avoid leaking of sensitive information leads to development of association rule hiding algorithms. Many heuristic algorithms are developed but they are limited to optimal solutions. In this paper, an efficient meta-heuristic algorithm has been developed for association rule hiding based on chemical reaction optimization algorithm. The results of the proposed approach are compared with the genetic algorithm, particle swarm optimization, and cuckoo-based algorithms. The experimental results of the proposed algorithm are tested on the benchmark datasets.
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
References
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM SIGMOD International Conference on Management of Data SIGMOD, p. 207 (1993)
Hahsler, M., Grun, B., Hornik, K.: Arules—A computational environment for mining association rules and frequent itemsets. J. Stat. Softw. (2005)
Tan, P.-N., Steinbach, M., Kumar, V.: Association analysis: basic concepts and algorithms. In: Introduction to Data Mining (2005)
Lam, A.Y.S., Li, V.O.K.: Chemical reaction optimization (2012)
Lam, A.Y.S., Li, V.O.K.: Real Coded Chemical reaction optimization. IEEE Trans. Evol. Comput. 16(3), 339–353 (2012)
Atallah, M., Bertino, E., Elmagarmid, A., Ibrahim, M., Verykios, V.: Disclosure limitation of sensitive rules, pp. 45–52 (1999)
Dasseni, E., Verkios, V.S., Elmagarmid, A.K., Bernito, E.: Hiding association rules by using confidence and support, pp. 369–383 (2000)
Verykios, V.S., Elmagarmid, A.K., Bertino, E., Saygin, Y., Dasseni, E.: Association rule hiding. Trans. Knowl. Data Eng. 16(4), 434–447 (2004)
Oliveira, S.R.M., Zaiane, O.R.: Privacy preserving frequent itemset mining (2002)
Wang: Hiding informative association rules sets. Expert Syst. Appl. 33(2), 316–323 (2007)
Shah, K., Thakkar, A., Ganatra, A.: Association rule hiding by heuristic approach to reduce side effects and hide multiple R.H.S. Int. J. Comput. Appl. 45(1), 1–7 (2012)
Damandiya, N.H., Rao, U.P.: Hiding sensitive association rules to maintain privacy and data quality in database, pp. 1306–1310 (2013)
Hong, T.-P., Lin, C.-W., Yang, K.-T., Wang, S.L.: A heuristic data-sanitization approach based on SIF-IDF (2011)
Lin, C.-W., Hong, T.-P., Hsu, H.C.: Reducing side effects of hiding sensitive item sets in privacy preserving data mining. Sci. World J. (2014)
Ghalehsefidi, N.J., Dehkordi, M.N.: A hybrid algorithm based on heuristic method to preserve privacy in association rule mining. Indian J. Sci. Technol. 9(27) (2016)
Belwal, R., Varshney, J., Khan, S.: Hiding sensitive association rules efficiently by introducing new variable hiding counter (2013)
Modi, C.N., Rao, U.P., Patel, D.R.: Maintaining privacy and data quality in privacy preserving association rule mining (2010)
Modi, C.N., Rao, U.P., Patel, D.R.: An efficient solution for privacy preserving association rule mining. Int. J. Comput. Netw. 2(5), 79–85 (2010)
Kesava Murthy, B., Khan, A.M.: Privacy preserving association rule mining over distributed databases using genetic algorithm. Neural Comput. Appl. 22(1), S351–S364 (2013)
Lin, J.C.W,: A sanitization approach for hiding sensitive Itemsets based on particle swarm optimization. Eng. Appl. Artif. Intell. 53, 1–18 (2016)
Afshari, M.H.: Association rule hiding using cuckoo optimization algorithm. Expert Syst. Appl. 64,340–351 (2016)
Satyanarayana Murthy, T.: Pine Apple Expert System Using Improved C4.5 Algorithm, pp. 1264–1266 (2013)
Satyanarayana Murthy, T.: Privacy Preserving for Expertise Data using K-anonymity Technique to Advise the Farmers (2013)
Gopalan, N.P., Satyanarayana Murthy, T., Venkateswarlu, Y.: Hiding Critical Transaction using Unrealization approach (2017)
SatyanarayanaMurthy, T., Gopalan, N.P., Venkateswarlu, Y.: An Efficient Method for Hiding Association Rules with Additional Parameter Metrics (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gopalan, N.P., Murthy, T.S. (2019). Association Rule Hiding Using Chemical Reaction Optimization. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 816. Springer, Singapore. https://doi.org/10.1007/978-981-13-1592-3_19
Download citation
DOI: https://doi.org/10.1007/978-981-13-1592-3_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1591-6
Online ISBN: 978-981-13-1592-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)