Privacy preservation of class association rules and its optimization by utilizing genetic algorithm

  • Authors

    • Darshana H. Patel Gujarat Technological University.
    • Dr. Saurabh Shah C.U. Shah University
    • Dr. Avani Vasant Gujarat Technological University.
    2019-07-14
    https://doi.org/10.14419/ijet.v7i4.27434
  • Data Mining, Associative Classification, Privacy Preserving Data Mining, Optimization, Genetic Algorithm.

  • Abstract

    With the advent of various technologies and digitization, popularity of the data mining has been increased for analysis and growth purpose in several fields. However, such pattern discovery by data mining also discloses personal information of an individual or organization. In today’s world, people are very much concerned about their sensitive information which they don’t want to share. Thus, it is very much required to protect the private data. This paper focuses on preserving the sensitive information as well as maintaining the efficiency which gets affected due to privacy preservation. Privacy is preserved by anonymization and efficiency is improved by optimization techniques as now days several advanced optimization techniques are used to solve the various problems of different areas. Furthermore, privacy preserving association classification has been implemented utilizing various datasets considering the accuracy parameter and it has been concluded that as privacy increases, accuracy gets degraded due to data transformation. Hence, optimization techniques are applied to improve the accuracy.

     

  • References

    1. [1] H Jiawei, K Micheline, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, Elsevier, 2011.

      [2] R Liu, H Wang, Privacy-preserving data publishing, Proc. – Int. Conf. Data. Eng., (2010) 305–308.

      [3] B Liu, W Hsu, Y Ma, Integrating Classification and Association Rule Mining, KDD98-012pdf 1998.

      [4] N Harnsamut, J Natwichai, X Sun, X Li, Privacy preservation for associative classification, Comput. Intell. (2014) 752–770 https://doi.org/10.1111/coin.12028.

      [5] S Gokila, P Venkateswari, A Survey on Privacy Preserving Data Publishing, Int. J Cybern. Informatics (2014) 1–8. https://doi.org/10.5121/ijci.2014.3101.

      [6] F Thabtah, P Cowling, Y Peng, Multiple labels associative classification, Knowl. Inf. Syst. (2006) 109–129. https://doi.org/10.1007/s10115-005-0213-x.

      [7] B Seisungsittisunti, J Natwichai, Incremental privacy preservation for associative classification, Proceeding ACM first Int. Work. Priv. anonymity very large databases - PAVLAD (2009) https://doi.org/10.1145/1651449.1651458.

      [8] G Nayak,S Devi, A Survey on Privacy Preserving Data Mining Approaches and Techniques, Int. J Eng. Sci. (2011) 2127–2133.

      [9] N Safaei, S Sadjadi, M Babakhani, an efficient genetic algorithm for determining the optimal price discrimination, Appl. Math. Comput. (2006) 1693–1702 https://doi.org/10.1016/j.amc.2006.03.022.

      [10] K Park, J Lee, J Choi, Deep Neural Networks for News Recommendations, Proc. 2017 ACM Conf. Inf. Knowl. Manag. - CIKM ’17 (2017) 2255–2258 https://doi.org/10.1145/3132847.3133154.

      [11] D Patel, R Kotecha, Privacy Preserving Data Mining: A Parametric Analysis, Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications (2016) 139–149 https://doi.org/10.1007/978-981-10-3156-4_14.

      [12] Y Jiang, J Shang, y Liu, Maximizing customer satisfaction through an online recommendation system, A novel associative classification model Decision Support Syst. (2010) 470–479 https://doi.org/10.1016/j.dss.2009.06.006.

      [13] D. Martín, J. Alcalá-Fdez, A. Rosete, F. Herrera, “A Niching Genetic Algorithm to mine a diverse set of interesting quantitative association rulesâ€, Elsevier, Information Sciences, Volumes 355–356, pp. 208-228, (2016). https://doi.org/10.1016/j.ins.2016.03.039.

      [14] J Natwichai, Privacy preservation for associative classification: an approximation algorithm, ECTI-CON2010: The 2010 ECTI International Confernce on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (2011) 127-131.

      [15] D Patel, R Kotecha, Associative Classification: A Comprehensive Analysis and Empirical Evaluation, Nirma University International Conference on Engineering (NUiCONE) (2017). https://doi.org/10.1109/NUICONE.2017.8325616.

      [16] M Ouda, S Salem, I Ali, E Saad, Privacy-Preserving Data Mining (PPDM) Method for Horizontally Partitioned Data, International Journal of Computer Science (2012) 339–347.

      [17] Y Zhu, Y Tang, G Chen, A privacy preserving algorithm for mining distributed association rules, Int. Conf. Comput. Manag. CAMAN (2011) https://doi.org/10.1109/CAMAN.2011.5778775.

      [18] S Wedyan, Review and Comparison of Associative Classification Data Mining Approaches, Int. J. Comput. Information Syst. Control Eng. (2014) 34–45.

      [19] A Haris, M Abdullah, A Othman, F Rahman, Optimization and data mining for decision Making, World Congr. Comput. Appl. Inf. Syst. WCCAIS (2014) https://doi.org/10.1109/WCCAIS.2014.6916587.

      [20] M.Hassoon and M. S.Kouhi and M.Zomorodi-Moghadam and M.Abdar, “Rule Optimization of Boosted C5.0 Classification Using Genetic Algorithm for Liver disease Predictionâ€, International Conference on Computer and Applications (ICCA), pp. 299-305, (2017). https://doi.org/10.1109/COMAPP.2017.8079783.

      [21] M Lichman, UCI Machine Learning Repository Irvine CA, University of California School of Information and Computer Science, https://archiveicsuciedu/ml/indexphp (2018).

      [22] D. Martín, J. Alcalá-Fdez, A. Rosete, F. Herrera, “A Niching Genetic Algorithm to mine a diverse set of interesting quantitative association rulesâ€, Elsevier, Information Sciences, Volumes 355–356, pp. 208-228, (2016). https://doi.org/10.1016/j.ins.2016.03.039.

      [23] A.Alexander, C.Stefan, "Respecting Data Privacy in Educational Data Mining: An Approach to the Transparent Handling of Student Data and Dealing with the Resulting Missing Value Problem," IEEE 27th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), Paris, pp160-164. (2018).

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  • How to Cite

    H. Patel, D., Saurabh Shah, D., & Avani Vasant, D. (2019). Privacy preservation of class association rules and its optimization by utilizing genetic algorithm. International Journal of Engineering & Technology, 7(4), 6862-6865. https://doi.org/10.14419/ijet.v7i4.27434

    Received date: 2019-02-14

    Accepted date: 2019-06-12

    Published date: 2019-07-14