A Comprehensive Review and Efficient Implementation of Privacy-Preserving Associative Classification

Darshana H. Patel*, Avani R. Vasant**, Saurabh Shah***
* Head and Assistant Professor, Department of Information Technology, V. V. P. Engineering College, Gujarat, India.
** Professor and Head, Department of Computer Science and Engineering, Babaria Institute of Technology, Gujarat, India.
*** Director and Professor, Department of Computer Engineering, C.U. Shah University, Wadhwan, Gujarat, India.
Periodicity:June - August'2018
DOI : https://doi.org/10.26634/jit.7.3.14406

Abstract

The enormous development in Information and Communications technology had increased the requirement for digital data to be stock up and communal securely. This immense quantity of data, if publicly available, can be employed for growth and development. However, data in its raw form comprises of sensitive information and advances in data mining techniques have increased the breach of privacy or confidential data. As a consequence, a field of privacy-preserving data mining emerged which deals with efficient conduction and application of data mining functionalities without scarifying the privacy of the data. Nevertheless, data should be mined before preserving privacy and amongst various techniques of data mining, associative classification is favorable for classification purpose. This paper focuses on the study of Associative Classification techniques in addition to privacy preserving techniques along with its pros and cons. In addition, related study of privacy preserving associative classification has been presented with an aim of prolific delve in this area. 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.

Keywords

Data Mining, Classification, Associative Classification, Privacy-Preservation.

How to Cite this Article?

Patel,D.H., Avsant,A.R., and Shah,S.(2018). A Comprehensive Review and Efficient Implementation of Privacy-Preserving Associative Classification. i-manager’s Journal on Information Technology, 7(3), 45-52. https://doi.org/10.26634/jit.7.3.14406

References

[1]. Aggarwal, C. C., & Philip, S. Y. (2008). A general survey of privacy-preserving data mining models and algorithms. In Privacy-Preserving Data Mining (pp. 11-52). Springer, Boston, MA.
[2]. Ayala-Rivera, V., McDonagh, P., Cerqueus, T., & Murphy, L. (2014). A systematic comparison and evaluation of k-anonymization algorithms for practitioners. Transactions on Data Privacy, 7(3), 337-370.
[3]. Digital India. (n.d.). In Wikipedia. Retrieved from https://en.wikipedia.org/wiki/Digital_India
[4]. Gambhir, S., & Gondaliya, N. (2012). A survey of Associative Classification Algorithms. International Journal of Engineering Research & Technology (IJERT), 1(9), 1-5.
[5]. Gupta, M., & Aggarwal, N. (2010). Classification techniques analysis. NCCI 2010 -National Conference on Computational Instrumentation CSIO (pp. 128-131).
[6]. Han, J., Pei, J., & Kamber, M. (2012). Data Mining: Concepts and Techniques (3rd Edition), Elsevier.
[7]. Harnsamut, N., Natwichai, J., & Seisungsittisunti, B. (2008). Privacy preserving of associative classification and heuristic approach. In Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2008. SNPD'08. Ninth ACIS International Conference on (pp. 434-439). IEEE.
[8]. Harnsamut, N., Natwichai, J., Sun, X., & Li, X. (2014). Privacy preservation for associative classification. Computational Intelligence, 30(4), 752-770.
[9]. Kundu, G., Munir, S., Bari, M. F., Islam, M. M., & Murase, K. (2007). A novel algorithm for associative classification. In International Conference on Neural Information Processing (pp. 453-459). Springer, Berlin, Heidelberg.
[10]. Li, W., Han, J., & Pei, J. (2001). CMAR: Accurate and efficient classification based on multiple classassociation rules. In ICDM (p. 369-376). IEEE.
[11]. Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. Italicize UCI Machine Learning Repository.
[12]. Liu, B., Hsu., & Ma, Y. (1998). Integrating classification and association rule mining. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD'98) (pp. 80-86).
[13]. Murugeshwari, B., & Sugumar, R. (2016). Rule based privacy preservation method for medical data sets. Middle-East Journal of Scientific Research, 24(8), 2640- 2648.
[14]. Natwichai, J. (2011). Privacy preservation for associative classification: An approximation algorithm. International Journal of Business Intelligence and Data Mining, 6(3), 283-301.
[15]. Nayak, G., & Devi, S. (2011). A survey on privacy preserving data mining: Approaches and techniques. International Journal of Engineering Science and Technology, 3(3), 2127-2133.
[16]. Nikam, S. S. (2015). A comparative study of classification techniques in data mining algorithms. Oriental Journal of Computer Science & Technology, 8(1), 13-19.
[17]. Patel, D., & Kotecha, R. (2017). Privacy Preserving Data Mining: A Parametric Analysis. In Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications (pp. 139-149). Springer, Singapore.
[18]. Raghuram, B., & Gyani, J. (2012). Privacy preserving associative classification on vertically partitioned databases. In Advanced Communication Control and Computing Technologies (ICACCCT), 2012 IEEE International Conference on (pp. 188-192). IEEE.
[19]. Satapathy, S. C., Bhateja, V., Udgata, S. K., & th Pattnaik, P. K. (2016). Proceedings of the 5 International Conference on Frontiers in Intelligent Computing: Theory and Applications (Vol.1), Advances in Intelligent Systems and Computing, Springer.
[20]. Seisungsittisunti, B., & Natwichai, J. (2009). Incremental privacy preservation for associative classification. In Proceedings of the ACM First International Workshop on Privacy and Anonymity for Very Large Databases (pp. 37-44). ACM.
[21]. Tian, T., Hua, D., & Guoping, H. (2010). Privacypreserving classification on horizontally partitioned data. In 2010 International Conference on Computational Intelligence and Security (pp. 230-233). IEEE.
[22]. Vijayarani, S., & Divya, M. (2011). An efficient algorithm for classification rule hiding. International Journal of Computer Applications, 33(3), 39-45.
[23]. Yin, X., & Han, J. (2003). CPAR: Classification based on predictive association rules. In Proceedings of the 2003 SIAM International Conference on Data Mining (pp. 331-335). Society for Industrial and Applied Mathematics.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Online 15 15

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.