Abstract

Recent interest in data collection and monitoring using data mining for security and business-related applications has raised privacy. Privacy Preserving Data Mining (PPDM) techniques require data modification to disinfect them from sensitive information or to anonymize them at an uncertainty level. This study uses PPDM with adult dataset to investigate effects of K-anonymization for evaluation metrics. This study uses Artificial Bee Colony (ABC) algorithm for feature generalization and suppression where features are removed without affecting classification accuracy. Also k-anonymity is accomplished by original dataset generalization.

Keywords:

Adult dataset, Artificial Bee Colony (ABC) algorithm, data mining, K-anonymization, Privacy Preserving Data Mining (PPDM),

References

1. Aggarwal, C.C. and P.S. Yu, 2008. On static and dynamic methods for condensation-based privacy-preserving data mining. ACM T. Database Syst., 33(1): 2.
   CrossRef    
2. Aggarwal, C.C. and S.Y. Philip, 2008. A General Survey of Privacy-Preserving Data Mining Models and Algorithms. Springer, US, pp: 11-52.
   CrossRef    
3. Agrawal, R. and R. Srikant, 2000. Privacy-preserving data mining. Proceeding of the ACM SIGMOD International Conference on Management of Data, pp: 439-450.
   CrossRef    
4. Agrawal, D. and C.C. Aggarwal, 2002. On the design and quantification of privacy preserving data mining algorithms. Proceeding of the 20th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS'01), pp: 247-255.
   
5. Asuncion, A. and D. Newman, 2007. UCI Machine Learning Repository.Retrieved from: http://www.ics.uci.edu/~mlearn/MLRepository.html.
   
6. Bertino, E., D. Lin and W. Jiang, 2008. A survey of quantification of privacy preserving data mining algorithms. Lect. Notes Comput. Sc., 34: 183-205.
   CrossRef    
7. Blanton, M., 2011. Achieving full security in privacy-preserving data mining. Proceeding of the IEEE 3rd International Conference on Privacy, Security, Risk and Trust (PASSAT) and IEEE 3rd International Conference on Social Computing (SOCIALCOM), pp: 925-934.
   CrossRef    
8. Chen, K. and L. Liu, 2008. A Survey of Multiplicative Perturbation for Privacy-preserving Data Mining. Privacy-preserving Data Mining. Springer, US, pp: 157-181.
   
9. Ciriani, V., S.D.C. di Vimercati, S. Foresti and P. Samarati, 2008. k-anonymous data mining: A survey. Lect. Notes Comput. Sc., 34: 105-136.
   CrossRef    
10. Evfimievski, A. and T. Grandison, 2009. Privacy-preserving Data Mining. Handbook of Research on Innovations in Database Technologies and Applications: Current and Future Trends. IGI Global, pp: 1-8.
    
11. Farooqui, M.F., M. Muqeem and M.R. Beg, 2010. A comparative study of multi agent based and high-performance privacy preserving data mining. Int. J. Comput. Appl., 4(12): 23-26.
    CrossRef    
12. Ho, S.S., 2012. Preserving privacy for moving objects data mining. Proceeding of the IEEE International Conference on Intelligence and Security Informatics (ISI), pp: 135-137.
    CrossRef    
13. Hussein, M., A. El-Sisi and N. Ismail, 2008. Fast cryptographic privacy preserving association rules mining on distributed homogenous data base. Lect. Notes Comput. Sc., 5178: 607-616.
    CrossRef    
14. Jha, M.K.M. and M. Barot, 2014. Privacy preserving data mining. Int. J. Futurist. Trends Eng. Tech., 4(1).
    
15. Kadampur, M.A., 2010. A noise addition scheme in decision tree for privacy preserving data mining. J. Comput., 2(1): 137-144.
    
16. Karakos, D., M. Dredze, K. Church, A. Jansen and S. Khudanpur, 2011. Estimating document frequencies in a speech corpus. Proceeding of the IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU, 2011), pp: 407-412.
    CrossRef    
17. Li, Y., M. Chen, Q. Li and W. Zhang, 2012. Enabling multilevel trust in privacy preserving data mining. IEEE T. Knowl. Data En., 24(9): 1598-1612.
    CrossRef    
18. Liu, L., M. Kantarcioglu and B. Thuraisingham, 2008. The applicability of the perturbation based privacy preserving data mining for real-world data. Data Knowl. Eng., 65(1): 5-21.
    CrossRef    
19. Liu, L., M. Kantarcioglu and B. Thuraisingham, 2009. Privacy preserving decision tree mining from perturbed data. Proceeding of the 42nd Hawaii International Conference on System Sciences (HICSS'09), pp: 1-10.
    
20. Machanavajjhala, A., D. Kifer, J. Gehrke and M. Venkitasubramaniam, 2007. l-diversity: Privacy beyond k-anonymity. ACM T. Knowl. Discov. Data, 1(1): 3.
    CrossRef    
21. Magkos, E., M. Maragoudakis, V. Chrissikopoulos and S. Gritzalis, 2009. Accurate and large-scale privacy-preserving data mining using the election paradigm. Data Knowl. Eng., 68(11): 1224-1236.
    CrossRef    
22. Mandapati, S., R.B. Bhogapathi, M.C.S. Rao and V. Vjiet, 2013. Swarm optimization algorithm for privacy preserving in data mining. Int. J. Comput. Sci. Issues, 10(2).
    
23. Matatov, N., L. Rokach and O. Maimon, 2010. Privacy-preserving data mining: A feature set partitioning approach. Inform. Sciences, 180(14): 2696-2720.
    CrossRef    
24. Navarro-Arribas, G., V. Torra, A. Erola and J. Castellà-Roca, 2012. User< i> k-anonymity for privacy preserving data mining of query logs. Inform. Process. Manag., 48(3): 476-487.
    CrossRef    
25. Nayak, G. and D. Swagatika, 2011. A survey on privacy preserving data mining: Approaches and techniques. Int. J. Eng. Sci. Technol., 3.3(2011): 2117-2133.
    
26. Pandey, U.K. and S. Pal, 2011. Data mining: A prediction of performer or underperformer using classification. Int. J. Comput. Sci. Inform. Technol., 2(2): 686-690.
    
27. Patel, M., P. Richariya and A. Shrivastava, 2013. A review paper on privacy preserving data mining. J. Eng. Technol., 2: 359-361.
    
28. Samarati, P., 2001. Protecting respondents identities in microdata release. IEEE T. Knowl. Data En., 13(6): 1010-1027.
    CrossRef    
29. Schiezaro, M. and H. Pedrini, 2013. Data feature selection based on artificial bee colony algorithm. EURASIP J. Image Video Process., 2013(1): 1-8.
    CrossRef    
30. Singh, U.K., K.P. Bhupendra and D. Keerti, 2011. An overview on privacy preserving data mining methodologies. Int. J. Eng. Trends Technol., 2(2).
    
31. Sweeney, L., 2002. Achieving k-anonymity privacy protection using generalization and suppression. Int. J. Uncertain. Fuzz., 10(5): 571-588.
    CrossRef    
32. Tsai, P.W., J.S. Pan, B.Y. Liao and S.C. Chu, 2009. Enhanced artificial bee colony optimization. Int. J. Innov. Comput. I., 5(12): 5081-5092.
    
33. Yang, B., H. Nakagawa, I. Sato and J. Sakuma, 2010. Collusion-resistant privacy-preserving data mining. Proceeding of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp: 483-492.
    CrossRef    
34. Zhan, J., 2008. Privacy-preserving collaborative data mining. IEEE Comput. Intell. M., 3(2): 31-41.
    CrossRef    

Competing interests

The authors have no competing interests.

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