Abstract
Privacy preservation in data mining tends to protect the sensitive information from getting exploited by the nefarious users in a huge database. This paper explains the concepts of utility-based privacy mining approach using genetic algorithm for optimized computing and search to enhance security and confidentiality. A brief classification and comparison of PPDM (Privacy Preservation in Data Mining) techniques are also listed along with the techniques and the pros and cons of utility mining techniques. To hide the sensitive information, many approaches have been proposed. In this study, we propose an efficient method, for protecting high utility itemsets using genetic approach to achieve the privacy with balance between privacy and disclosure of information. The basic idea behind is to use the proposed work to enhance effectiveness measurements with certain parameters.
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
Fayyad, U.M., Shapiro, G.P., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 37–53 (1996). ISSN 0738-4602
Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann Publishers
Lindell, Y., Pinkas, B.: Privacy-Preserving Data Mining. CRYPTO (2000)
Bertino, E., Fovino, I.N., Provenza, L.P.: A framework for evaluating privacy preserving data mining algorithms. Data Min. Knowl. Disc. 11(2), 121–154 (2005)
Mukherjee, S., Chen, Z., Gangopadhyay, A.: A privacy-preserving technique for Euclidean distance-based mining algorithms using Fourier-related transforms. VLDB J. 15(4), 293–315 (2006)
Kantarcioglu, M., Clifton, C.: Privacy-preserving distributed mining of association rules on horizontally partitioned data. IEEE Trans. Knowl. Data Eng. 16(9), 1026–1037 (2004)
Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: Proceedings of ACM SIGMOD’00, pp: 439–450, Dallas, Texas, USA (2000)
Boora, R.K., Shukla, R., Misra, A.K.: An improved approach to high level privacy preserving itemset mining. (IJCSIS) Int. J. Comput. Sci. Inf. Secur. 6(3) (2009)
Shankar, S., Purusothoman, T.P., Jayanthi, S., Babu, N.: A fast algorithm for mining high utility itemsets. In: Proceedings of IEEE International Advance Computing Conference (IACC 2009), Patiala, India, pp. 1459–1464 (2009)
Yao, H., Hamilton, H., Geng, L.: A unified framework for utilty-based measures for mining itemsets. In: Proceedings of the ACM International Conference on Utility-Based Data Mining Workshop (UBDM), pp. 28–37 (2006)
Verykios, V.S., Bertino, E., Fovino, I.N., Provenza, L.P., Saygin, Y., Theodoridis, Y.: State-of-the-art in privacy preserving data mining, SIGMOD Rec. 33(1), 50–57 (2004)
Berkhin, P.: A survey of clustering data mining techniques. In: Grouping Multidimensional Data, pp. 25–71 (2006)
Liu, K., Kargupta, H.: Random perturbation-based multiplicative data perturbation for privacy preserving distributed data mining. IEEE Trans. Knowl. Data Eng. 18(1) (2006)
Samarati, P.: Protecting respondent’s identities in microdata release. IEEE Trans. Knowl. Data Eng. 13(6), 1010–1027 (2001)
Yeh, J.S., Hsu, P.C. Hhuif and msicf: Novel algorithms for privacy preserving utility mining. Expert Syst. Appl. 37(7), 4779–4786 (2010)
Lin, C.W., Hong, T.P., Wong, J.W., Lan, G.C., Lin, W.Y.: A GA-based approach to hide sensitive high utility itemsets, Hindawi Publishing Corporation. Sci. World J. 2014 (Article ID 804629), 12 (2014)
Yao, A.C.: Protocol for secure computations. In: Proceedings of the 23rd Annual IEEE Symposium on Foundation of Computer Science, pp. 160–164, (1982)
Cheung, D.W.L., et al.: Efficient mining of association rules in distributed databases. IEEE Trans. Knowl. Data Eng. 8(6), 911–922 (1996)
Chan, P.: An extensible meta-learning approach for scalable and accurate inductive learning. PhD Thesis, Department of Computer Science, Columbia University, New York, NY (1996)
Chan, P.: On the accuracy of meta-learning for scalable data mining. J. Intell. Inf. Syst. 8, 5–28 (1997)
Polat, H., Du, W.: Privacy-preserving collaborative filtering using randomized perturbation techniques. In: Proceedings of the Third IEEE International Conference on Data Mining (ICDM’03), pp. 625–628, Melbourne, FL, 19–22 Nov 2003
Oliveira, S.R.M., Zaïne, O.R.: A framework for enforcing privacy in mining frequent patterns, Technical Report, TR02-13, Computer Science Department, University of Alberta, Canada, June (2000)
Du, W., Han, Y., Chen, S.: Privacy preserving multivariate statistical analysis Linear regression and classification. In: Proceedings of the 4th SIAM International Conference on Data Mining, pp. 222–233, Florida, 22–24 Apr 2004
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Sugandha Rathi, Rishi Soni (2016). Privacy Preservation in Utility Mining Based on Genetic Algorithm: A New Approach. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 437. Springer, Singapore. https://doi.org/10.1007/978-981-10-0451-3_8
Download citation
DOI: https://doi.org/10.1007/978-981-10-0451-3_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0450-6
Online ISBN: 978-981-10-0451-3
eBook Packages: EngineeringEngineering (R0)