Abstract
In privacy preserving classification, when data is stored in a centralized database and distorted using a randomization-based technique, we have information loss and reduced accuracy of classification. Moreover, there are several possible algorithms, different reconstruction types (in case of decision tree) to use and we cannot point out the best combination of them. Meta-learning is the solution to combine information from all algorithms. Furthermore, it gives higher accuracy of classification. This paper presents the new meta-learning approach to privacy preserving classification for centralized data. Effectiveness of this solution has been tested on real data sets and presented in this paper.
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References
Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: Proc. of the ACM SIGMOD Conference on Management of Data, May 2000, pp. 439–450. ACM Press, New York (2000)
Andruszkiewicz, P.: Privacy preserving classification for continuous and nominal attributes. In: Proceedings of the 16th International Conference on Intelligent Information Systems (2008)
Lindell, Y., Pinkas, B.: Privacy preserving data mining. In: Bellare, M. (ed.) CRYPTO 2000. LNCS, vol. 1880, pp. 36–54. Springer, Heidelberg (2000)
Agrawal, D., Aggarwal, C.C.: On the design and quantification of privacy preserving data mining algorithms. In: PODS 2001: Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, pp. 247–255 (2001)
Wenliang Du, Z.Z.: Using randomized response techniques for privacy-preserving data mining. In: Getoor, L., Senator, T.E., Domingos, P., Faloutsos, C. (eds.) KDD, pp. 505–510. ACM, New York (2003)
Yang, Z., Zhong, S., Wright, R.N.: Privacy-preserving classification of customer data without loss of accuracy. In: SDM (2005)
Zhang, N., Wang, S., Zhao, W.: A new scheme on privacy-preserving data classification. In: Grossman, R., Bayardo, R., Bennett, K.P. (eds.) KDD, pp. 374–383. ACM, New York (2005)
Xiong, L., Chitti, S., Liu, L.: Mining multiple private databases using a knn classifier. In: SAC 2007: Proceedings of the 2007 ACM symposium on Applied computing, pp. 435–440 (2007)
Andruszkiewicz, P.: Probability distribution reconstruction for nominal attributes in privacy preserving classification. In: Proceedings of the International Conference on Convergence and Hybrid Information Technology (2008)
Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)
Freund, Y., Schapire, R.: Experiments with a new boosting algorithm. In: Proceedings of the Thirteenth International Conference on Machine Learning (ICML), pp. 148–156 (1996)
Chan, P.K., Stolfo, S.J.: Experiments on multi-strategy learning by meta-learning. In: Bhargava, B.K., Finin, T.W., Yesha, Y. (eds.) CIKM, pp. 314–323. ACM, New York (1993)
Chan, P.K., Stolfo, S.J.: On the accuracy of meta-learning for scalable data mining. J. Intell. Inf. Syst. 8(1), 5–28 (1997)
Chan, P.K.W.: An extensible meta-learning approach for scalable and accurate inductive learning. PhD thesis, New York, NY, USA, Sponsor-Salvatore J. Stolfo. (1996)
Dietterich, T.G.: Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation (10), 1895–1924 (1998)
Shafer, J.C., Rakesh Agrawal, M.M.: Sprint: A scalable parallel classifier for data mining. In: Vijayaraman, T.M., Buchmann, A.P., Mohan, C., Sarda, N.L. (eds.) VLDB 1996, Proceedings of 22th International Conference on Very Large Data Bases, Mumbai (Bombay), September 3-6, pp. 544–555. Morgan Kaufmann, San Francisco (1996)
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Andruszkiewicz, P. (2009). Classification with Meta-learning in Privacy Preserving Data Mining. In: Chen, L., Liu, C., Liu, Q., Deng, K. (eds) Database Systems for Advanced Applications. DASFAA 2009. Lecture Notes in Computer Science, vol 5667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04205-8_22
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DOI: https://doi.org/10.1007/978-3-642-04205-8_22
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