Abstract
In privacy preserving classification based on randomisation, the additive and multiplicative perturbation methods were shown as preserving little privacy. Thus, we focus on the retention replacement randomisation-based method for classification over centralised data. We propose how to build privacy preserving classifiers over data distorted by means of the retention replacement randomisation-based method. We consider the eager and lazy classifiers based on emerging patterns and the decision tree. We have tested our proposal and show that the high accuracy results in classification can be obtained with the usage of the retention replacement method.
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References
Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: Chen, W., Naughton, J.F., Bernstein, P.A. (eds.) SIGMOD Conference, pp. 439–450. ACM (2000)
Kim, J.J., Winkler, W.E.: Multiplicative noise for masking continuous data. Technical report, Statistical Research Division, US Bureau of the Census, Washington D.C. (2003)
Chen, K., Liu, L.: Privacy preserving data classification with rotation perturbation. In: ICDM, pp. 589–592. IEEE Computer Society (2005)
Kargupta, H., Datta, S., Wang, Q., Sivakumar, K.: On the privacy preserving properties of random data perturbation techniques. In: ICDM, pp. 99–106. IEEE Computer Society (2003)
Liu, K., Giannella, C.M., Kargupta, H.: An Attacker’s View of Distance Preserving Maps for Privacy Preserving Data Mining. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 297–308. Springer, Heidelberg (2006)
Andruszkiewicz, P.: Privacy preserving classification for continuous and nominal attributes. In: Proceedings of the 16th International Conference on Intelligent Information Systems (2008)
Andruszkiewicz, P.: Probability distribution reconstruction for nominal attributes in privacy preserving classification. In: ICHIT 2008: Proceedings of the 2008 International Conference on Convergence and Hybrid Information Technology, pp. 494–500. IEEE Computer Society, Washington, DC (2008)
Andruszkiewicz, P.: Privacy preserving classification with emerging patterns. In: Saygin, Y., Yu, J.X., Kargupta, H., Wang, W., Ranka, S., Yu, P.S., Wu, X. (eds.) ICDM Workshops, pp. 100–105. IEEE Computer Society (2009)
Andruszkiewicz, P.: Lazy Approach to Privacy Preserving Classification with Emerging Patterns. In: Ryżko, D., Rybiński, H., Gawrysiak, P., Kryszkiewicz, M. (eds.) Emerging Intelligent Technologies in Industry. SCI, vol. 369, pp. 253–268. Springer, Heidelberg (2011)
Liu, K., Giannella, C., Kargupta, H.: A survey of attack techniques on privacy-preserving data perturbation methods. In: Aggarwal, C.C., Yu, P.S. (eds.) Privacy-Preserving Data Mining. Advances in Database Systems, vol. 34, pp. 359–381. Springer (2008)
Agrawal, R., Srikant, R., Thomas, D.: Privacy preserving olap. In: SIGMOD 2005: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, pp. 251–262. ACM, New York (2005)
Andruszkiewicz, P.: Privacy preserving data mining on the example of classification. Master’s thesis, Warsaw University of Technology (2005) (in Polish)
Andruszkiewicz, P.: Optimization for MASK Scheme in Privacy Preserving Data Mining for Association Rules. In: Kryszkiewicz, M., Peters, J.F., Rybiński, H., Skowron, A. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 465–474. Springer, Heidelberg (2007)
Asuncion, A., Newman, D.J.: UCI machine learning repository (2007)
Shafer, J.C., Agrawal, R., Mehta, M.: Sprint: A scalable parallel classifier for data mining. In: Vijayaraman, T.M., Buchmann, A.P., Mohan, C., Sarda, N.L. (eds.) Proceedings of 22th International Conference on Very Large Data Bases, VLDB 1996, September 3-6, pp. 544–555. Morgan Kaufmann, Mumbai (1996)
van Rijsbergen, C.J.: Information Retrieval. Butterworth-Heinemann, Newton (1979)
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Andruszkiewicz, P. (2012). Retention Replacement in Privacy Preserving Classification. In: Morzy, T., Härder, T., Wrembel, R. (eds) Advances in Databases and Information Systems. ADBIS 2012. Lecture Notes in Computer Science, vol 7503. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33074-2_2
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DOI: https://doi.org/10.1007/978-3-642-33074-2_2
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