The Community for Technology Leaders
RSS Icon
Issue No.02 - February (2012 vol.24)
pp: 353-364
Pui K. Fong , University of Victoria, Victoria
Jens H. Weber-Jahnke , University of Victoria, Victoria
Privacy preservation is important for machine learning and data mining, but measures designed to protect private information often result in a trade-off: reduced utility of the training samples. This paper introduces a privacy preserving approach that can be applied to decision tree learning, without concomitant loss of accuracy. It describes an approach to the preservation of the privacy of collected data samples in cases where information from the sample database has been partially lost. This approach converts the original sample data sets into a group of unreal data sets, from which the original samples cannot be reconstructed without the entire group of unreal data sets. Meanwhile, an accurate decision tree can be built directly from those unreal data sets. This novel approach can be applied directly to the data storage as soon as the first sample is collected. The approach is compatible with other privacy preserving approaches, such as cryptography, for extra protection.
Classification, data mining, machine learning, security and privacy protection.
Pui K. Fong, Jens H. Weber-Jahnke, "Privacy Preserving Decision Tree Learning Using Unrealized Data Sets", IEEE Transactions on Knowledge & Data Engineering, vol.24, no. 2, pp. 353-364, February 2012, doi:10.1109/TKDE.2010.226
[1] S. Ajmani, R. Morris, and B. Liskov, "A Trusted Third-Party Computation Service," Technical Report MIT-LCS-TR-847, MIT, 2001.
[2] S.L. Wang and A. Jafari, "Hiding Sensitive Predictive Association Rules," Proc. IEEE Int'l Conf. Systems, Man and Cybernetics, pp. 164-169, 2005.
[3] R. Agrawal and R. Srikant, "Privacy Preserving Data Mining," Proc. ACM SIGMOD Conf. Management of Data (SIGMOD '00), pp. 439-450, May 2000.
[4] Q. Ma and P. Deng, "Secure Multi-Party Protocols for Privacy Preserving Data Mining," Proc. Third Int'l Conf. Wireless Algorithms, Systems, and Applications (WASA '08), pp. 526-537, 2008.
[5] J. Gitanjali, J. Indumathi, N.C. Iyengar, and N. Sriman, "A Pristine Clean Cabalistic Foruity Strategize Based Approach for Incremental Data Stream Privacy Preserving Data Mining," Proc. IEEE Second Int'l Advance Computing Conf. (IACC), pp. 410-415, 2010.
[6] N. Lomas, "Data on 84,000 United Kingdom Prisoners is Lost," Retrieved Sept. 12, 2008, http://news.cnet.com8301-1009_3-10024550-83.html , Aug. 2008.
[7] BBC News Brown Apologises for Records Loss. Retrieved Sept. 12, 2008, 7104945.stm, Nov. 2007.
[8] D. Kaplan, Hackers Steal 22,000 Social Security Numbers from Univ. of Missouri Database, Retrieved Sept. 2008, article 34964/, May 2007.
[9] D. Goodin, "Hackers Infiltrate TD Ameritrade client Database," Retrieved Sept. 2008, 15ameritrade_database_burgled/, Sept. 2007.
[10] L. Liu, M. Kantarcioglu, and B. Thuraisingham, "Privacy Preserving Decision Tree Mining from Perturbed Data," Proc. 42nd Hawaii Int'l Conf. System Sciences (HICSS '09), 2009.
[11] Y. Zhu, L. Huang, W. Yang, D. Li, Y. Luo, and F. Dong, "Three New Approaches to Privacy-Preserving Add to Multiply Protocol and Its Application," Proc. Second Int'l Workshop Knowledge Discovery and Data Mining, (WKDD '09), pp. 554-558, 2009.
[12] J. Vaidya and C. Clifton, "Privacy Preserving Association Rule Mining in Vertically Partitioned Data," Proc Eighth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '02), pp. 23-26, July 2002.
[13] M. Shaneck and Y. Kim, "Efficient Cryptographic Primitives for Private Data Mining," Proc. 43rd Hawaii Int'l Conf. System Sciences (HICSS), pp. 1-9, 2010.
[14] C. Aggarwal and P. Yu, Privacy-Preserving Data Mining:, Models and Algorithms. Springer, 2008.
[15] L. Sweeney, "k-Anonymity: A Model for Protecting Privacy," Int'l J. Uncertainty, Fuzziness and Knowledge-based Systems, vol. 10, pp. 557-570, May 2002.
[16] J. Dowd, S. Xu, and W. Zhang, "Privacy-Preserving Decision Tree Mining Based on Random Substitions," Proc. Int'l Conf. Emerging Trends in Information and Comm. Security (ETRICS '06), pp. 145-159, 2006.
[17] S. Bu, L. Lakshmanan, R. Ng, and G. Ramesh, "Preservation of Patterns and Input-Output Privacy," Proc. IEEE 23rd Int'l Conf. Data Eng., pp. 696-705, Apr. 2007.
[18] S. Russell and N. Peter, Artificial Intelligence. A Modern Approach 2/E. Prentice-Hall, 2002.
[19] P.K. Fong, "Privacy Preservation for Training Data Sets in Database: Application to Decision Tree Learning," master's thesis, Dept. of Computer Science, Univ. of Victoria, 2008.
20 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool