Abstract
In this paper, we study the privacy-preserving decision tree building problem on vertically partitioned data. We made two contributions. First, we propose a novel hybrid approach, which takes advantage of the strength of the two existing approaches, randomization and the secure multi-party computation (SMC), to balance the accuracy and efficiency constraints. Compared to these two existing approaches, our proposed approach can achieve much better accuracy than randomization approach and much reduced computation cost than SMC approach.
We also propose a multi-group scheme that makes it flexible for data miners to control the balance between data mining accuracy and privacy. We partition attributes into groups, and develop a scheme to conduct group-based randomization to achieve better data mining accuracy. We have implemented and evaluated the proposed schemes for the ID3 decision tree algorithm.
This work was supported by Grant ISS-0219560, ISS-0312366 and CNS-0430252 from the United States National Science Foundation.
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References
Agrawal, D., Aggarwal, C.: On the design and quantification of privacy preserving data mining algorithms. In: Proceedings of the 20th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, Santa Barbara, California, USA, May 21-23, 2001, ACM Press, New York (2001)
Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: Proceedings of the 2000 ACM SIGMOD on Management of Data, Dallas, TX, USA, May 15 - 18, 2000, pp. 439–450. ACM Press, New York (2000)
Du, W., Zhan, Z.: Building decision tree classifier on private data. In: Workshop on Privacy, Security, and Data Mining at The 2002 IEEE International Conference on Data Mining (ICDM’02), Maebashi City, Japan, December 9, 2002, IEEE Computer Society Press, Los Alamitos (2002)
Du, W., Zhan, Z.: Using randomized response techniques for privacy-preserving data mining. In: Proceedings of The 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 24-27, 2003, pp. 505–510. ACM Press, New York (2003)
Evfimievski, A., et al.: Privacy preserving mining of association rules. In: Proceedings of 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, July 2002, ACM Press, New York (2002)
Vaidya, J., Clifton, C.: Privacy preserving association rule mining in vertically partitioned data. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, July 23-26, 2002, ACM Press, New York (2002)
Warner, S.L.: Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association 60(309), 63–69 (1965)
Yang, Z., Zhong, S., Wright, R.N.: Anonymity-preserving data collection. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA, August 21-24, 2005, ACM Press, New York (2005)
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Teng, Z., Du, W. (2007). A Hybrid Multi-group Privacy-Preserving Approach for Building Decision Trees. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_30
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DOI: https://doi.org/10.1007/978-3-540-71701-0_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71700-3
Online ISBN: 978-3-540-71701-0
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