Abstract
In today’s globally networked society, there is a dual demand on both information sharing and information protection. A typical scenario is that two parties wish to integrate their private databases to achieve a common goal beneficial to both, provided that their privacy requirements are satisfied. In this paper, we consider the goal of building a classifier over the integrated data while satisfying the k-anonymity privacy requirement. The k-anonymity requirement states that domain values are generalized so that each value of some specified attributes identifies at least k records. The generalization process must not leak more specific information other than the final integrated data. We present a practical and efficient solution to this problem.
Research was supported in part by a research grant from Emerging Opportunity Fund of IRIS, and a research grant from the Natural Science and Engineering Research Council of Canada.
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© 2005 Springer-Verlag Berlin Heidelberg
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Wang, K., Fung, B.C.M., Dong, G. (2005). Integrating Private Databases for Data Analysis. In: Kantor, P., et al. Intelligence and Security Informatics. ISI 2005. Lecture Notes in Computer Science, vol 3495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427995_14
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DOI: https://doi.org/10.1007/11427995_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25999-2
Online ISBN: 978-3-540-32063-0
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