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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Wang, K., Fung, B. C. M., & Dong, G. (2005). Integrating private databases for data analysis. In Lecture Notes in Computer Science (Vol. 3495, pp. 171–182). Springer Verlag. https://doi.org/10.1007/11427995_14
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