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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
The House of Commons in Canada: The personal information protection and electronic documents act (2000), http://www.privcom.gc.ca/
Agrawal, R., Evfimievski, A., Srikant, R.: Information sharing across private databases. In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, San Diego, California (2003)
Yao, A.C.: Protocols for secure computations. In: Proceedings of the 23rd Annual IEEE Symposium on Foundations of Computer Science (1982)
Liang, G., Chawathe, S.S.: Privacy-preserving inter-database operations. In: Proceedings of the 2nd Symposium on Intelligence and Security Informatics (2004)
Dalenius, T.: Finding a needle in a haystack - or identifying anonymous census record. Journal of Official Statistics 2, 329–336 (1986)
Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. International Journal on Uncertainty, Fuzziness, and Knowledge-based Systems 10, 571–588 (2002)
Hundepool, A., Willenborg, L.: μ- and τ-argus: Software for statistical disclosure control. In: Third International Seminar on Statistical Confidentiality, Bled (1996)
Fung, B.C.M., Wang, K., Yu, P.S.: Top-down specialization for information and privacy preservation. In: Proceedings of the 21st IEEE International Conference on Data Engineering, Tokyo, Japan (2005)
Wang, K., Yu, P., Chakraborty, S.: Bottom-up generalization: a data mining solution to privacy protection. In: Proceedings of the 4th IEEE International Conference on Data Mining (2004)
Iyengar, V.S.: Transforming data to satisfy privacy constraints. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, AB, Canada, pp. 279–288 (2002)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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
eBook Packages: Computer ScienceComputer Science (R0)