Abstract
We propose two models to quantitatively measure the degree of privacy invasion based on the granular computing methodology. The total cost model measures the privacy invasion in light of the effort needed for an investigator to find individual’s private information. The average benefit model measures the privacy invasion in light of the benefit an investigator gets when his investigation improves the assessment of individuals private information. These two models can remedy the inadequacy of the deterministic formulation of privacy proposed in [4]. These two measurements have been implemented in CellSecu 2.0, and a more relaxed generalization procedure, called external generalization, has also been implemented.
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.
References
Adam, N.R. and Wortmann, J.C., Security-control methods for statistical databases: A comparative study. ACM Computing Surveys, Vol. 21, No. 4, December 1989.
Alexander, L. and Jabine, T., Access to social security microdata files for research and statistical purposes. Social Security Bulletin, (41) No. 8, 1978.
Chiang, Y.-C., Protecting privacy in public database (in Chinese), Master’s thesis, Graduate Institute of Information Management, National Taiwan University, 2000.
Chiang, Y.-C. and Hsu, T.-s. and Kuo, S. and Wang, D.-W., Preserving confidentially when sharing medical data. In Proceedings Asia Pacific Medical Informatics Conference (APAMI-MIC), 2000.
Chellas, B.F., Modal Logic. Cambridge, U.K.: Cambridge University Press, 1980.
Denning, D.E. and Akl, S.G. and Heckman, M. and Lunt, T.F. and Morgenstern, M. and Neumann, P.G. and Schell, R. R., Views for multilevel database security. In IEEE Transactions on Software Engineering, Vol. 13, No. 2, pp. 129–140, February 1987.
Fagin, R. and Halpern, J.Y. and Moses, Y. and Vardi, M.Y., Reasoning about knowledge MIT Press 1995.
Ford, W.R. and O’Keefe, J. and Thuraisingham, M.B., Database Inference Controller: An Overview. Technical Report MTR 10963 Vol. 1. The MITRE Corporation. August 1990.
Garvey, T.D. and Lunt, T.F. and Quin, X. and Stickel, M.E. Inference Channel Detection and Elimination in Knowledge-Based Systems. Final Report ECU 2528, SRI International, October 1994.
Halpern, J.K. and Tuttle, M.R., Knowledge, probability, and adversaries. Journal of the A CM 40(4), 917–962, 1993.
Hinke, T.H., Inference aggregation detection in database management systems. In Proceedings of the IEEE Symposium on Research in Security and Privacy, pp. 96–106, April 1988.
Hoffman, L.J. and Miller, W.F., Getting a personal dossier from a Statistical data bank. Datamation, vol. 16, No. 5, pp. 74–74, May 1970.
Hsu, T.-s. and Liau, C.-J and Wang, D.-W., A logical model for privacy protection. In Proceedings of Information Security Conference (ISC), Springer-Verlag LNCS # 2200, pp. 110–124, 2001.
Hundepool, A. and Willenborg, L., μ and τ-argus: software for statistical disclosure control. In Procceedings of the Third International Seminar on Statistical Confidentiality, Bled, 1996.
Jajodia, S., Aggregation and inference problems in multilevel secure systems. In Proceedings of the 5th Rome Laboratory Database Security Workshop, 1992.
Krasucki, P. and Parikh, R. and Ndjatou, G. Probabilistic knowledge and probabilistic common knowledge (preliminary report). In Ras, Z. W. and Zemankova, M. and Emrich, M.L. editors, Methodologies for Intelligent Systems, vol 5, pp. 1–8. Elsevier Science Publishing Co., Inc., The Hague, 1990.
Sweeney, L., Guaranteeing Anonymity When Sharing Medical Data, the Datafly System. MIT A.I. Working Paper No. AIWP-WP334. May 1997.
Morgenstern, M., Controlling logical inference in multilevel database systems. In Proceedings of the IEEE Symposium on Security and Privacy, pp. 245–255, April 1988.
Palley, M.A., Security of statistical databases compromise through attribute correlational modeling. In Proceedings of IEEE Conference on Data Engineering, pp. 67–74, 1986.
Yao Y.Y. and Liau C.J., A generalized decision logic language for granular computing. In Proceedings of the 11th IEEE International Conference on Fuzzy Systems, IEEE Press, 2002.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chiang, YT., Chiang, YC., Hsu, Ts., Liau, CJ., Wang, DW. (2002). How Much Privacy? — A System to Safe Guard Personal Privacy while Releasing Databases. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds) Rough Sets and Current Trends in Computing. RSCTC 2002. Lecture Notes in Computer Science(), vol 2475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45813-1_29
Download citation
DOI: https://doi.org/10.1007/3-540-45813-1_29
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44274-5
Online ISBN: 978-3-540-45813-5
eBook Packages: Springer Book Archive