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Data Mining and Knowledge Discovery

Erschienen in:

01.09.2005

Probabilistic Information Loss Measures in Confidentiality Protection of Continuous Microdata

verfasst von: Josep M. Mateo-Sanz, Josep Domingo-Ferrer, Francesc Sebé

Erschienen in: Data Mining and Knowledge Discovery | Ausgabe 2/2005

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Abstract

Inference control for protecting the privacy of microdata (individual data) should try to optimize the tradeoff between data utility (low information loss) and protection against disclosure (low disclosure risk). Whereas risk measures are bounded between 0 and 1, information loss measures proposed in the literature for continuous data are unbounded, which makes it awkward to trade off information loss for disclosure risk. We propose in this paper to use probabilities to define bounded information loss measures for continuous microdata.

Vorheriger Artikel Preserving the Confidentiality of Categorical Statistical Data Bases When Releasing Information for Association Rules*

Nächster Artikel Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation

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Agrawal, D. and Aggarwal, C.C. 2001. On the design and quantification of privacy preserving data mining algorithms. In Proceedings of the 20th Symposium on Principles of Database Systems, Santa Barbara CA: ACM.

Dandekar, R., Domingo-Ferrer, J., and Sebé, F. 2002. Lhs-based hybrid microdata vs. rank swapping and microaggregation for numeric microdata protection. In Inference Control in Statistical Databases, J. Domingo-Ferrer (Ed.), volume 2316 of LNCS, Berlin, Heidelberg: Springer, pp. 153–162

Domingo-Ferrer, J. and Mateo-Sanz, J.M. 2002. Practical data-oriented microaggregation for statistical disclosure control. IEEE Transactions on Knowledge and Data Engineering, 14(1):189–201.CrossRef

Domingo-Ferrer, J., Mateo-Sanz, J.M., and Torra, V. 2001. Comparing sdc methods for microdata on the basis of information loss and disclosure risk. In Pre-proceedings of ETK-NTTS'2001 vol. 2, Luxemburg: Eurostat, pp. 807–826

Domingo-Ferrer, J. and Torra, V. 2001a. Disclosure protection methods and information loss for microdata. In Confidentiality, Disclosure and Data Access: Theory and Practical Applications for Statistical Agencies, P. Doyle, J.I. Lane, J.J.M. Theeuwes, and L. Zayatz (Eds.), North-Holland: Amsterdam, pp. 91–110, http://vneumann.etse.urv.es/publications/bcpi

Domingo-Ferrer, J. and Torra, V. 2001b. A quantitative comparison of disclosure control methods for microdata. In Confidentiality, Disclosure and Data Access: Theory and Practical Applications for Statistical Agencies, P. Doyle, J.I. Lane, J.J.M. Theeuwes, and L. Zayatz (Eds.), North-Holland: Amsterdam, pp. 111–134, http://vneumann.etse.urv.es/publications/bcpi

Härdle, W. 1991. Smoothing Techniques with Implementation in S. New York: Springer-VerlagMATH

Kendall, M.G., Stuart, A., J.K. Ord, S.F.A., and O'Hagan, A. 1994. Kendall's Advanced Theory of Statistics, Volume 1: Distribution Theory (6th Edition). London: Arnold

Moore, R. 1996. Controlled data swapping techniques for masking public use microdata sets. U.S. Bureau of the Census, Washington, DC (unpublished manuscript).

Parzen, E. 1962. On estimation of a probability density and mode. Annals of Mathematical Statistics, 35:1065–1076.CrossRefMathSciNet

Rosenblatt, M. 1956. Remarks on some non-parametric estimates of a density function. Annals of Mathematical Statistics, 27:642–669.CrossRefMathSciNet

Sebé, F., Domingo-Ferrer, J., Mateo-Sanz, J.M., and Torra, V. 2002. Post-masking optimization of the tradeoff between information loss and disclosure risk in masked microdata sets. In Inference Control in Statistical Databases, J. Domingo-Ferrer (Ed.), volume 2316 of LNCS, Berlin, Heidelberg: Springer, pp. 163–171

Silverman, B.W. 1982. Kernel density estimation using the fast fourier transformation. Applied Statistics, 31:93–97.MATHCrossRef

Trottini, M. 2003. Decision models for data disclosure limitation. PhD thesis, Carnegie Mellon University. http://www.niss.org/dgii/TR/Thesis-Trottini-final.pdf

Winkler, W.E. 1999. Re-identification methods for evaluating the confidentiality of analytically valid microdata. In Statistical Data Protection, J. Domingo-Ferrer (Ed.), Luxemburg: Office for Official Publications of the European Communities. (Journal version in Research in Official Statistics, vol. 1, no. 2, pp. 50–69, 1998).

Yancey, W.E., Winkler, W.E., and Creecy, R.H. 2002. Disclosure risk assessment in perturbative microdata protection. In Inference Control in Statistical Databases, J. Domingo-Ferrer (Ed.), volume 2316 of LNCS, Berlin, Heidelberg: Springer, pp. 135–152

Titel: Probabilistic Information Loss Measures in Confidentiality Protection of Continuous Microdata
verfasst von: Josep M. Mateo-Sanz
Josep Domingo-Ferrer
Francesc Sebé
Publikationsdatum: 01.09.2005
Verlag: Springer US
Erschienen in: Data Mining and Knowledge Discovery / Ausgabe 2/2005
Print ISSN: 1384-5810
Elektronische ISSN: 1573-756X
DOI: https://doi.org/10.1007/s10618-005-0011-9

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