Abstract
Statistical agencies release microdata to researchers after applying statistical disclosure control (SDC) methods. Noise addition is a perturbative SDC method which is carried out by adding independent random noise to a continuous variable or by misclassifying values of a categorical variable according to a probability mechanism. Because these errors are purposely introduced into the data by the statistical agency, the perturbation parameters are known and can be used by researchers to adjust statistical inference through measurement error models. However, statistical agencies rarely release perturbation parameters and therefore modifications to SDC methods are proposed that a priori ensure valid inferences on perturbed datasets.
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
Anwar, N.: Micro-Aggregation-The Small Aggregates Method. Informe Intern. Luxembourg, Eurostat (1993)
Brand, R.: Micro-data Protection Through Noise Addition. In: Domingo-Ferrer, J. (ed.) Inference Control in Statistical Databases. LNCS, vol. 2316, pp. 97–116. Springer, Heidelberg (2002)
Dalenius, T., Reiss, S.P.: Data Swapping: A Technique for Disclosure Control. Journal of Statistical Planning and Inference 7, 73–85 (1982)
Domingo-Ferrer, J., Mateo-Sanz, J.: Practical Data-Oriented Microaggregation for Statistical Disclosure Control. IEEE Transactions on Knowledge and Data Engineering 14(1), 189–201 (2002)
Fienberg, S.E., McIntyre, J.: Data Swapping: Variations on a Theme by Dalenius and Reiss. In: Domingo-Ferrer, J., Torra, V. (eds.) PSD 2004. LNCS, vol. 3050, pp. 14–29. Springer, Heidelberg (2004)
Fuller, W.A.: Measurement Error Models. Wiley, New York (1987)
Fuller, W.A.: Masking Procedures for Micro-data Disclosure Limitation. Journal of Official Statistics 9, 383–406 (1993)
Gourweleeuw, J., Kooiman, P., Willenborg, L.C.R.J., De Wolf, P.P.: Post Randomisation for Statistical Disclosure Control: Theory and Implementation. Journal of Official Statistics 14, 463–478 (1998)
Kargupta, H., Datta, S., Wang, Q., Ravikumar, K.: Random Data Perturbation Techniques and Privacy Preserving Data Mining. Knowledge and Information Systems 7(4), 387–414 (2005)
Kim, J.J.: A Method for Limiting Disclosure in Micro-data Based on Random Noise and Transformation. In: ASA Proceedings of the Section on SRM, pp. 370–374 (1986)
Shlomo, N., De Waal, T.: Protection of Micro-data Subject to Edit Constraints Against Statistical Disclosure. Journal of Official Statistics 24(2), 1–26 (2008)
Tendick, P., Matloff, N.: A Modified Random Perturbation Method for Database Security. ACM Transactions on Database Systems 19(1), 47–63 (1994)
Willenborg, L.C.R.J., De Waal, T.: Elements of Statistical Disclosure Control in Practice. LNS, vol. 155. Springer, New York (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shlomo, N. (2010). Measurement Error and Statistical Disclosure Control. In: Domingo-Ferrer, J., Magkos, E. (eds) Privacy in Statistical Databases. PSD 2010. Lecture Notes in Computer Science, vol 6344. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15838-4_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-15838-4_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15837-7
Online ISBN: 978-3-642-15838-4
eBook Packages: Computer ScienceComputer Science (R0)