Re-identifying register data by survey data using cluster analysis: an empirical study
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Information Fusion in Data Mining
Information Fusion in Data Mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
On aggregation operators for ordinal qualitative information
IEEE Transactions on Fuzzy Systems
Spatial and non-spatial model-based protection procedures for the release of business microdata
Statistics and Computing
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Distribution-preserving statistical disclosure limitation
Computational Statistics & Data Analysis
A method for evaluating marketer re-identification risk
Proceedings of the 2010 EDBT/ICDT Workshops
Kd-trees and the real disclosure risks of large statistical databases
Information Fusion
TrustBus'07 Proceedings of the 4th international conference on Trust, Privacy and Security in Digital Business
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The performance of Statistical Disclosure Control (SDC) methods for microdata (also called masking methods) is measured in terms of the utility and the disclosure risk associated to the protected microdata set. Empirical disclosure risk assessment based on record linkage stands out as a realistic and practical disclosure risk assessment methodology which is applicable to every conceivable masking method. The intruder is assumed to know an external data set, whose records are to be linked to those in the protected data set; the percent of correctly linked record pairs is a measure of disclosure risk. This paper reviews conventional record linkage, which assumes shared variables between the external and the protected data sets, and then shows that record linkage—and thus disclosure—is still possible without shared variables.