Handbook of record linkage: methods for health and statistical studies, administration, and business
Handbook of record linkage: methods for health and statistical studies, administration, and business
Microdata Protection through Noise Addition
Inference Control in Statistical Databases, From Theory to Practice
Disclosure Risk Assessment in Perturbative Microdata Protection
Inference Control in Statistical Databases, From Theory to Practice
Validating Distance-Based Record Linkage with Probabilistic Record Linkage
CCIA '02 Proceedings of the 5th Catalonian Conference on AI: Topics in Artificial Intelligence
A computational algorithm for handling the special uniques problem
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Minimum Spanning Tree Partitioning Algorithm for Microaggregation
IEEE Transactions on Knowledge and Data Engineering
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation
Data Mining and Knowledge Discovery
Data Quality: Concepts, Methodologies and Techniques (Data-Centric Systems and Applications)
Data Quality: Concepts, Methodologies and Techniques (Data-Centric Systems and Applications)
Duplicate Record Detection: A Survey
IEEE Transactions on Knowledge and Data Engineering
Modeling Decisions: Information Fusion and Aggregation Operators (Cognitive Technologies)
Modeling Decisions: Information Fusion and Aggregation Operators (Cognitive Technologies)
Rethinking rank swapping to decrease disclosure risk
Data & Knowledge Engineering
On the disclosure risk of multivariate microaggregation
Data & Knowledge Engineering
Data Quality and Record Linkage Techniques
Data Quality and Record Linkage Techniques
Constrained Microaggregation: Adding Constraints for Data Editing
Transactions on Data Privacy
Using mahalanobis distance-based record linkage for disclosure risk assessment
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Information fusion in data privacy: A survey
Information Fusion
Heuristic supervised approach for record linkage
MDAI'12 Proceedings of the 9th international conference on Modeling Decisions for Artificial Intelligence
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In data privacy, record linkage can be used as an estimator of the disclosure risk of protected data. To model the worst case scenario one normally attempts to link records from the original data to the protected data. In this paper we introduce a parametrization of record linkage in terms of a weighted mean and its weights, and provide a supervised learning method to determine the optimum weights for the linkage process. That is, the parameters yielding a maximal record linkage between the protected and original data. We compare our method to standard record linkage with data from several protection methods widely used in statistical disclosure control, and study the results taking into account the performance in the linkage process, and its computational effort.