Microdata Protection through Noise Addition
Inference Control in Statistical Databases, From Theory to Practice
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)
Data Access in a Cyber World: Making Use of Cyberinfrastructure
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
| Hi-index | 0.00 |
Record linkage is a well known technique used to link records from one database to records from another database which make reference to the same individuals. Although it is usually used in database integration, it is also used in the data privacy field for the disclosure risk evaluation of protected datasets. In this paper we compare two different supervised algorithms which rely on distance-based record linkage techniques, specifically using the Choquet integral's fuzzy integral to compute the distance between records. The first approach uses a linear optimization problem which determines the optimal fuzzy measure for the linkage. While, the second approach is a kind of gradient algorithm with constraints for the fuzzy measures' identification. We show the advantages and drawbacks of both algorithms and also in which situations they will work better.