Improving record linkage with supervised learning for disclosure risk assessment

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.

► We introduce a supervised learning approach for distance-based record linkage to assets disclosure risk in data privacy. ► A weighted mean is used to determine the optimal linkage. ► We discuss the improvements in the linkage process, and the computational costs associated to our approach.