PPRL-Stack: A Novel Stacking Architecture for Efficient and Secure Record Linkage

Record linkage is a fundamental step in ensuring the quality of data by detecting duplicate records within different databases. Nevertheless, dealing with big, imbalanced databases and ensuring data confidentiality is still difficult in terms of performance and precision. This paper introduces a new Privacy-Preserving Record Linkage (PPRL) method named PPRL-Stack, which uses the Bloom filter encoding technique to hide information and a Stack Ensemble structure for classification. The proposed model consists of Support Vector Machine (SVM) as a base learner and Logistic Regression (LR) as a meta-classifier in combination with the application of Sorted Neighborhood Method (SNM) technique to bring down the time complexity to O(N log N). Experiments conducted on the Freely Extensible Biomedical Record Linkage (FEBRL) and North Carolina Voter Registration (NCVR) databases prove that the proposed PPRL-Stack can obtain nearly perfect discrimination with an F1-score of 0.9921. Particularly, our proposed architecture is more than 340 times and 40 times faster than the latest Siamese Bidirectional Long Short-Term Memory (Bi-LSTM) architecture in training and validation stages, respectively.