Record Linkage

Record linkage, the process of identifying corresponding records across different datasets, aims to integrate fragmented information for more comprehensive analysis. Current research emphasizes developing privacy-preserving techniques, often employing federated learning, split learning, and differential privacy to protect sensitive data while enabling linkage. Advanced methods leverage deep learning architectures, such as transformers and vision transformers, to improve accuracy, particularly in handling noisy or multimodal data (e.g., text and images), and address challenges posed by diverse data formats and languages. This work has significant implications for various fields, including healthcare, agriculture, and social sciences, by enabling more robust and ethical analyses of large-scale datasets.