Pragmatic De-Identification of Cross-Domain Unstructured Documents: A Utility-Preserving Approach with Relation Extraction Filtering

Abstract

The volume of information, and in particular personal information, generated each day is increasing at a staggering rate. The ability to leverage such information depends greatly on being able to satisfy the many compliance and privacy regulations that are appearing all over the world. We present READI, a utility preserving framework for the unstructured document de-identification. READI leverages Named Entity Recognition and Relation Extraction technology to improve the quality of the entity detection, thus improving the overall quality of the data de-identification process. We evaluate the proposed approach on two different datasets and compare with the existing state-of-the-art approaches. We show that READI notably reduces the number of false positives and improves the utility of the de-identified text.