Safeguarding Patients’ Healthcare Data: Introducing Data Privacy Broker

Abstract

To strengthen the healthcare data privacy protecting techniques and ensure the transparency of healthcare data exchange between healthcare stakeholders, various types of data privacy-preserving methods are introduced continuously, and this elevates the privacy concern of ultimate data owners. This thesis highlights privacy concerns and introduces techniques and research directions towards data privacy on healthcare data in Healthcare Information Systems. The thesis uses the power of Shapley Additive exPlanations (SHAP) machine learning algorithms to identify critical data elements that can put personal privacy at risk within a dataset and proposes a patient-centric healthcare information system architecture with a data broker based on what the industries refer to as Cloud Access Security Broker (CASB.) The proposed data privacy broker is inspired by the CASB and middleware integration applications used for financial and administrative purposes by industries. The privacy broker leverages application programming interface services and integration middleware in safeguarding healthcare data privacy.