Secure and Privacy-Compliant Data Sharing: An Essential Framework for Healthcare Organizations

Abstract

Data integration from multiple sources can improve decision-making and predict epidemiological trends. While there are many ben- eőts to data integration, there are also privacy concerns, especially in healthcare. The Health Insurance Portability and Accountability Act (HIPAA) is one of the essential regulations in healthcare, and it sets strict standards for the privacy and security of patient data. Often, data integration can be complex because different rules apply to different companies. Many existing data integration technologies are domain-speciőc and theoretical, while others rigorously adhere to uniőed data integration. Moreover, the integration systems do not have semantic access control, which causes privacy breaches. We propose a framework using a knowledge graph for sharing and integrating data across healthcare providers by protecting data privacy. We use an ontology to provide Attribute-Based Access Control (ABAC) for preventing excess or unwanted access based on the user attributes or central organization rules. The data is shared by removing sensitive attributes and anonymizing the rest using k-anonymity to strike a balance between data utility and secret information. A metadata layer describes the schema mapping to integrate data from multiple sources. Our framework is a promising approach to data integration in healthcare, and it addresses some of the critical challenges of data integration in this domain.