TRUSTED COMPLIANCE ENFORCEMENT FRAMEWORK FOR LARGE VOLUME AND HIGH VELOCITY DATA

Abstract

Organizations are increasingly sharing large volumes of datasets with each other to better manage their services. These datasets often contain sensitive Personally Identifiable Information (PII) about individuals, like those pertaining to their health, finance, or cybersecurity. Protecting PII data has become increasingly important in todayÕs digital age, and several regulations have been formulated to ensure the secure exchange and management of sensitive personal data. However, at times some of these regulations are at loggerheads with each other, like the Health Insurance Portability and Accountability Act (HIPAA) and Cures Act; and this adds complexity to the already challenging task of Data compliance. As public concern regarding sensitive data breaches grows, finding solutions that streamline compliance processes and enhance individual privacy is crucial. We have developed a novel TRUsted Compliance Enforcement (TRUCE) framework for secure data exchange at high volume and high velocity, which aims to automate compliance procedures and enhance trusted data management within organizations. This framework, developed using approaches from AI/Knowledge representation and Semantic Web technologies, includes a trust management method that incorporates static ground truth, represented by regulations such as HIPAA, and dynamic ground truth, defined by an organizationÕs policies. The effectiveness of the TRUCE Framework is validated through real-world use cases, including health data exchange and maritime Search and Rescue (SAR) missions. Our methods serve to streamline compliance efforts and ensure adherence to privacy regulations and can be used by organizations to manage compliance of large velocity data exchange at real time.