TRUCE: TRUsted Compliance Enforcement Service for Secure Health Data Exchange

2025-12-09

AI Summary

To address regulatory conflicts—particularly between HIPAA and the 21st Century Cures Act—and associated PII leakage risks in health data sharing, this paper proposes a Trusted Compliance Enforcement Framework for real-time, automated governance of sensitive health data. Methodologically, it introduces a novel two-layer trust modeling mechanism that integrates static regulatory requirements with dynamic organizational policies, leveraging AI-based knowledge representation, ontological modeling, semantic rule engines, and adaptive trust scoring algorithms to enable cross-regulatory compliance reasoning and millisecond-scale decision-making. Evaluated on CDC’s million-record contact-tracing dataset, the framework fully satisfies HIPAA Data Use Agreement (DUA) requirements, achieves <10 ms compliance adjudication latency, reduces manual review effort by over 90%, and significantly enhances both privacy protection efficacy and regulatory adaptability.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Responsible Web: Consent frameworks and practices on the web

Abstract

Organizations are increasingly sharing large volumes of sensitive Personally Identifiable Information (PII), like health records, with each other to better manage their services. Protecting PII data has become increasingly important in today's 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 Health 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 which aims to automate compliance procedures and enhance trusted data management within organizations. The TRUCE framework reasons over contexts of data exchange and assesses the trust score of users and the veracity of data based on corresponding regulations. 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 organization's policies. In this paper, we present our framework in detail along with the validation against the Health Insurance Portability and Accountability Act (HIPAA) Data Usage Agreement (DUA) on CDC Contact Tracing patient data, up to one million patient records. TRUCE service will streamline compliance efforts and ensure adherence to privacy regulations and can be used by organizations to manage compliance of large velocity data exchange in real time.

Problem

• Automates compliance procedures for sensitive health data exchange
• Resolves conflicts between regulations like HIPAA and Cures Act
• Enhances trusted data management using AI and semantic technologies

Innovation

• AI and Semantic Web automate compliance procedures
• Trust management combines static regulations and dynamic policies
• Real-time framework validates large-scale health data exchange