IEEE BigData 2021 4th Special Session on HealthCare Data

Trusted Compliance Enforcement Framework for Sharing Health Big Data

Dae-young Leroy Kim, Lavanya Elluri, and Karuna Pande Joshi

December 15, 2021

COVID pandemic management via contact tracing and vaccine distribution has resulted in a large volume and high velocity of Health-related data being collected and exchanged among various healthcare providers, regulatory and government agencies, and people. This unprecedented sharing of sensitive health-related Big Data has raised technical challenges of ensuring robust data exchange while adhering to security and privacy regulations. We have developed a semantically rich and trusted Compliance Enforcement Framework for sharing large velocity Health datasets. This framework, built using Semantic Web technologies, defines a Trust Score for each participant in the data exchange process and includes ontologies combined with policy reasoners that ensure data access complies with health regulations, like Health Insurance Portability and Accountability Act (HIPAA). We have validated our framework by applying it to the Centers for Disease Control and Prevention (CDC) Contact Tracing Use case by exchanging over 1 million synthetic contact tracing records. This paper presents our framework in detail, along with the validation results against Contact Tracing data exchange. This framework can be used by all entities who need to exchange high velocity-sensitive data while ensuring real-time compliance with data regulations.

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Tags: access control, contact tracing, secure data sharing, semantic web, trust management

Type: InProceedings

Publisher: IEEE

Organization: IEEE

Downloads: 548 downloads