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Trusted Compliance Enforcement Framework for Sharing Health Big Data
2021 IEEE International Conference on Big Data, Big Data 2021 ; : 4715-4724, 2021.
Article in English | Scopus | ID: covidwho-1730889
ABSTRACT
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. © 2021 IEEE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 2021 IEEE International Conference on Big Data, Big Data 2021 Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 2021 IEEE International Conference on Big Data, Big Data 2021 Year: 2021 Document Type: Article