Trusted Compliance Enforcement Framework for Sharing Health Big Data
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2022-01-13
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© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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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