Trusted Compliance Enforcement Framework for Sharing Health Big Data
| dc.contributor.author | Kim, Dae-young | |
| dc.contributor.author | Elluri, Lavanya | |
| dc.contributor.author | Joshi, Karuna | |
| dc.date.accessioned | 2022-02-07T15:44:01Z | |
| dc.date.available | 2022-02-07T15:44:01Z | |
| dc.date.issued | 2022-01-13 | |
| dc.description | 2021 IEEE International Conference on Big Data (Big Data) 15-18 Dec. 2021 Orlando, FL, USA | en_US |
| dc.description.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 | en_US |
| dc.description.sponsorship | This research was partially supported by a DoD supplement to the NSF award 1747724, Phase I IUCRC UMBC: Center for Accelerated Real time Analytics (CARTA). | en_US |
| dc.description.uri | https://ieeexplore.ieee.org/document/9671834 | en_US |
| dc.format.extent | 10 pages | en_US |
| dc.genre | conference papers and proceedings | en_US |
| dc.genre | postprints | en_US |
| dc.identifier | doi:10.13016/m2gxoz-gpvk | |
| dc.identifier.uri | https://doi.org/10.1109/BigData52589.2021.9671834 | |
| dc.identifier.uri | http://hdl.handle.net/11603/24129 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Information Systems Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | © 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. | en_US |
| dc.subject | UMBC Ebiquity Research Group | |
| dc.title | Trusted Compliance Enforcement Framework for Sharing Health Big Data | en_US |
| dc.type | Text | en_US |
| dcterms.creator | https://orcid.org/0000-0002-6354-1686 | en_US |