Abstract
Post-GDPR, the public/private (healthcare) enterprises, while
performing (sensitive) Big Data Analytics (BDA), encounter the dilemma
of abiding by the privacy regulations on one hand and extracting maximum value from (healthcare) metadata on the other. Concerning this,
one of the major issues is the Privacy Utility trade-off (PUT). The
PUT affects each phase including (healthcare) metadata collection, formulation, storage, and resharing amongst (healthcare) enterprises. So
far in healthcare, PUT concerning issues are identified and resolved in
a remote, disintegrated manner. It’s high time to resolve the issue by
taking a holistic approach. This Ph.D. research work strives to achieve
the same with normative (should be) and empirical (as-is) evaluation of
PUT in Dutch care metadata share landscape. For clarity, the problem
area is segregated into four fundamental dimensions. For each dimension, empirical evaluation is performed using Process Mining (discovery/conformance checking) techniques on real-world healthcare event log(s). Based on data analytics, the conceptual modeling frameworks
are formulated using e3 value modeling or/and REA ontologies. For
normative evaluation, two alternative approaches; the ‘Content Analysis’, to formulate the conceptual modeling framework(s) and ‘BPMN
text extraction’, for documents ‘Rule Mining’ for drawing the respective business model(s), are used. Later, the (in-field) IT expert(s) further evaluates the proposed conceptual model(s). The aim is to evaluate the technical (IS-based privacy-preserving tools and techniques) and respective organizational (access governance, data ownership) measures of Dutch healthcare providers. The research work will (ultimately) contribute standardized conceptual modeling framework(s) with technical and respective organizational measures to efficiently cope with the PUT in handling sensitive (healthcare) metadata.
performing (sensitive) Big Data Analytics (BDA), encounter the dilemma
of abiding by the privacy regulations on one hand and extracting maximum value from (healthcare) metadata on the other. Concerning this,
one of the major issues is the Privacy Utility trade-off (PUT). The
PUT affects each phase including (healthcare) metadata collection, formulation, storage, and resharing amongst (healthcare) enterprises. So
far in healthcare, PUT concerning issues are identified and resolved in
a remote, disintegrated manner. It’s high time to resolve the issue by
taking a holistic approach. This Ph.D. research work strives to achieve
the same with normative (should be) and empirical (as-is) evaluation of
PUT in Dutch care metadata share landscape. For clarity, the problem
area is segregated into four fundamental dimensions. For each dimension, empirical evaluation is performed using Process Mining (discovery/conformance checking) techniques on real-world healthcare event log(s). Based on data analytics, the conceptual modeling frameworks
are formulated using e3 value modeling or/and REA ontologies. For
normative evaluation, two alternative approaches; the ‘Content Analysis’, to formulate the conceptual modeling framework(s) and ‘BPMN
text extraction’, for documents ‘Rule Mining’ for drawing the respective business model(s), are used. Later, the (in-field) IT expert(s) further evaluates the proposed conceptual model(s). The aim is to evaluate the technical (IS-based privacy-preserving tools and techniques) and respective organizational (access governance, data ownership) measures of Dutch healthcare providers. The research work will (ultimately) contribute standardized conceptual modeling framework(s) with technical and respective organizational measures to efficiently cope with the PUT in handling sensitive (healthcare) metadata.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Doctoral Consortium Papers Presented at the 33rd International Conference on Advanced Information Systems Engineering (CAiSE 2021) |
| Pages | 11-20 |
| Number of pages | 10 |
| Publication status | Published - 12 Jul 2021 |
| Event | 33rd International Conference on Advanced Information Systems Engineering, CAiSE 2021: Doctoral Consortium - University of Melbourne, Virtual Event, Australia Duration: 28 Jun 2021 → 2 Jul 2021 Conference number: 33 https://caise21.org/ |
Publication series
| Name | CEUR Workshop Proceedings |
|---|---|
| Volume | 2906 |
| ISSN (Electronic) | 1613-0073 |
Conference
| Conference | 33rd International Conference on Advanced Information Systems Engineering, CAiSE 2021 |
|---|---|
| Abbreviated title | CAiSE 2021 |
| Country/Territory | Australia |
| City | Virtual Event |
| Period | 28/06/21 → 2/07/21 |
| Internet address |