TY - GEN
T1 - Handling Uncertainty in Multi-cloud EMR Trust Assessment
T2 - 9th International Conference on Information System Design and Intelligent Applications, ISDIA 2025
AU - Anjana, S.
AU - Singhal, Sunita
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - Trust quantification in multi-cloud electronic medical record (EMR) sharing environments presents complex challenges due to uncertainty, dynamic factors, and sensitive data requirements. This research introduces a novel fuzzy logic-based trust assessment framework that effectively models trust by leveraging linguistic variables, adaptive membership functions, and sophisticated inference rules. By integrating critical trust dimensions such as data security, regulatory compliance, historical performance, and system uptime, the proposed methodology provides a nuanced, context-sensitive approach to trust evaluation. The framework demonstrates significant potential for enhancing security and reliability in healthcare cloud infrastructures by handling imprecision, incorporating expert knowledge, and enabling continuous learning and trust model adaptation. Experimental results validate the approach’s effectiveness in generating meaningful trust scores that capture the multifaceted nature of trust in complex technological ecosystems.
AB - Trust quantification in multi-cloud electronic medical record (EMR) sharing environments presents complex challenges due to uncertainty, dynamic factors, and sensitive data requirements. This research introduces a novel fuzzy logic-based trust assessment framework that effectively models trust by leveraging linguistic variables, adaptive membership functions, and sophisticated inference rules. By integrating critical trust dimensions such as data security, regulatory compliance, historical performance, and system uptime, the proposed methodology provides a nuanced, context-sensitive approach to trust evaluation. The framework demonstrates significant potential for enhancing security and reliability in healthcare cloud infrastructures by handling imprecision, incorporating expert knowledge, and enabling continuous learning and trust model adaptation. Experimental results validate the approach’s effectiveness in generating meaningful trust scores that capture the multifaceted nature of trust in complex technological ecosystems.
UR - https://www.scopus.com/pages/publications/105027938980
UR - https://www.scopus.com/pages/publications/105027938980#tab=citedBy
U2 - 10.1007/978-981-95-0378-0_22
DO - 10.1007/978-981-95-0378-0_22
M3 - Conference contribution
AN - SCOPUS:105027938980
SN - 9789819503773
T3 - Lecture Notes in Networks and Systems
SP - 313
EP - 326
BT - Information System Design
A2 - Bhateja, Vikrant
A2 - Oroumchian, Farhad
A2 - Tang, Jinshan
A2 - Azar, Ahmad Taher
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 3 January 2025 through 4 January 2025
ER -