Visible to the public Tracking Sensitive Information and Operations in Integrated Clinical Environment

TitleTracking Sensitive Information and Operations in Integrated Clinical Environment
Publication TypeConference Paper
Year of Publication2019
AuthorsLi, Zhangtan, Cheng, Liang, Zhang, Yang
Conference Name2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)
Date Publishedaug
Keywordscomplex ICE systems, composability, data flow, data flow analysis, data flow analysis framework, data privacy, device interoperability, dynamic analysis, high-level supervisory apps, Ice, integrated Clinical Environment, interoperability, low-level communication middleware, medical cyber physical system, medical cyber-physical systems, Medical Devices, medical information systems, Metrics, middleware, open systems, performance evaluation, privacy, pubcrawl, Publishing, security, security of data, sensitive data, sensitive information, sensitive operations, standardized framework, Standards, static analysis, taint analysis
AbstractIntegrated Clinical Environment (ICE) is a standardized framework for achieving device interoperability in medical cyber-physical systems. The ICE utilizes high-level supervisory apps and a low-level communication middleware to coordinate medical devices. The need to design complex ICE systems that are both safe and effective has presented numerous challenges, including interoperability, context-aware intelligence, security and privacy. In this paper, we present a data flow analysis framework for the ICE systems. The framework performs the combination of static and dynamic analysis for the sensitive data and operations in the ICE systems. Our experiments demonstrate that the data flow analysis framework can record how the medical devices transmit sensitive data and perform misuse detection by tracing the runtime context of the sensitive operations.
DOI10.1109/TrustCom/BigDataSE.2019.00034
Citation Keyli_tracking_2019