This work expands upon the privacy threat assessment model to quantitatively evaluate the risks of data likability, identifiability, non-repudiation, detectability, unintended disclosure, indulgence, and policy & consent noncompliance, and constructs a framework aimed at mitigating these identified risks.
Initial evaluations using various machine-learning algorithms on pre-and post-generalized datasets demonstrate the privacy framework’s effectiveness in mitigating privacy risks while preserving data usability.
Ze-Yang Zhu, Matthias N. Louws, Roland V. Bumbuc et al.· 0 citations
It is argued that robust data protection must be balanced with scientific progress and that this requires opt-in consent frameworks, staff training, and transparent AI governance.
S. Sablone, A. Costantino, Federica Laurenzano et al.· The Scientist· 0 citations
The analysis highlights how privacy-enhancing technologies offer benefits beyond anonymisation by embedding privacy-by-design values to support responsible innovation and protect sensitive patient data throughout the design, training, and validation of medical AI systems.
Yasmine Zoya· European Journal of Health L...· 0 citations
This research sets out to investigate how Multi-Party Computation can be integrated into a data warehouse system to enable secure, privacy-preserving analyses and demonstrates that such a system can be viable, if it accounts well for performance issues of the technology.
This interdisciplinary project proposes the co-creation of three clinical use cases involving seven medical centers located in the EU and beyond, where sensitive patient data is made available and analyzed in a GDPR-compliant mechanism via a Distributed Analytics (DA) paradigm called the Personal Health Train (PHT).
A. García, Diana Martínez, Ana León et al.· RCIS Workshops· 0 citations
This study collected literature from major databases and revealed that all researchers addressed security aspects, such as basic authentication and privacy, but they neglected cryptographic measures, which remain the weakest point of execution in Arduino-based medical systems.
M. Qasim, Murtadha Al-Maliki, Radhwan Sneesl et al.· Applied computing Journal· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
