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@inproceedings{1674782,
author = {Fairweather, Elliot and Wittner, Rudolf and Chapman, Martin and Holub, Petr and Curcin, Vasa},
address = {Cham},
booktitle = {Lecture Notes in Computer Science},
doi = {http://dx.doi.org/10.1007/978-3-030-80960-7_10},
keywords = {data provenance;non-repudiation;health informatics;decision support systems},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Cham},
isbn = {978-3-030-80959-1},
note = {The conference was organised as a virtual event due to current situation with COVID-19. The conference paper will be published in Provenance and Annotation of Data and Processes (part of LNCS).},
pages = {165-182},
publisher = {Springer},
title = {Non-repudiable provenance for clinical decision support systems},
url = {https://doi.org/10.1007/978-3-030-80960-7_16},
year = {2021}
}
TY - JOUR
ID - 1674782
AU - Fairweather, Elliot - Wittner, Rudolf - Chapman, Martin - Holub, Petr - Curcin, Vasa
PY - 2021
TI - Non-repudiable provenance for clinical decision support systems
PB - Springer
CY - Cham
SN - 9783030809591
N1 - The conference was organised as a virtual event due to current situation with COVID-19. The conference paper will be published in Provenance and Annotation of Data and Processes (part of LNCS).
KW - data provenance;non-repudiation;health informatics;decision support systems
UR - https://doi.org/10.1007/978-3-030-80960-7_16
N2 - Provenance templates are now a recognised methodology for the construction of data provenance records. Each template defines the provenance of a domain-specific action in abstract form, which may then be instantiated as required by a single call to the provenance template service. As data reliability and trustworthiness becomes a critical issue in an increasing number of domains, there is a corresponding need to ensure that the provenance of that data is non-repudiable. In this paper we contribute two new, complementary modules to our template model and implementation to produce non-repudiable data provenance. The first, a module that traces the operation of the provenance template service itself, and records a provenance trace of the construction of an object-level document, at the level of individual service calls. The second, a non-repudiation module that generates evidence for the data recorded about each call, annotates the service trace accordingly, and submits a representation of that evidence to a provider-agnostic notary service. We evaluate the applicability of our approach in the context of a clinical decision support system. We first define a policy to ensure the non-repudiation of evidence with respect to a security threat analysis in order to demonstrate the suitability of our solution. We then select three use cases from within a particular system, Consult, with contrasting data provenance recording requirements and analyse the subsequent performance of our prototype implementation against three different notary providers.
ER -
FAIRWEATHER, Elliot, Rudolf WITTNER, Martin CHAPMAN, Petr HOLUB and Vasa CURCIN. Non-repudiable provenance for clinical decision support systems. In \textit{Lecture Notes in Computer Science}. Cham: Springer, 2021, p.~165-182. ISBN~978-3-030-80959-1. Available from: https://dx.doi.org/10.1007/978-3-030-80960-7\_{}10.
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