Non-repudiable provenance for clinical decision support systems

D 2021

Non-repudiable provenance for clinical decision support systems

FAIRWEATHER, Elliot, Rudolf WITTNER, Martin CHAPMAN, Petr HOLUB, Vasa CURCIN et. al.

Basic information

Original name

Non-repudiable provenance for clinical decision support systems

Authors

FAIRWEATHER, Elliot, Rudolf WITTNER (703 Slovakia, belonging to the institution), Martin CHAPMAN, Petr HOLUB (203 Czech Republic, belonging to the institution) and Vasa CURCIN

Edition

Cham, Lecture Notes in Computer Science, p. 165-182, 18 pp. 2021

Publisher

Springer

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Switzerland

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

References:

URL

Impact factor

Impact factor: 0.402 in 2005

RIV identification code

RIV/00216224:14330/21:00120672

Organization unit

Faculty of Informatics

ISBN

978-3-030-80959-1

ISSN

DOI

http://dx.doi.org/10.1007/978-3-030-80960-7_10

Keywords in English

data provenance;non-repudiation;health informatics;decision support systems

Abstract

V originále

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.

Citovat

FAIRWEATHER, Elliot, Rudolf WITTNER, Martin CHAPMAN, Petr HOLUB and Vasa CURCIN. Non-repudiable provenance for clinical decision support systems. In 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.

@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.

Detailed Information on Publication Record