Trust and Risk Relationship Analysis on a Workflow Basis: A Use Case

Valentina Viduto; Karim Djemame; Paul Townend; Jie Xu; Sarah Fores; Lydia Lau; Vania Dimitrova; Martyn Fletcher; Stephen Hobson; Jim Austin; John McAvoy; Charlie E. Dibsdale

Trust and Risk Relationship Analysis on a Workflow Basis: A Use Case

Valentina Viduto, Karim Djemame, Paul Townend, Jie Xu, Sarah Fores, Lydia Lau, Vania Dimitrova, Martyn Fletcher, Stephen Hobson, Jim Austin, John McAvoy, Charlie E. Dibsdale

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Trust and risk are often seen in proportion to each other; as such, high trust may induce low risk and vice versa. However, recent research argues that trust and risk relationship is implicit rather than proportional. Considering that trust and risk are implicit, this paper proposes for the first time a novel approach to view trust and risk on a basis of a W3C PROV provenance data model applied in a healthcare domain. We argue that high trust in healthcare domain can be placed in data despite of its high risk, and low trust data can have low risk depending on data quality attributes and its provenance. This is demonstrated by our trust and risk models applied to the BII case study data. The proposed theoretical approach first calculates risk values at each workflow step considering PROV concepts and second, aggregates the final risk score for the whole provenance chain. Different from risk model, trust of a workflow is derived by applying DS/AHP method. The results prove our assumption that trust and risk relationship is implicit.

Original language	English
Title of host publication	Proceedings of the Ninth International Conference on Internet Monitoring and Protection
Editors	Jaime Lloret Mauri, Constantion Paleologu
Place of Publication	Paris, France
Publisher	International Academy, Research, and Industry Association (IARIA)
Pages	7-12
Number of pages	6
ISBN (Electronic)	9781612083629
Publication status	Published - 20 Jul 2014
Externally published	Yes
Event	9th International Conference on Internet Monitoring and Protection - Paris, France Duration: 20 Jul 2014 → 24 Jul 2014 Conference number: 9 https://www.iaria.org/conferences2014/ICIMP14.html (Link to Conference Website)

Conference

Conference	9th International Conference on Internet Monitoring and Protection
Abbreviated title	ICIMP 2014
Country/Territory	France
City	Paris
Period	20/07/14 → 24/07/14
Internet address	https://www.iaria.org/conferences2014/ICIMP14.html (Link to Conference Website)

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http://eprints.hud.ac.uk/id/eprint/22834/Licence: Unspecified
http://www.thinkmind.org/index.php?view=article&articleid=icimp_2014_1_20_30010Licence: Other

Cite this

Viduto, V., Djemame, K., Townend, P., Xu, J., Fores, S., Lau, L., Dimitrova, V., Fletcher, M., Hobson, S., Austin, J., McAvoy, J., & Dibsdale, C. E. (2014). Trust and Risk Relationship Analysis on a Workflow Basis: A Use Case. In J. L. Mauri, & C. Paleologu (Eds.), Proceedings of the Ninth International Conference on Internet Monitoring and Protection (pp. 7-12). International Academy, Research, and Industry Association (IARIA). http://eprints.hud.ac.uk/id/eprint/22834/

Viduto, Valentina ; Djemame, Karim ; Townend, Paul et al. / Trust and Risk Relationship Analysis on a Workflow Basis : A Use Case. Proceedings of the Ninth International Conference on Internet Monitoring and Protection. editor / Jaime Lloret Mauri ; Constantion Paleologu. Paris, France : International Academy, Research, and Industry Association (IARIA), 2014. pp. 7-12

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title = "Trust and Risk Relationship Analysis on a Workflow Basis: A Use Case",

abstract = "Trust and risk are often seen in proportion to each other; as such, high trust may induce low risk and vice versa. However, recent research argues that trust and risk relationship is implicit rather than proportional. Considering that trust and risk are implicit, this paper proposes for the first time a novel approach to view trust and risk on a basis of a W3C PROV provenance data model applied in a healthcare domain. We argue that high trust in healthcare domain can be placed in data despite of its high risk, and low trust data can have low risk depending on data quality attributes and its provenance. This is demonstrated by our trust and risk models applied to the BII case study data. The proposed theoretical approach first calculates risk values at each workflow step considering PROV concepts and second, aggregates the final risk score for the whole provenance chain. Different from risk model, trust of a workflow is derived by applying DS/AHP method. The results prove our assumption that trust and risk relationship is implicit.",

keywords = "risk model, provenance, Decision support, workflow, DS/AHP model",

author = "Valentina Viduto and Karim Djemame and Paul Townend and Jie Xu and Sarah Fores and Lydia Lau and Vania Dimitrova and Martyn Fletcher and Stephen Hobson and Jim Austin and John McAvoy and Dibsdale, {Charlie E.}",

note = "No authors are affiliated to Huddersfield on the output. SH 2/8/17; 9th International Conference on Internet Monitoring and Protection, ICIMP 2014 ; Conference date: 20-07-2014 Through 24-07-2014",

year = "2014",

month = jul,

day = "20",

language = "English",

pages = "7--12",

editor = "Mauri, {Jaime Lloret} and Constantion Paleologu",

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Viduto, V, Djemame, K, Townend, P, Xu, J, Fores, S, Lau, L, Dimitrova, V, Fletcher, M, Hobson, S, Austin, J, McAvoy, J & Dibsdale, CE 2014, Trust and Risk Relationship Analysis on a Workflow Basis: A Use Case. in JL Mauri & C Paleologu (eds), Proceedings of the Ninth International Conference on Internet Monitoring and Protection. International Academy, Research, and Industry Association (IARIA), Paris, France, pp. 7-12, 9th International Conference on Internet Monitoring and Protection, Paris, France, 20/07/14. <http://eprints.hud.ac.uk/id/eprint/22834/>

Trust and Risk Relationship Analysis on a Workflow Basis : A Use Case. / Viduto, Valentina; Djemame, Karim; Townend, Paul et al.

Proceedings of the Ninth International Conference on Internet Monitoring and Protection. ed. / Jaime Lloret Mauri; Constantion Paleologu. Paris, France : International Academy, Research, and Industry Association (IARIA), 2014. p. 7-12.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Trust and Risk Relationship Analysis on a Workflow Basis

T2 - 9th International Conference on Internet Monitoring and Protection

AU - Viduto, Valentina

AU - Djemame, Karim

AU - Townend, Paul

AU - Xu, Jie

AU - Fores, Sarah

AU - Lau, Lydia

AU - Dimitrova, Vania

AU - Fletcher, Martyn

AU - Hobson, Stephen

AU - Austin, Jim

AU - McAvoy, John

AU - Dibsdale, Charlie E.

N1 - Conference code: 9

PY - 2014/7/20

Y1 - 2014/7/20

N2 - Trust and risk are often seen in proportion to each other; as such, high trust may induce low risk and vice versa. However, recent research argues that trust and risk relationship is implicit rather than proportional. Considering that trust and risk are implicit, this paper proposes for the first time a novel approach to view trust and risk on a basis of a W3C PROV provenance data model applied in a healthcare domain. We argue that high trust in healthcare domain can be placed in data despite of its high risk, and low trust data can have low risk depending on data quality attributes and its provenance. This is demonstrated by our trust and risk models applied to the BII case study data. The proposed theoretical approach first calculates risk values at each workflow step considering PROV concepts and second, aggregates the final risk score for the whole provenance chain. Different from risk model, trust of a workflow is derived by applying DS/AHP method. The results prove our assumption that trust and risk relationship is implicit.

AB - Trust and risk are often seen in proportion to each other; as such, high trust may induce low risk and vice versa. However, recent research argues that trust and risk relationship is implicit rather than proportional. Considering that trust and risk are implicit, this paper proposes for the first time a novel approach to view trust and risk on a basis of a W3C PROV provenance data model applied in a healthcare domain. We argue that high trust in healthcare domain can be placed in data despite of its high risk, and low trust data can have low risk depending on data quality attributes and its provenance. This is demonstrated by our trust and risk models applied to the BII case study data. The proposed theoretical approach first calculates risk values at each workflow step considering PROV concepts and second, aggregates the final risk score for the whole provenance chain. Different from risk model, trust of a workflow is derived by applying DS/AHP method. The results prove our assumption that trust and risk relationship is implicit.

KW - risk model

KW - provenance

KW - Decision support

KW - workflow

KW - DS/AHP model

M3 - Conference contribution

SP - 7

EP - 12

BT - Proceedings of the Ninth International Conference on Internet Monitoring and Protection

A2 - Mauri, Jaime Lloret

A2 - Paleologu, Constantion

PB - International Academy, Research, and Industry Association (IARIA)

CY - Paris, France

Y2 - 20 July 2014 through 24 July 2014

ER -

Trust and Risk Relationship Analysis on a Workflow Basis: A Use Case

Abstract

Conference

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