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A reputation-based framework for honest provenance reporting
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Barakat, Lina, Taylor, Phillip M., Griffiths, Nathan and Miles, Simon (2022) A reputation-based framework for honest provenance reporting. ACM Transactions on Internet Technology, 22 (4). 103. doi:10.1145/3507908 ISSN 1533-5399.
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WRAP-reputation-based-framework-honest-provenance-reporting-Griffiths-2022.pdf - Accepted Version - Requires a PDF viewer. Download (3187Kb) | Preview |
Official URL: https://doi.org/10.1145/3507908
Abstract
Given the distributed, heterogenous, and dynamic nature of service-based IoT systems, capturing circumstances data underlying service provisions becomes increasingly important for understanding process flow and tracing how outputs came about, thus enabling clients to make more informed decisions regarding future interaction partners. Whilst service providers are the main source of such circumstances data, they may often be reluctant to release it, e.g. due to the cost and effort required, or to protect their interests. In response, this paper introduces a reputation-based framework, guided by intelligent software agents, to support the sharing of truthful circumstances information by providers. In this framework, assessor agents, acting on behalf of clients, rank and select service providers according to reputation, while provider agents, acting on behalf of service providers, learn from the environment and adjust provider’s circumstances provision policies in the direction that increases provider profit with respect to perceived reputation. The novelty of the reputation assessment model adopted by assessor agents lies in affecting provider reputation scores by whether or not they reveal truthful circumstances data underlying their service provisions, in addition to other factors commonly adopted by existing reputation schemes. The effectiveness of the proposed framework is demonstrated through an agent-based simulation including robustness against a number of attacks, with a comparative performance analysis against FIRE as a baseline reputation model.
| Item Type: | Journal Article | |||||||||
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| Subjects: | H Social Sciences > HD Industries. Land use. Labor Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||
| Library of Congress Subject Headings (LCSH): | Internet of things , Internet of things -- Standards, Internet of things -- Patents, Electronic data processing , Data integrity, Disclosure of information, Database management, Intelligent agents (Computer software) | |||||||||
| Journal or Publication Title: | ACM Transactions on Internet Technology | |||||||||
| Publisher: | Association for Computing Machinery, Inc. | |||||||||
| ISSN: | 1533-5399 | |||||||||
| Official Date: | November 2022 | |||||||||
| Dates: |
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| Volume: | 22 | |||||||||
| Number: | 4 | |||||||||
| Article Number: | 103 | |||||||||
| DOI: | 10.1145/3507908 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Reuse Statement (publisher, data, author rights): | © ACM, 2022. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Internet Technology, 22(4), Nov 2022 http://doi.acm.org/10.1145/3507908 | |||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||
| Date of first compliant deposit: | 7 January 2022 | |||||||||
| Date of first compliant Open Access: | 7 January 2022 | |||||||||
| RIOXX Funder/Project Grant: |
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