Deep-Learning based Reputation Model for Indirect Trust Management

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F23%3A00130330" target="_blank" >RIV/00216224:14330/23:00130330 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.procs.2023.03.052" target="_blank" >https://doi.org/10.1016/j.procs.2023.03.052</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.procs.2023.03.052" target="_blank" >10.1016/j.procs.2023.03.052</a>

Result language
angličtina
Original language name
Deep-Learning based Reputation Model for Indirect Trust Management
Original language description
In the digital era, human and thing behavioral patterns have been merged, which leads to the need for trust management to secure the relationship among people and things (e.g., driverless cars). Due to the dynamism and complexity of digital environments, trust management depends largely on indirect trust to support its reasoning by building the reputation of trustees based on recommendations reflected in the feedback of sentiment and non-sentiment objects. However, different biases are still affecting the accuracy of indirect trust that reflects a collective trustworthiness belief or societal stereotypes. This work focuses on enabling indirect trust management by leveraging deep learning in combination with synthetic data for bias management. Specifically, this paper proposes a reputation model to support decision-making in trust management by minimizing bias in indirect trust information and fostering fairly the relationship among sentiment and non-sentiment objects. Our experimental results show that the synthetic data can significantly improve the classification accuracy in trust management.
Czech name
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Czech description
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Project
<a href="/en/project/EF16_019%2F0000822" target="_blank" >EF16_019/0000822: CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
14th International Conference on Ambient Systems, Networks and Technologies Networks (ANT 2023)
ISBN
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ISSN
1877-0509
e-ISSN
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Number of pages
8
Pages from-to
405-412
Publisher name
Elsevier
Place of publication
Neuveden
Event location
Neuveden
Event date
Jan 1, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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