Deep-Learning based Reputation Model for Indirect Trust
Management

D 2023

Deep-Learning based Reputation Model for Indirect Trust Management

BANGUI, Hind; Mouzhi GE and Barbora BÜHNOVÁ

Basic information

Original name

Deep-Learning based Reputation Model for Indirect Trust Management

Authors

BANGUI, Hind (504 Morocco, guarantor, belonging to the institution); Mouzhi GE (156 China) and Barbora BÜHNOVÁ (203 Czech Republic, belonging to the institution)

Edition

Neuveden, 14th International Conference on Ambient Systems, Networks and Technologies Networks (ANT 2023), p. 405-412, 8 pp. 2023

Publisher

Elsevier

Other information

Language

English

Type of outcome

Proceedings paper

Field of Study

10200 1.2 Computer and information sciences

Country of publisher

Netherlands

Confidentiality degree

is not subject to a state or trade secret

Publication form

electronic version available online

References:

URL

RIV identification code

RIV/00216224:14330/23:00130330

Organization unit

Faculty of Informatics

ISSN

DOI

http://dx.doi.org/10.1016/j.procs.2023.03.052

EID Scopus

2-s2.0-85162885481

Keywords in English

Trust Management; Deep learning; IoT; AI

Abstract

V originále

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.

Links

CZ.02.1.01/0.0/0.0/16_019/0000822, interní kód MU
(CEP code: EF16_019/0000822)

Name: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur (Acronym: C4e)

Investor: Ministry of Education, Youth and Sports of the CR, CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence, Priority axis 1: Strengthening capacities for high-quality research

EF16_019/0000822, research and development project

Name: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur

Cite

BANGUI, Hind; Mouzhi GE and Barbora BÜHNOVÁ. Deep-Learning based Reputation Model for Indirect Trust Management. Online. In 14th International Conference on Ambient Systems, Networks and Technologies Networks (ANT 2023). Neuveden: Elsevier, 2023, p. 405-412. ISSN 1877-0509. Available from: https://dx.doi.org/10.1016/j.procs.2023.03.052.

@inproceedings{2255177,
   author = {Bangui, Hind and Ge, Mouzhi and Bühnová, Barbora},
   address = {Neuveden},
   booktitle = {14th International Conference on Ambient Systems, Networks and Technologies Networks (ANT 2023)},
   doi = {http://dx.doi.org/10.1016/j.procs.2023.03.052},
   keywords = {Trust Management; Deep learning; IoT; AI},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Neuveden},
   pages = {405-412},
   publisher = {Elsevier},
   title = {Deep-Learning based Reputation Model for Indirect Trust Management},
   url = {https://doi.org/10.1016/j.procs.2023.03.052},
   year = {2023}
}

TY  - CONF
ID  - 2255177
AU  - Bangui, Hind - Ge, Mouzhi - Bühnová, Barbora
PY  - 2023
TI  - Deep-Learning based Reputation Model for Indirect Trust Management
PB  - Elsevier
CY  - Neuveden
KW  - Trust Management
KW  - Deep learning
KW  - IoT
KW  - AI
UR  - https://doi.org/10.1016/j.procs.2023.03.052
N2  - 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.
ER  -

BANGUI, Hind; Mouzhi GE and Barbora BÜHNOVÁ. Deep-Learning based Reputation Model for Indirect Trust Management. Online. In \textit{14th International Conference on Ambient Systems, Networks and Technologies Networks (ANT 2023)}. Neuveden: Elsevier, 2023, p.~405-412. ISSN~1877-0509. Available from: https://dx.doi.org/10.1016/j.procs.2023.03.052.

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