Deep-Learning based Reputation Model for Indirect Trust Management

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep-Learning based Reputation Model for Indirect Trust Management

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Deep-Learning based Reputation Model for Indirect Trust Management

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/EF16_019%2F0000822" target="_blank" >EF16_019/0000822: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

14th International Conference on Ambient Systems, Networks and Technologies Networks (ANT 2023)

ISBN

—

ISSN

1877-0509

e-ISSN

—

Počet stran výsledku

8

Strana od-do

405-412

Název nakladatele

Elsevier

Místo vydání

Neuveden

Místo konání akce

Neuveden

Datum konání akce

1. 1. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—