Publication details

Deep-Learning based Reputation Model for Indirect Trust Management

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BANGUI Hind GE Mouzhi BÜHNOVÁ Barbora

Year of publication 2023
Type Article in Proceedings
Conference 14th International Conference on Ambient Systems, Networks and Technologies Networks (ANT 2023)
MU Faculty or unit

Faculty of Informatics

Keywords Trust Management; Deep learning; IoT; AI
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.
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