A trust enhancement model based on distributed learning and blockchain in service ecosystems

Abstract

In a service ecosystem, the trust of users in services serves as the foundation for maintaining normal interactions among users, service providers, and platforms. However, malicious attacks can tamper with the trust value of these services, making it difficult for users to identify reliable services and undermining the benefits of reliable service providers and platforms. When existing trust management models address the impact of malicious attacks on service reliability, they rarely consider leveraging different attack targets to improve the accuracy of compromised service trust. Therefore, we propose a trust enhancement model based on distributed learning and blockchain in the service ecosystem, which adaptively enhances the trust values of compromised services according to the targets of anomalous attacks. Firstly, we conduct a comprehensive analysis of the targets of malicious attacks using distributed learning. Secondly, we introduced a trust enhancement contract that utilizes different methods to enhance the trust of the service based on various attack targets. Finally, our approach outperforms the baseline method significantly. For different attack targets, we observe a reduction in RMSE by 12.38% and 12.12%, respectively, and an enhancement in coverage by 24.94% and 14.56%, respectively. The experimental results show the reliability and efficacy of our proposed model.