MarketTrust: blockchain-based trust evaluation model for SIoT-based smart marketplaces

Area of Science:

• Computer Science
• Information Security
• Internet of Things

Background:

• Trust is crucial for secure and reliable transactions in Social Internet of Things (SIoT) smart marketplaces.
• Existing trust assessment methods in SIoT are vulnerable to seller dishonesty, impacting system integrity.
• Need for robust trust evaluation to mitigate risks posed by malicious actors in online trading environments.

Purpose of the Study:

• Introduce the MarketTrust model, a novel blockchain-based framework for trust assessment in IoT smart marketplaces.
• Develop a comprehensive trust evaluation system integrating familiarity, personal interactions, and public perception.
• Enhance the security, viability, and efficacy of trust assessment in SIoT environments.

Main Methods:

• Developed a blockchain-based MarketTrust model comprising three trust components: familiarity, personal interactions, and public perception.
• Designed a conceptual model and assessment technique for merging these components into a global trust value.
• Conducted experiments to evaluate the model's security, viability, and efficacy against benchmark models.

Main Results:

• The MarketTrust model achieved a 21.99% higher trust score compared to benchmark models.
• Demonstrated a 47.698% lower average latency in trust assessment.
• Effectively reduced malicious behavior and improved buyer confidence in selecting sellers.

Conclusions:

• The MarketTrust model provides an efficient and trustworthy solution for trust assessment in SIoT smart marketplaces.
• Blockchain integration enhances the security and reliability of trust evaluations.
• The proposed framework empowers buyers to make informed decisions and mitigates risks associated with dishonest sellers.