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<p>Trust and reputation management systems (TRMS) play a pivotal role in enabling secure and collaborative interactions across distributed networks. Although existing approaches have extensively studied challenges such as scalability, lightweight design, dynamic environments, and identity spoofing, two more fundamental issues remain under-explored yet critically impede trustworthy decentralization: 1) the concentration of power among high-reputation nodes, which systematically distorts fairness and decision equality, and 2) collusive manipulation among participants, which directly erodes transactional trust and system integrity. These problems represent core structural vulnerabilities that undermine the foundational principles of decentralized trust management—making them essential to resolve for achieving truly equitable and secure distributed systems. To bridge this gap, we propose BARM, a blockchain-based reputation calculus framework that fundamentally restructures reputation governance through: 1) anti-centralization mechanisms preventing dominance, 2) collusion-resistant partner selection and 3) authentic trust recommendations. As a foundational task-driven reputation calculus architecture, BARM ensures provable reliability-security co-assurance via coordinated execution of: Uniform Group (UG), Reputation Average (RA), Two-Phase Surfer (TPS) strategies and blockchain-based verification services. Finally, analysis and simulation results based on multiple realistic attack scenarios demonstrate BARM has superior reliability and system robustness compared to conventional approaches.</p>

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BARM: A decentralized and manipulation-resistant reputation management approach for distributed networks

  • 期刊论文

  • 发表日期：

    2025年11月26日

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  • Yang Peng,
  • Jie Huang

摘要

Trust and reputation management systems (TRMS) play a pivotal role in enabling secure and collaborative interactions across distributed networks. Although existing approaches have extensively studied challenges such as scalability, lightweight design, dynamic environments, and identity spoofing, two more fundamental issues remain under-explored yet critically impede trustworthy decentralization: 1) the concentration of power among high-reputation nodes, which systematically distorts fairness and decision equality, and 2) collusive manipulation among participants, which directly erodes transactional trust and system integrity. These problems represent core structural vulnerabilities that undermine the foundational principles of decentralized trust management—making them essential to resolve for achieving truly equitable and secure distributed systems. To bridge this gap, we propose BARM, a blockchain-based reputation calculus framework that fundamentally restructures reputation governance through: 1) anti-centralization mechanisms preventing dominance, 2) collusion-resistant partner selection and 3) authentic trust recommendations. As a foundational task-driven reputation calculus architecture, BARM ensures provable reliability-security co-assurance via coordinated execution of: Uniform Group (UG), Reputation Average (RA), Two-Phase Surfer (TPS) strategies and blockchain-based verification services. Finally, analysis and simulation results based on multiple realistic attack scenarios demonstrate BARM has superior reliability and system robustness compared to conventional approaches.