ReputationChain：面向区块链赋能供应链的鲁棒信任更新
ReputationChain: Robust Trust Updating for Blockchain-Enabled Supply Chains
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中文总结 AI 辅助
该研究提出ReputationChain框架，以区块链为证据溯源层，通过多维度规则优化声誉计算，在供应链模拟中显著降低合谋收益与声誉膨胀率，提升新参与者声誉并降低错误低信任率。
中文摘要 AI 辅助
区块链可保存供应链记录，但仅账本完整性无法表明参与者在未来对风险敏感的交易中是否应被信任。现有声誉系统主要关注产品证据、全局反馈聚合或评论真实性，而较少关注重复双边膨胀、身份多样性不足以及稀疏历史的诚实参与者的不公平衰减问题。我们提出RC（ReputationChain），这是一个参与者信任框架，将区块链用作证据和溯源层而非信任源。受监管的交互结果被转换为有界证据：相同双方的重复交互会被折扣，交易对手多样性低会被惩罚，监管提供的身份置信度会对正面证据加权，分数会根据经验证的交互量向中性先验衰减。身份、合约、结果和更新溯源均记录在链上，而非线性声誉计算在链下执行并在链上检查是否合规。在包含30次种子运行和匹配交互轨迹的受控模拟中，该完整模型将平均合谋收益降至0.1443，而朴素平均证据模型为0.3688，静态衰减模型为0.3585；当一个控制器控制10个身份时，声誉膨胀率降至0.8723，而三个对比基线均高于1.08；在相同的新参与者轨迹上，感知交互量的衰减将新参与者平均声誉从0.6626提升至0.7589，并将错误低信任率从0.3633降至0.1683；配对分析确认了各运行中的这些改进。结果表明该方法可在不检测攻击者的情况下，实现声誉失真的有界降低，投入生产前仍需部署评估和运营数据校准。
英文摘要
Blockchain can preserve supply-chain records, but ledger integrity alone does not show whether a participant should be trusted in a future risk-sensitive transaction. Existing reputation systems mainly address product evidence, global feedback aggregation, or review authenticity, while giving less attention to repeated bilateral inflation, identity multiplicity, and unfair decay for honest participants with sparse histories. We present \RC, a participant trust framework that uses blockchain as an evidence and provenance layer rather than as the source of trust. Governed interaction outcomes are converted into bounded evidence. Repeated interactions between the same pair are discounted, low counterparty diversity is penalized, governance-supplied identity confidence weights positive evidence, and scores decay toward a neutral prior according to verified interaction volume. Identity, contract, outcome, and update provenance remain on chain, while nonlinear reputation computation is performed off chain and checked on chain for admissibility. In controlled simulations with 30 seeded runs and matched interaction traces, the full model reduces mean collusive gain to 0.1443, compared with 0.3688 for naive mean evidence and 0.3585 for static decay. With ten identities under one controller, the reputation inflation ratio falls to 0.8723, while three comparison baselines remain above 1.08. On identical newcomer traces, volume-aware decay increases mean newcomer reputation from 0.6626 to 0.7589 and reduces the false low-trust rate from 0.3633 to 0.1683. Paired analysis confirms these improvements across runs. The results support a bounded reduction in reputation distortion, not attacker detection. Deployment evaluation and calibration with operational data are still required before production use.
