ReputationChain: Robust Trust Updating for Blockchain-Enabled Supply Chains

2026-08-04

AI Summary

Existing blockchain-based supply chain systems struggle to effectively assess the future trustworthiness of participants and are vulnerable to issues such as repeated exaggeration, Sybil attacks, and unfair reputation allocation caused by sparse interactions. This work proposes a novel trust framework that treats on-chain data as evidence and a provenance layer rather than a direct source of trust. It innovatively integrates governance-weighted identity confidence, interaction diversity constraints, and a volume-aware decay mechanism to dynamically update reputations, thereby mitigating collusion and reputation inflation while ensuring fair initial reputation for new entrants. The system employs a hybrid architecture combining on-chain attestation, off-chain nonlinear computation, and on-chain verification. Simulation results demonstrate that the average collusion gain is reduced to 0.1443 (baseline > 0.35), the reputation inflation ratio under ten-fold Sybil identities is 0.8723, new participants achieve an average reputation of 0.7589, and the low-trust misjudgment rate drops to 0.1683.

Abstract

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.

Problem

• reputation inflation
• identity multiplicity
• unfair decay
• trust updating
• blockchain-enabled supply chains

Innovation

• ReputationChain
• blockchain-enabled supply chain
• trust framework
• identity multiplicity
• volume-aware decay