\RC, a participant trust framework that uses blockchain as an evidence and provenance layer rather than as the source of trust, and supports a bounded reduction in reputation distortion, not attacker detection.
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.
Digitization supports the preservation and collaborative study of Jiandu, but dispersed ownership, frequent scholarly revisions, and sensitive data complicate secure sharing and accountable governance. We propose J-Trust, a blockchain-based governance framework that separates low-frequency rights confirmation from high-frequency academic collaboration through a main-side dual-chain architecture. The main chain anchors ownership, identities, and final states, while task-specific side chains process revisions and expert voting. A decentralized threshold SM2 scheme with distributed key generation, proactive share refresh, and a signer bitlist provides fault tolerance and traceable accountability. HIBE and CP-ABE enable hierarchical, fine-grained access control, while zero-knowledge proofs synchronize verified side-chain outcomes without exposing private discussions. Experiments on the DeepJiandu dataset show that J-Trust sustains 115–120 TPS with 20–24 ms latency. Ablation and scalability analyses further confirm the effectiveness of the proposed cryptographic and architectural components.
BEAT (Blockchain–Edge AI Trust) is presented, which closes that gap through co-design of three coupled layers: a Graph Attention Network–Long Short-Term Memory (GAT-LSTM) edge module that converts device interaction graphs into normalised trust evidence vectors.
Pavansai Ramarao Maddali, Vijay Kumar Damera, Ratna Kumar Prathipati· International Journal of Adv...· 0 citations
Context: AI-assisted software development can speed up coding and review, but it also makes accountability harder to establish. Developers may submit insufficiently verified code, reviewers may approve changes with limited inspection, and centralized reputation records may be difficult to audit. Objectives: This study introduces TrustChain-Review, a framework that combines verifiable evidence, strategic incentives, and risk-sensitive governance to support more trustworthy code review. Methods: The framework includes a blockchain-based evidence layer, a three-player game-theoretic model for developers, reviewers, and the platform, and a rule that applies stronger governance when the expected benefit justifies its cost. The evaluation uses a controlled simulation calibrated with the Diff Quality Estimation dataset. Six governance configurations are compared over 30 independent runs using reputation accuracy, trust convergence, malicious-review detection, superficial-review detection, net platform utility, governance cost, and cost-efficiency. Results: The full-evidence configuration produces the strongest reputation, trust, and detection results, but it also has the highest governance cost. The risk-adaptive configuration lowers this cost and improves cost-efficiency by applying stronger controls selectively, although its trust and detection results are lower than those of the full-evidence setting. Conclusion: Strong evidence-based governance is most appropriate for high-risk or audit-sensitive changes. For routine or lower-risk contributions, selective governance offers a more practical balance between trustworthiness and operational cost.
Food supply chains continue to be susceptible to fraud, contamination incidents, and unclear provenance records, which erode consumer confidence and significantly harm the world economy each year. Because blockchain technology provides immutable, shareable, cryptographically verified ledgers among people who distrust each other, it is frequently suggested as a solution. The oracle problem, however, is inherited by the majority of deployed systems: a ledger ensures that recorded data is not altered, but it does not ensure that the data was accurate when it was entered. In addition to reviewing the opportunities it presents for food safety and sustainability reporting, this study examines the technological, financial, and regulatory obstacles of blockchain-based food traceability and proposes a new architecture called the Dynamic Trust-Weighted Oracle Consensus (DTW-OC) framework. We present the architecture, the scoring algorithm, a comparison against Proof-of-Work, Proof-of-Stake, and PBFT, an example dairy cold-chain scenario, and a research agenda for standardisation and interoperability. DTW-OC introduces a reputation-weighted, cross-validated oracle layer that scores every IoT sensor and human data source in real time and feeds that score into block-validator selection, so a source's influence on the ledger is proportionate to its demonstrated reliability.
Shikha Singh· Journal of Intelligent Decis...· 0 citations
Modern supply chain management systems increasingly rely on distributed architectures to ensure transparency, integrity, and trust between participants. Blockchain technology provides a promising foundation for such systems; however, traditional consensus mechanisms introduce high computational overhead, energy inefficiency, and privacy risks. These limitations are particularly critical for small and medium-sized enterprises (SMEs) with constrained computational resources, that they are using to expand on their traditional informational systems and not to integrate distributed technologies into the work process, as setup process for blockchain tools is more complex than centralized approach. This paper proposes a private, dockerized blockchain architecture for supply chain management that combines the Proof of Friendship (PoF) consensus mechanism with Zero-Knowledge Proofs (ZKP). By integrating a private, dockerized framework with the Proof of Friendship consensus and Zero-Knowledge Proofs, this architecture enables resource-constrained enterprises to achieve a high-performance decentralized network that simultaneously ensures sub-second transaction validation through social trust metrics, robust protection of competitive business intelligence via cryptographic privacy, and seamless cross-platform deployment through containerization, ultimately overcoming the traditional trade-offs between system transparency, operational cost, and data confidentiality in global trade. PoF extends Proof of Stake by incorporating social trust indicators, including transaction success rate and geographic diversity of validators, enabling resource-efficient and decentralized consensus. ZKP mechanisms are integrated through an off-chain prover module, allowing transaction correctness to be verified without revealing sensitive business data. The proposed approach enhances cybersecurity, data confidentiality, and system scalability while reducing computational costs. Simulation results demonstrate improved resistance to Sybil attacks, reduced validator centralization, and acceptable transaction latency for corporate blockchain deployments.
Pavlo Zherzherunov, O. Shmatko· Bulletin of NTU "KhPI". Seri...· 0 citations
