tamper‑evident data anchoring / ledger integration

To address the vulnerability of IoT sensor data to tampering and forgery during untrusted transmission—posing critical risks to logistics, healthcare, and humanitarian operations—this paper proposes an end-to-end verifiable data provenance framework. The method innovatively integrates physical-layer tamper-proof hardware signatures, redundant hash chains, and blockchain-based Merkle trees to establish a three-tiered verification mechanism, enabling integrity and authenticity recovery even under partial data loss. A lightweight cryptographic protocol ensures compatibility with Ethereum while balancing security and deployment efficiency. Evaluation in real-world IoT deployments achieves a 99.8% data verifiability rate, sub-1.2-second verification latency, 67% reduction in storage overhead, and support for offline post-hoc auditing.

This study addresses the vulnerability of high-accuracy fraud detection models in enterprise settings, where insiders may tamper with logs or bypass approval workflows, thereby compromising audit integrity. To counter this, the authors propose a tamper-proof AI auditing system that uniquely leverages blockchain as an active enforcement layer. By anchoring explainable machine learning predictions and approval workflows atomically to a Layer-2 chain (e.g., Polygon) via smart contracts, the system ensures end-to-end immutability, verifiability, and compliance with GDPR. Empirical evaluation demonstrates strong performance: an F1 score of 0.895, a PR-AUC of 0.974, inference latency under 25 milliseconds, transaction costs below \$0.01 per operation, and scalability to support tens of thousands of monthly payments.

Institutional Bitcoin custody faces concurrent pressures to demonstrate solvency, manage cross-domain risks, and ensure regulatory compliance—necessitating verifiable transparency without compromising privacy. This paper proposes Treasury Proof Ledger (TPL), a multi-domain treasury logging framework anchored to the Bitcoin blockchain. TPL uniformly models on-chain and off-chain risk exposures via a state machine, defines multi-domain exposure vectors and formal security criteria, and supports verifiable reserve proofs, cross-domain flow tracing, and policy-aware, permissioned views. By integrating hash commitments, transit proofs, and standard reserve proofs under economic and governance assumptions, TPL enables joint accountability and compliant policy disclosure. The framework balances transparency with commercial confidentiality and has been empirically validated for cross-institutional verification. To our knowledge, TPL is the first deployable, accountability-driven transparency architecture designed specifically for institutional Bitcoin treasury management.

Cross-blockchain transactions suffer from insufficient auditability and non-repudiation, rendering them vulnerable to malicious denial or fraudulent accountability. This paper proposes InterSnap, a secure interoperability framework leveraging cross-chain transaction receipts and on-demand distributed ledger snapshots. InterSnap innovatively binds cryptographic receipts with lightweight, dynamically generated ledger snapshots, immutably preserving evidence via decentralized storage. Snapshots are generated on demand and transmitted with low overhead, balancing security and efficiency. Evaluated on Hyperledger Fabric, the prototype demonstrates that InterSnap enables complete recovery of cross-chain credentials following adversarial attacks, supports high-concurrency workloads and cross-cloud deployments, and significantly enhances verifiability, non-repudiation, and system resilience in heterogeneous blockchain interactions.

This work addresses the limitations of existing file integrity verification schemes, which often rely on centralized authorities or incur excessive resource overhead, thereby struggling to achieve a balance among lightweight design, decentralization, and long-term trustworthiness. Building upon the Haber-Stornetta hash anchoring paradigm, the authors propose a lightweight verification mechanism deployed on the Solana blockchain that immutably records SHA-256 hashes of files, enabling efficient and deterministic integrity checks. The approach innovatively decouples proof of existence from proof of authorship, ensuring verifiability of file existence even in the event of private key loss. Furthermore, a four-layer trust architecture is introduced to support cross-chain persistence over extended periods. Empirical evaluation across file sizes ranging from 1 KB to 500 MB demonstrates the system’s feasibility, security against second-preimage attacks, and robust integrity guarantees.

This study addresses the challenges posed by fragmented aircraft maintenance records, which hinder verification and create information asymmetry, thereby impairing residual value assessments. To resolve this, the authors propose a blockchain-based two-layer decentralized architecture that integrates FAA electronic record standards, stores maintenance data off-chain via IPFS, and anchors content identifiers (CIDs) on the BNB Smart Chain to enable efficient and traceable record management. Implemented on the BNB testnet, the system achieves O(1) on-chain operational complexity per record and reduces gas consumption by 93.9%. Economic modeling further demonstrates that tamper-proof records can lower audit costs by over 90%, substantially enhancing the accuracy of residual value evaluations and overall operational efficiency.

Existing autonomous commercial protocols struggle to achieve interoperable, tamper-proof auditing and event temporal verification across heterogeneous domains. This work proposes a verifiable global event timeline architecture that constructs a reproducible, tamper-resistant AI fraud intelligence training pipeline by formalizing event schemas, employing deterministic batching, leveraging Merkle append-only commitments, and anchoring events to blockchain-based timestamps. The approach innovatively integrates cryptographic fraud markers—binding risk labels with anchored evidence—and a data provenance model to establish a verifiable, traceable, AI-ready intelligence layer. Evaluated on a prototype processing 50,000 events, the system constructs Merkle trees in just 47 milliseconds, achieves end-to-end verification in under 0.013 milliseconds, and exhibits logarithmic proof size growth, yielding a 14.4× improvement in verification efficiency over linear scanning.

This study addresses a critical gap in blockchain research, which has predominantly focused on on-chain transactions while neglecting the comprehensive lifecycle management of cryptographic assets. For the first time, the paper introduces the ISO 15489-1:2016 records management standard into the blockchain domain, leveraging records lifecycle theory to propose a seven-stage data lifecycle framework spanning from creation to disposition. The applicability of this model is demonstrated through case studies involving Bitcoin, fungible tokens, and non-fungible tokens. By elucidating the inherent characteristics of blockchain as a records management system, the framework clarifies the boundaries between on-chain and off-chain data and examines how privacy-enhancing technologies affect lifecycle visibility. This structured perspective offers valuable insights for the governance of crypto-assets, regulatory compliance, and forensic investigations.

This work addresses the vulnerabilities in blockchain-based registration systems arising from reliance on a single privileged operator, which enables indistinguishable user behavior and tree pollution attacks—including spurious root registrations, malicious subtree mounts, and identity spoofing. To mitigate these issues, the paper proposes a two-layer cryptographic commitment scheme that binds tree roots to registration identifiers via client-side keys, rendering false attribution a strictly dominated strategy in game-theoretic terms. The study innovatively integrates Nash equilibrium analysis into provenance systems, formally demonstrating that honest behavior constitutes the unique equilibrium. It further establishes that integrity of the provenance tree is guaranteed only through the synergistic interplay of cryptographic prioritization, governance cascades, and contract execution. Built upon standard cryptographic assumptions, the system implements an AnchorRegistry on Ethereum L2 (Base) with O(1) gas cost, enabling trustless reconstruction of the registry from event logs, and its correctness and pollution resistance are formally verified.

This work addresses the tension between the transparency of public distributed ledgers and the data minimization principle, particularly the vulnerability of existing zero-knowledge proof schemes in smart contracts to front-running and proof-replay attacks. We propose a Selective Disclosure Authorization Scheme (SDAS) that enables fine-grained, revocable, and privacy-preserving compliance checks through a user-controlled “authorize–verify–revoke” mechanism without revealing underlying credentials. We formalize a security model for SDAS, introducing ledger-bound attribute unlinkability and context-aware sender binding to ensure proofs are valid only within authorized contexts. Our Ethereum-based ZK-Compliance implementation employs a 14-constraint Circom circuit to anchor proofs to on-chain sender addresses. Evaluations on Sepolia show browser-side proof generation under 200 ms and on-chain verification costing 240,512 gas, effectively preventing proof reuse while rigorously preserving attribute privacy.