This paper proposes a novel distributed secure computation engine based on blockchain technology. The core claim is to leverage blockchain's inherent trust and traceability mechanisms to secure computation, guaranteeing the integrity and security of the resulting data. The proposed system employs zero-knowledge proofs and homomorphic encryption to facilitate secure computation while utilizing a blockchain to record the computation process and its outcome, thereby ensuring complete traceability. This represents a new approach to secure computation by directly integrating blockchain's capabilities, addressing limitations of traditional approaches and offering enhanced security and auditability. The system's architecture, core mechanisms, and potential applications are thoroughly detailed, highlighting its advantages and future directions.
This paper proposes a novel approach to constructing distributed knowledge graphs (KGs) leveraging blockchain technology. Traditional knowledge graph construction relies heavily on centralized databases, leading to vulnerabilities concerning data security, trust, and potential manipulation. This research addresses these shortcomings by introducing a decentralized, trustworthy, and traceable KG built upon a blockchain network. The core mechanism involves storing KG nodes and edges directly on the blockchain, coupled with smart contracts to facilitate knowledge validation, verification, and updates. This ensures data integrity and reliability while providing an immutable audit trail. The proposed system offers enhanced security, transparency, and accountability, fundamentally changing how KGs are built and maintained. The research explores the technical challenges and potential benefits of this decentralized approach, demonstrating its feasibility and suitability for a variety of applications.
Smart contracts, self-executing agreements written in code, are increasingly prevalent in decentralized applications (dApps). However, their immutable nature and complex logic make them vulnerable to security breaches, leading to significant financial losses and reputational damage. Traditional security auditing methods, relying heavily on manual code review and fuzzing, are often insufficient in uncovering subtle vulnerabilities. This paper proposes a novel approach to smart contract security auditing utilizing formal verification. We represent smart contracts as formal specifications and employ formal verification tools to rigorously analyze their behavior and detect potential security flaws. The core claim is that leveraging formal verification techniques significantly enhances the security and reliability of smart contracts. The core mechanism involves translating the contract's logic into a formal language, allowing automated verification against predefined security properties. This approach addresses the limitations of traditional auditing, providing a more comprehensive and reliable assessment of smart contract security. The research contributes to a proactive security strategy for smart contract development, reducing the risk of vulnerabilities and fostering greater trust in decentralized systems.
Marjan Sadat Fatemi Ghomi, Abbas Saghaei, Majid Mirzaee Ghazani
Accurately forecasting cryptocurrency price movements following market overreactions is crucial for traders, investors, and risk managers operating in highly volatile environments. This study presents a novel multi-source, multi-output deep learning framework designed to predict the direction of price changes in four major cryptocurrencies — Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), and Ripple (XRP) — immediately after overreaction events. By jointly modeling these assets, our approach captures their interconnected market dynamics, enhancing predictive accuracy. We compile an extensive dataset with over 656 features from diverse sources, including historical trading data, on-chain metrics, technical indicators, and social sentiment data from Google Trends, collected at both daily and intraday frequencies. To improve model interpretability and performance, we introduce two engineered features — price change magnitude and price variation speed — that effectively represent intraday volatility. Feature selection using a Random Forest approach reduces the feature set to 30 key variables, ensuring robustness and avoiding overfitting. Using three advanced deep learning architectures — LSTM, RNN, and CNN — we train models to classify the next-day price movement as upward or downward. Empirical results demonstrate that the multi-output LSTM achieves an F1-score of 73.42%, outperforming both single-asset models (62.95–68.25%) and alternative architectures. These findings highlight the benefits of joint modeling, leading to more reliable forecasts during turbulent market conditions. Our framework offers a practical tool for algorithmic trading, portfolio management, and risk mitigation in the dynamic cryptocurrency landscape.
Hadi Nurhadi, Mahdi Mahdi, Irdawati Irdawati, Meliana Meliana
This article examines how the adoption of blockchain technology influences audit efficiency and fraud detection within modern accounting practice. As financial transactions increasingly move into distributed ledger environments, external auditors are compelled to revisit the assumptions, procedures, and tools that underpin traditional assurance work. Through a systematic review of contemporary auditing, accounting information systems, and financial technology literature, this study identifies the mechanisms through which blockchain reshapes the audit process. The findings indicate that blockchain adoption enhances efficiency primarily by enabling real-time access to immutable transaction records, reducing reliance on sampling-based procedures, automating control testing through smart contracts, and facilitating continuous auditing. At the same time, the technology strengthens fraud detection capacity by improving the traceability of transactions, increasing the difficulty of retroactive manipulation, and supporting forensic analysis of anomalous patterns. However, the realization of these benefits is conditional on several factors, including auditor competence, integration with legacy systems, regulatory clarity, data privacy constraints, and the risk that fraud shifts to points where the blockchain boundary intersects with off-chain processes. The review concludes that blockchain does not eliminate the need for professional skepticism; rather, it redefines the focus of auditor judgment from verifying recorded transactions to evaluating the reliability of the underlying technology environment and its governance. Practical implications for audit firms, standard-setting bodies, and regulators are discussed.
Proof-of-Work (PoW) consensus mechanisms, foundational to cryptocurrencies like Bitcoin, rely on computational difficulty to secure the network. However, this reliance introduces vulnerabilities. Traditional PoW systems are susceptible to targeted attacks where adversaries strategically generate blocks designed to disrupt consensus. This paper proposes a novel approach – adversarial training – to mitigate these vulnerabilities. We introduce a framework where a generator, mimicking an attacker, attempts to craft malicious blocks, while a discriminator learns to identify these blocks. Through iterative training, the system strengthens its defenses against such attacks, promoting robust consensus. This represents a significant departure from conventional PoW security strategies and offers a potentially more resilient approach to distributed ledger technology. We demonstrate the core principle through a conceptual model and outline a possible implementation strategy, highlighting the potential for future research and development.
This paper proposes a novel decentralized consensus protocol utilizing a cellular automaton (CA) as the proof-of-work (PoW) mechanism. Traditional blockchain-based PoW systems rely heavily on computationally intensive cryptographic hash functions, resulting in significant energy consumption and scalability limitations. This research introduces a fundamentally different approach, leveraging the inherent parallelism and computational simplicity of CA systems to achieve distributed agreement. The core mechanism involves nodes collaboratively evolving a CA, with computation occurring through local rule updates. The difficulty of achieving a predefined CA state, representing a block, is dynamically adjusted based on network participation, creating a more energy-efficient and scalable PoW solution. This approach moves beyond cryptographic hashing, offering a potentially transformative method for decentralized consensus in resource-constrained environments. The paper details the theoretical framework, outlines the proposed protocol, and discusses its potential benefits and challenges. Key performance indicators, such as block generation rate and energy consumption, are analyzed, demonstrating the protocol's efficiency compared to traditional PoW systems.
Academic certificate fraud is a persistent problem across developing economies, undermining trust between graduates, employers, and higher education institutions. In Nigeria, this has prompted a federal mandate requiring every employer to verify staff credentials; yet existing verification processes remain manual, slow, and easily circumvented. This paper proposed, implemented, and evaluated a fully decentralized academic certificate management framework built on the Ethereum blockchain, leveraging Solidity smart contracts, the InterPlanetary File System (IPFS) for off-chain document storage, QR-code-enabled instant verification, and on-chain revocation.The system was deployed and tested on the Ethereum Sepolia testnet using Hardhat 2.19.4 and ethers.js 6.x. Results show that issuing a single certificate costs a fraction of a cent, that batch issuance lowers this cost further, that verification is free for employers, and that the system correctly detects fabricated or altered certificates in the overwhelming majority of cases while cutting verification time from days to seconds. These findings demonstrate that blockchain-based credential management is technically sound, affordable, and readily deployable in resource-constrained institutional settings, offering a practical path toward restoring trust in academic credentials across Nigerian and other African universities.
This paper proposes a novel consensus mechanism for blockchain networks, termed Distributed Proof-of-Work with Differential Privacy Constraints (DPPoW). The core objective is to integrate differential privacy guarantees into a Proof-of-Work (PoW) system, mitigating the privacy vulnerabilities inherent in traditional blockchain designs. The proposed mechanism introduces calibrated noise directly into the hashing process, carefully controlled to achieve a specified ε-differential privacy level. Furthermore, it incorporates a distributed key generation scheme to bolster security and prevent centralized control. The design addresses the growing concern about data traceability and potential deanonymization of participants within blockchain networks. Mathematical formulations detail the noise injection process and the key generation protocol, demonstrating the feasibility and effectiveness of the approach. The primary innovation lies in the synergistic combination of PoW's security features with differential privacy, offering a robust solution for privacy-conscious blockchain applications. The system aims to balance security, anonymity, and computational efficiency, a challenging endeavor currently unmet by existing blockchain technologies.
This paper proposes a decentralized software version control system leveraging blockchain technology. Traditional version control systems suffer from central points of failure, lack of transparency, and limited traceability. This system addresses these shortcomings by utilizing a blockchain to immutably record and manage software version information. The core mechanism involves hashing software versions and storing these hashes on a blockchain, ensuring data integrity and providing a transparent, auditable trail. This approach enhances security, improves transparency, and offers enhanced traceability compared to centralized solutions. The system utilizes a distributed consensus mechanism to maintain blockchain integrity, mitigating the risks associated with a single point of failure. This research explores the feasibility and potential benefits of applying blockchain technology to software version control, representing a novel approach to managing software evolution.
Blockchain technology has garnered significant attention for its potential to revolutionize various industries. A critical component of blockchain is its consensus mechanism, which ensures the integrity and reliability of the distributed ledger. However, current blockchain consensus protocols are frequently characterized by their complexity, making formal verification challenging. This paper addresses this challenge by presenting a formal model of a blockchain consensus protocol using process calculi. Specifically, we utilize the Temporal Temporal Calculus (TCC) formalism to represent the protocol's state transitions and interactions. This approach allows for a rigorous analysis of the protocol's correctness, security, and efficiency. The model captures key aspects such as node participation, transaction validation, and block creation. The use of process calculi offers a precise and unambiguous description of the protocol, facilitating the identification and mitigation of potential vulnerabilities. This work contributes to a foundational understanding of blockchain consensus and paves the way for improved protocol design and enhanced security assurances.
This study examines the structural interplay between Decentralized Finance (DeFi) innovations and adaptive regulatory sandbox architectures within modern financial systems. The rapid proliferation of disintermediated protocols-engineered through smart contracts, Automated Market Makers (AMMs), decentralized lending pools, and algorithmic governance-fundamentally challenges traditional supervisory paradigms anchored in centralized, identifiable financial intermediaries. Utilizing the theoretical foundations of financial intermediation, transaction cost economics, and institutional regulatory design, this paper evaluates how regulatory sandboxes serve as dynamic policy testing grounds to reconcile technological experimentation with systemic stability and investor protection. The findings indicate that deploying specialized DeFi sandbox cohorts, augmented by embedded supervision and cryptographic compliance tools, substantially lowers regulatory uncertainty, prevents systemic contagion, and establishes an evidence-based pathway toward resilient decentralized financial governance.
The increasing prevalence of smart contracts in decentralized applications necessitates robust methods for ensuring their correctness and security. Smart contracts, due to their inherent complexity and potential for subtle errors, pose a significant challenge for traditional verification techniques. This paper proposes a novel approach combining symbolic execution with constraint solving and temporal logic verification for the systematic analysis of smart contracts. The core idea is to systematically explore the execution paths of a smart contract using symbolic execution, identifying potential vulnerabilities and execution scenarios. Subsequently, temporal logic is applied to formally verify that the contract's behavior aligns with its intended specifications over time, capturing temporal constraints such as safety and liveness properties. This integrated framework provides a more comprehensive and reliable method for verifying smart contracts compared to relying solely on symbolic execution or temporal logic alone. The approach offers a structured methodology for detecting and mitigating risks associated with smart contract development.
This paper explores stability, volatility and structural change in Bitcoin using an Archive Framework that distinguishes between normal ("Archive") and abnormal ("Evental") market states. Using more than eleven years of daily Bitcoin data, the study investigates whether measures of structural tension help explain periods of market instability. While most predictive relationships prove weak after correcting for methodological bias, the analysis identifies a significant shift in Bitcoin's behaviour during the post-ETF era, characterised by lower realised volatility and substantially greater occupancy of structurally stable market states. The findings suggest that the principal value of the Archive Framework may lie in describing market regimes rather than predicting them.
The traditional Power Law model for Bitcoin is limited; it struggles to simultaneously fit historical data points across different eras without piecewise parameter adjustments. Bitcoin's trajectory is more naturally described as a tangent-based hyper-exponential system, driven by absolute supply scarcity.
This paper presents a novel approach to achieving both strong differential privacy guarantees and Byzantine fault tolerance (BFT) in distributed systems. The core of the system leverages a verifiable random function (VRF) based consensus protocol built upon a Byzantine fault-tolerant distributed ledger. This architecture effectively addresses the inherent challenges of protecting sensitive data while maintaining system availability and data integrity even in the presence of malicious actors. The system utilizes VRF to mask computation results, providing differential privacy, and the distributed ledger to ensure robustness against Byzantine attacks. The key innovation lies in the verifiable and layered design, offering a rigorous framework for combining these two traditionally disparate goals. We demonstrate a theoretical framework for the system's operation, outlining the protocols involved and the mathematical properties utilized to guarantee both privacy and fault tolerance. The system achieves a privacy parameter ε and a fault tolerance threshold τ, where ε controls the privacy loss and τ represents the level of Byzantine fault tolerance. The system's architecture is designed to be adaptable to various distributed applications, offering a robust solution for sensitive data processing in challenging environments.
Automated health insurance claim processing has become increasingly important as insurers rely on intelligent systems to handle growing claim volumes and automated decision support systems for claim transparency. However, existing models often suffer from limited interpretability, insufficient data security, and weak generalizability across diverse claim patterns. To address these challenges, this research proposes InsureFusionNet, a hybrid explainable structured ensemble framework integrating heterogeneous models of deep feature learning, uncertainty-aware prediction, interpretable boosting mechanisms, and high-performance gradient-boosting classifiers through a fusion strategy for robust health insurance claim approval classification. Explainable AI techniques, including SHAP and LIME, are incorporated to provide transparent justifications for automated claim approval decisions, thereby enhancing stakeholder trust and accountability. To ensure data privacy and security, the proposed framework integrates AES-256-GCM encryption for confidentiality, elliptic curve cryptography for secure authentication, and SHA-256 hashing for integrity assurance within a permissioned Hyperledger Fabric blockchain, enabling controlled access, tamper-resistant auditability, and trustworthy claim management. Experimental results demonstrate that InsureFusionNet achieves superior performance, attaining an accuracy of 97.24%, precision of 98.41%, recall of 95.89%, and F1-score of 97.11% compared to individual classifiers. Overall, the proposed framework offers a secure, transparent, and reliable solution for secure and explainable automated health insurance claim approval classification and contributes toward responsible and trustworthy AI deployment in healthcare systems.
Decentralized Autonomous Organizations (DAOs) present a novel paradigm for organizational structure and operation, leveraging blockchain technology and smart contracts. However, the inherent decentralization of DAOs introduces significant vulnerabilities to manipulation and challenges in achieving fair and efficient decision-making. This paper proposes a framework for governing DAOs utilizing formal game theory, aiming to establish robust governance mechanisms that mitigate these risks. The core claim is that DAOs necessitate rigorous governance, and the proposed mechanism involves designing a DAO governance system based on the equilibrium outcomes of a meticulously constructed game. Voting rights and decision-making processes are directly linked to these game-theoretic equilibria. This approach provides a mathematically sound and verifiable basis for DAO governance, offering a significant advancement over existing, often informal, governance models. We outline the key components of this framework, including game selection, parameter tuning, and the potential for dynamic adaptation. The system's capacity for predicting and preventing manipulation, coupled with its emphasis on fairness, represents a key contribution to the development of stable and trustworthy DAOs.
This paper presents a novel distributed consensus algorithm designed for commodity networks, specifically focusing on achieving Byzantine fault tolerance. Existing consensus algorithms frequently suffer from complexity and high resource requirements, limiting their applicability in real-world scenarios where network conditions and potential malicious activity are significant concerns. This algorithm addresses these limitations by utilizing the inherent capabilities of commodity networks—such as the Internet—to facilitate distributed agreement. A core mechanism is the incorporation of Byzantine fault tolerance techniques, ensuring that the system can maintain agreement even when a fraction of the nodes are compromised and actively attempting to disrupt the consensus process. The proposed algorithm prioritizes simplicity and efficiency, aiming for accessibility on everyday networks while robustly handling potential attacks. The design emphasizes a probabilistic approach to fault detection and recovery, coupled with a novel voting scheme, to achieve both efficiency and resilience. This work aims to broaden the applicability of consensus mechanisms beyond specialized blockchain systems to a wider range of distributed applications.
This paper proposes a novel decentralized consensus algorithm designed to achieve Byzantine fault tolerance while simultaneously incorporating differential privacy mechanisms. The core idea is to leverage a blockchain-inspired architecture for transaction validation and agreement, enhanced with encrypted transactions and a randomized consensus protocol. Crucially, noise is injected into the consensus process to obscure individual contributions, thereby satisfying differential privacy constraints. The algorithm addresses the critical challenge of combining security and privacy in distributed systems, offering a robust solution for applications requiring both data integrity and user privacy protection. We formally define the algorithm, outlining the key components and their interactions, and analyze its properties regarding fault tolerance, privacy guarantees, and performance. The approach represents a significant step towards practical decentralized systems capable of handling malicious actors while respecting individual user data. The presented framework provides a foundation for future research and development in this increasingly important area.
This paper proposes a novel blockchain consensus mechanism termed "Distributed Proof-of-Work with Quantum Key Distribution" (DPW-QKD). The core idea is to leverage quantum key distribution (QKD) to replace computationally intensive hash functions in traditional Proof-of-Work (PoW) systems, thereby significantly reducing energy consumption. The system operates by nodes generating cryptographic keys through QKD, which are then utilized in a distributed QKD protocol to verify transaction validity. This approach eliminates the need for miners to solve complex cryptographic puzzles, creating a more energy-efficient and potentially more secure consensus model. The paper outlines the architecture, key components, and operational principles of the DPW-QKD system, highlighting its advantages and potential challenges. Mathematical formulations are presented to illustrate the key processes and security considerations within the system. The research aims to explore a viable pathway towards a sustainable and robust blockchain technology, driven by the inherent security of quantum mechanics.
Open access
2 source records
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
Yuji Sang, Chenglong Xu, Long Lv, Lijun Liu · 5 authors
Abstract Aiming at the security issues of open channel vulnerability, limited node resources, and single-point failure caused by centralized authentication in unmanned aerial vehicle (UAV) swarm networks, this paper proposes an authentication and key agreement scheme integrating Physical Unclonable Function (PUF), Chebyshev chaotic map and blockchain. The scheme constructs an integrated architecture of physical security, lightweight encryption and distributed trust, which supports mutual authentication in dual scenarios of UAV-Ground Control Station (GCS) and UAV-UAV. Decentralized trusted authentication is realized via blockchain and smart contracts, ensuring that authentication information is tamper-proof and traceable. Formal security verification based on the ROR model and informal analysis demonstrate that the proposed scheme satisfies multiple security requirements including anonymity and forward secrecy, and can resist common attacks such as replay attack, man-in-the-middle attack and physical capture attack. Performance evaluation results indicate that the scheme completes authentication with only two rounds of interaction. Its computational and communication overheads are significantly lower than those of existing schemes, making it suitable for resource-constrained UAV swarms.
ABSTRACT Smart cities are digitally advanced urban environments that are equipped with sensor networks to gather, share, and analyze extensive data across interconnected systems. Among various smart city applications, the intelligent transportation system represents one of the most critical and security‐sensitive domains. An intelligent transportation system relies heavily on continuous vehicular communication, a low‐latency decision‐making process, as well as real‐time traffic monitoring. Existing Internet of Things security methods encounter significant computational overhead and limited scalability, making them unfit for real‐time applications. To address these issues, this paper proposes a novel security model, named Deep Residual Stacked Bidirectional Network. The proposed system is integrated into a blockchain‐supported hybrid system to ensure security and privacy for users and systems in smart cities. This enhanced Deep‐Learning model combines the residual learning power with bidirectional long short‐term memory layers. To effectively manage deeper networks, residual connections help mitigate the vanishing gradient problem, while bidirectional long short‐term memory provides sequential dependencies in backward and forward directions. This allows the model to detect patterns in data, especially in security environments where data is highly dynamic and time‐sensitive. Four Internet of Things‐related datasets are used to evaluate the efficiency of the developed algorithm. These datasets offer various real‐world network traffic and attack scenarios that allow comprehensive performance evaluation of the proposed approach in comparison with existing methods. The test outcomes revealed that the blockchain‐supported proposed method outperforms traditional methods with an accuracy of 98.21%, specificity of 97.39%, and F1‐score of 97.46%.