Blockchain Papers

Jamal Hussain Arman, Fazal Muhammad, Muhammad Abdullah, Adil Zulfiqar

No abstract is available for this record.

J. J. Wang

This study investigates the theoretical foundations, practical applications, and optimization strategies of financial big data analysis and network security optimization in support of Sustainable Development Goals (SDGs). A comprehensive framework is developed to integrate sustainable financial management, environmental cost-benefit analysis, socially responsible investment decision-making, and sustainable supply chain management. The study further proposes a network security optimization architecture incorporating multi-level data encryption, access control, real-time threat monitoring, intelligent defense mechanisms, and blockchain-based data protection. The proposed framework is particularly applicable to communication-intensive environments, including wireless communication infrastructures and antenna-supported information transmission networks, where secure and reliable financial data exchange is essential. Experimental analyses demonstrate that the integration of financial big data technologies and network security mechanisms enhances data protection, operational efficiency, and sustainable decision-making capabilities. The results provide a practical reference for secure financial data governance and sustainable development in complex digital and communication-oriented systems.

Sandeep Girijashankar Shukla, Pradnya K. Bachhav, Mayuri K. Fulpagare, Abhijeet S. Kakade

No abstract is available for this record.

Sujan Mondal, Ankita Ray, Sorokhaıbam Khaba

Blockchain technology has revolutionized various industries by offering transparency, security, and decentralization. The critical aspect of blockchain technology is the consensus protocol, which plays a pivotal role in ensuring the integrity and reliability of distributed ledger systems. The selection of an appropriate consensus protocol for a given blockchain application is a complex and multifaceted decision-making process, influenced by various technical, environmental, and operational factors. This paper presents an integrated multicriteria decision-making (MCDM) approach to facilitate the selection of an optimal blockchain consensus protocol. Through a comprehensive evaluation of criteria, including performance, sustainability, incentives, security, and decentralization, our approach provides a robust decision-making framework for consensus protocol assessment. The results prioritize the importance of performance and security factors in blockchain consensus protocol evaluation. The sensitivity analysis is performed to determine the impact of experts’ weight coefficients on the result. The results prioritize the importance of performance and security in blockchain consensus protocol selection.

Ruixue Xu, Sijia Lian, Suhua Liu, Yuanjie Zhu

Aiming at the core pain points such as low execution efficiency, high resource consumption, and insufficient dynamic adaptability caused by the "deploy-and-freeze" characteristic of traditional blockchain smart contracts, this paper proposes a dynamic execution optimization scheme for smart contracts based on the Group Relative Policy Optimization (GRPO) algorithm [1]. Specifically, the "group sampling + relative advantage" mechanism, which is the core of the GRPO algorithm, is implemented through two key modules: for the group sampling module, the algorithm first divides the smart contract execution state space into multiple sub-scenarios based on key feature dimensions such as transaction type, data volume, and network congestion degree, then randomly selects 3–5 candidate execution actions from each sub-scenario and forms a candidate action group by fusing actions from different sub-scenarios; for the relative advantage calculation module, instead of adopting the absolute advantage evaluation method of the traditional Proximal Policy Optimization (PPO) algorithm [2], it introduces a relative advantage function that takes the average execution effect of the candidate action group as the reference benchmark, quantifies the advantage of each candidate action relative to other actions in the group through indicators such as Gas cost saving rate, execution delay reduction rate, and task completion rate, and weights the relative advantage values to determine the optimal execution action. With the GRPO reinforcement learning algorithm as the core driving force, the scheme constructs a three-layer collaborative architecture consisting of an off-chain Artificial Intelligence (AI) decision-making layer, an oracle data layer, and an on-chain contract execution layer. Through real-time state perception, group sampling action generation, and scenario-based reward function design, it realizes the dynamic adaptive adjustment of smart contract execution strategies. Experimental results show that in typical application scenarios such as Decentralized Finance (DeFi) lending and supply chain finance, compared with traditional static contracts and optimization schemes based on the mainstream PPO algorithm, this scheme can reduce the average Gas fee by 22%~25%, lower the non-performing loan rate from 3.2% to 1.1%, and control the execution response delay within 500ms, significantly improving the execution efficiency, resource utilization, and dynamic adaptation capability of smart contracts. This research provides a new technical path for solving the problem of dynamic execution of smart contracts and has important theoretical and practical significance for promoting the efficient and trusted operation of the Web3 ecosystem.

Yiwei Song, X. Q. Liu, Changyuan Jiang, Lei Cong · 5 authors

Centralized e-commerce recommenders face privacy risks, while Federated Recommendation Systems (FRS) suffer from accuracy loss in sparse environments and rely on untrusted aggregators. We propose BL-ZPRS, a framework utilizing bilayer zk-SNARKs for end-to-end trustworthiness. Its lower-layer User-to-Anchor (U2A) paradigm restores collaborative signals via verifiable vectors without exposing raw data, while an upper-layer ZKP proves FedAvg integrity. Evaluations on the Amazon Review dataset show BL-ZPRS achieves accuracy comparable to centralized models with superior resistance to poisoning attacks, effectively balancing privacy and integrity.

Okan Dardağan

This thesis presents a comprehensive predictive maintenance system and application interface that integrates deep learning and blockchain technologies in order to enhance maintenance strategies in industrial systems. Traditional predictive maintenance systems have significant issues regarding data security and decentralization. This study aims to address these limitations by leveraging blockchain technology, with a specific focus on improving the reliability and verifiability of predictive maintenance processes. In this study, an LSTM-CNN hybrid model was developed to evaluate complex patterns in both time and features, thereby enabling high-accuracy fault prediction. The proposed model is designed to perform binary classification for fault prediction in industrial equipment. During the implementation phase of the study, an open-source dataset was used to train and test the developed model. The Randomized Search method was used in the hyperparameter optimization process to increase the prediction success of the proposed model. The hybrid model was trained with 5-fold cross-validation, and class weighting and threshold value optimization methods were applied to eliminate the class imbalance problem. In the threshold optimization phase, F1-score-based methods are applied to maximize recall at three predefined minimum precision levels (0.05, 0.2, and 0.85), while identifying the most balanced trade-off between precision and recall. In the proposed system, sensor data are stored in a database (SQLite3), and cryptographic proofs generated using zero-knowledge techniques are transmitted to the Ethereum network. The Poseidon hash function is used to ensure data integrity, and the Groth16 protocol is used for Zk-Snark proof generation. This approach enables secure verification of data validity without publicly disclosing sensor data and simultaneously addresses scalability concerns. The system architecture is designed to include manager, operator, and engineer nodes, and all smart contracts are implemented using Solidity. In addition, a graphical user interface is developed using the Tkinter library in Python. The experimental results demonstrate that the proposed LSTM–CNN hybrid model produces successful outcomes in terms of fault prediction performance. According to scenario where the decision threshold is optimized based on the F1-score, the model achieves an accuracy of 0.987, an AUC value of 0.979, and an F1-score of 0.794. In future studies, the proposed system is planned to be implemented on the Ethereum mainnet instead of a test network, with a comprehensive evaluation of on-chain operational costs. However, instead of Zk-Snark proofs, which have a centralized structure, the use of Zk-Stark proofs, which are transparent and do not violate the principle of decentralization, is planned.

Rui Wu, Guangfu Wu, Kangjun Li, Lei Liu · 6 authors

As blockchain technology matures, the application scope of smart contracts continues to expand. Security issues in smart contracts have become an important research focus. In view of the limitations of traditional vulnerability detection methods such as limited vulnerability types and high false positive rate, this paper proposes a smart contract vulnerability detection model based on deep learning. By constructing token sequence sets and data flow graphs and inputting them into the pre-training model of this article, we obtained a pre-training model optimized for Solidity code and implemented a multi-label classifier in the downstream detection layer. The proposed model can detect integer overflow vulnerabilities, transaction sequence dependency vulnerabilities, timestamp vulnerabilities, and reentrancy vulnerabilities in the target contract. Experiments show that this model is better than other detection tools in detection effect.

Guodong Yi, Zhihui Wu, Xinyu Zhang, Xiaocui Li

Federated learning (FL) offers a distributed approach for the collaborative training of machine learning models across decentralized clients while safeguarding data privacy. This characteristic makes FL well suited for privacy-sensitive fields such as healthcare and finance. However, addressing the heterogeneity caused by nonindependent and identically distributed (non-IID) data remains a significant challenge for traditional FL methods. To address these issues, the enhancing clustered federated learning with adaptive similarity (AS-CFL) algorithm, which dynamically forms client clusters based on model update similarity and uses a forward-incentive mechanism to improve collaborative training efficiency among similar clients, is proposed in this study. Experimental results on the MNIST and EMNIST datasets reveal that compared with baseline methods such as the CFL, IFCA, and FedAvg models, the AS-CFL algorithm achieves faster convergence—reducing the number of communication rounds by approximately 20%—while maintaining competitive accuracy, demonstrating its effectiveness in heterogeneous FL scenarios.

Chandni Patel, Parth Sheth, Megh H. Shah, Dev Mehta · 8 authors

One of the main issues with the Industrial Internet of Things (IIoT) in V2X communication is the threat of attacks. A comprehensive Intrusion Detection System (IDS) and a transparent ledger are important for providing an Intelligent Transportation System (ITS) beyond 5G. However, another major problem is that it is centralized and lacks a clear explanation of traditional IDS. By integrating Federated Learning (FL) to make it distributed and Explainable AI (XAI) to add a brain to the black box model to add the explanation factor, we make the model more robust and suitable for real-life situations. In this approach, we experimented using the X-IIOTID dataset. This dataset is a real-time indicator of the attack in an IIoT network such as V2X. It provides difficult and real-time scenarios that highlight the complexity of IDS. Furthermore, the benign data the model classifies is stored in the blockchain to make the system secure and transparent. Our FL-XAI-based technique provides an accuracy of $98 \%$ results than previous models. The proposed approach provides a clear and brief view of factors that affect classification actions, which helps users make security decisions. Evaluation of Pravah based on latency, accuracy, precision, recall, F1-score, and ROC-AUC confirms its effectiveness. This study contributes towards a more secure and interpretable ITS, bridging the gap between model performance and real-world applicability.

Ping Wang, Fei Tang, Ankui Jing, Lei Liu · 6 authors

In vehicular ad-hoc networks (VANET), federated learning enables vehicles to collaboratively train global models for intelligent transportation without sharing raw data. However, global model training faces various potential risks, such as identity leakage, privacy inference, and malicious attacks, due to the dynamic network structure and untrusted wireless communication of VANET. To address these issues, a robust authentication mechanism for federated learning must be achieved to ensure the trustworthiness of model parameters. In this paper, we propose an efficient and privacy-preserving authentication scheme with lightweight encryption for federated learning in VANET using self-sovereign identity (SSI), called Fed-EALE. Fed-EALE constructs Merkle pseudonym identity trees with the aid of decentralized identifiers. Vehicle participants use unlinkable pseudonyms to achieve privacy protection. Fed-EALE utilizes verifiable credentials and zero-knowledge proof to build the authentication protocol to ensure the authenticity and integrity of model parameters from anonymous vehicles. In addition, to accurately identify and eliminate malicious participants in anonymous communications, Fed-EALE can track and recover the real identities of malicious vehicles. We perform a security analysis of Fed-EALE. Performance evaluations indicate that Fed-EALE reduces authentication overhead by approximately 76% compared to state-of-the-art protocols, while maintaining high stability and scalability in VANET.

Qingzhen Meng

Predictive maintenance in cross-border unmanned logistics systems (CBULS) faces persistent challenges, including data privacy, system heterogeneity, and collaborative efficiency. Existing studies that combine federated learning with blockchain address only partial aspects—such as communication or trust—but fail to effectively handle non-independent and identically distributed (non-IID) data, integrate multi-layer privacy, or design consensus mechanisms tailored to cross-border logistics. This paper proposes a predictive maintenance framework that integrates an improved FedProx algorithm with a hybrid Delegated Proof of Stake (DPoS) and Practical Byzantine Fault Tolerance (PBFT) consensus. The framework incorporates zero-knowledge proofs, fully homomorphic encryption, and local differential privacy, while employing hierarchical architecture and sharding for scalability. Simulation results show that the proposed method improves prediction accuracy by 6.9% compared with FedAvg and 3.7% compared with FedProx, enhances privacy protection by over 12%, increases system throughput by approximately 23%, and reduces transaction confirmation latency by nearly 18%. These results demonstrate that the framework provides a secure, efficient, and scalable solution for predictive maintenance in CBULS.

Jinkai Sun, Yulu Ren, J. Zhang, Xiaofang Chen

The Internet of Things (IoT) has not only significantly enhanced the efficiency of power marketing business systems but also introduced substantial security risks, particularly concerning the leakage and misuse of sensitive customer data. The current existence of a variety of data security auditing programs is more or less flawed, unable to comprehensively rule out the risk of data leakage. This paper proposes an IoT-driven blockchain-based fast traceability method for electricity marketing sensitive data using the Provenance Vocabulary Model (ProVOC), identifying power marketing sensitive data from the data flowing through the network, designing a structured storage model for sensitive data based on China’s ProVOC data traceability model standard, and then adopting blockchain technology to build a private Ether, generating a blockchain for data flow, reducing the storage space, and improving the speed of contract generation. This paper proposes a fast traceability method for power marketing sensitive data through three key innovations: a ProVOC-based identification mechanism that dynamically extracts sensitive data from network flows; a standard-aligned storage architecture compliant with China’s ProVOC traceability specifications; and a lightweight blockchain framework built on a privatized Ethereum network, which reduces storage overhead by 62% and accelerates smart contract deployment by 2.3 × compared to conventional approaches.

Iqra Nazir, Nermish Mushtaq, Hassam Ishfaq, Waqas Amin · 7 authors

This paper presents a novel framework for privacy-preserving, fair, and efficient peer-to-peer (P2P) energy trading within smart grids. The proposed approach introduces an auction-based dynamic pricing mechanism combined with a proportional energy allocation policy to ensure balanced benefits for both buyers and sellers. The pricing model adapts in real-time based on supply-demand ratios to optimize costs and revenues while maintaining fairness. To safeguard sensitive user data, a blockchain-based decentralized transaction system (DTS) is developed using Ethereum smart contracts,MetaMask, and Ganache. This system enables secure energy transactions without compromising privacy. The model is validated with real and simulated datasets, demonstrating significant reductions in buyer energy bills by 12.7% compared to existing methods. The proposed solution effectively balances economic optimization with data privacy, making it a robust framework for future decentralized energy markets.

Shijie Ji, Mingyang Lei, Zhuyu Shi, Weiming Hu · 5 authors

This paper presents the Federated Learning-Enhanced Distributed Ledger Framework (FL-DLF), a novel approach to address data rights confirmation in the evolving electricity market characterized by distributed energy resources and virtual power plants. The FL-DLF integrates blockchain technology with federated learning to ensure data integrity, security, and privacy, enabling decentralized data processing and model training without compromising sensitive information. By implementing advanced cryptographic methods, access controls, and continuous monitoring, the framework provides a secure and efficient solution for data rights management. Its modular architecture allows for seamless integration with current systems and adaptability to future technologies. The paper’s contributions include proposing a secure ecosystem for data asset ownership verification, introducing a comprehensive technical solution integrating blockchain and encryption, and exploring the use of smart contracts for automated and efficient data transaction processes. The FL-DLF addresses the challenges of data integrity, privacy, and infrastructure dependability, offering a robust solution for the modern electricity market’s complex data rights confirmation needs.

Mohammad Kamrul Hasan, S. Rayhan Kabir, Shayla Islam, Salwani Abdullah · 8 authors

The Smart Grid Artificial Intelligence-of-Things (SG-AIoT) uses SCADA (Supervisory Control and Data Acquisition) and AMI (Advanced Metering Infrastructure) to improve electricity distribution. However, the current SG-AIoT system faces challenges such as inaccurate energy forecasting, insecure data computation, information leakage, and data CIA (Confidentiality, Integrity, and Availability) issues. Smart-meter data has been provided to drive initiatives such as the UK’s "Low-Carbon-London" project, where the need for accurate forecasting and grid data security is felt. To address these issues, this paper proposes a secure federated learning framework that combines LSTM (Long Short-Term Memory) neural networks with AES (Advanced Encryption Standard) cryptographic blockchain across three-tier grid layers (Edge smart meters, Fog SCADA servers, and Cloud server). An AES-based blockchain framework is developed to secure data communication across all grid layers—edge, fog, and cloud. At the IoT smart meter edge nodes, a double-layered LSTM and dropout neural network is used for accurate energy demand forecasting. Forecasted data from each edge node is encrypted and aggregated at the fog layer using the AES blockchain. The proposed CBFedAggSum (Cryptographic Blockchain with Federated Learning Aggregation through Summation) method enables secure and efficient model aggregation at fog and cloud nodes. This end-to-end process strengthens grid data security and improves forecasting accuracy, effectively addressing key challenges in SG-AIoT systems. We compared our approach with six existing methods (FedAvg, FedSGD, SecAgg, 1D-CNN-GRU, PP-CE-FL, and Edge-Cloud-AIoT-FL). We found that our model outperformed them in forecasting accuracy and privacy preservation.

Mallikarjun Bellundagi

The rapid growth of intelligent systems has raised significant concerns regarding data privacy and security. Traditional centralized machine learning approaches require data aggregation, increasing the risk of data breaches and regulatory violations. Federated Learning (FL) has emerged as a promising paradigm that enables collaborative model training while keeping data decentralized. This paper presents a comprehensive study of federated learning for privacy-preserving intelligent systems, highlighting its architecture, methodologies, applications, and challenges. The study also proposes an adaptive federated framework integrating secure aggregation and differential privacy. The findings demonstrate that federated learning significantly enhances privacy while maintaining model performance, making it suitable for healthcare, finance, and IoT applications.

Rohit Rohit, Riya Sharma, Shreya Rai, Prakhar Singh · 5 authors

Land registry systems are essential for recording property ownership and transactions. Conventional system relies on centralized authorities. It maintains records of land ownership, which usually suffer from inefficiencies, lack of transparency, and susceptibility to fraud. In these systems, the process of transferring land ownership involves a lot of paperwork, lengthy verification procedures, and high transaction costs. It leads to delays and disputes. Blockchain technology offers a solution to address the shortcomings of conventional land registry systems like centralization, transparency, security. The paper proposes a blockchain-based land registration system employed on Ethereum. The smart contract of blockchain are developed in solidity programming language. It enables automated execution of land transactions that ensures trust value among all the stackholders. It eliminates manual intervention and reducing the risk of fraud and errors. Land ownership records are kept on InterPlanetary File System (IPFS) which is used for decentralized file storage. The proposed system promises to improve security, simplify processes, ensure trust among stakeholder. It provide transparent and immutable records of transaction and ownership.

Yu Li

With the advancement of intelligent power systems worldwide, the transition from traditional power grids to smart grids has significantly transformed power management. Smart grids rely on the real-time collection and aggregation of massive power data, where the aggregated results play a crucial role in load forecasting, equipment status monitoring, and fault detection. However, due to the privacy-sensitive nature of power data, there is a risk of privacy leakage during the aggregation process. While some studies have focused on ensuring privacy protection in data aggregation, challenges such as data consistency and validity verification remain unresolved. To address these issues, this paper proposes a privacy-preserving power grid data aggregation scheme based on blockchain and homomorphic encryption. The proposed scheme ensures privacy protection during data aggregation using homomorphic encryption, guarantees data consistency through blockchain and the Pedersen commitment mechanism, and verifies data validity by integrating blockchain with zero-knowledge proof techniques. Finally, theoretical analysis and experimental results demonstrate the feasibility and practicality of the proposed scheme.

Nisita Weerasinghe, Pawani Porambage, An Braeken, Madhusanka Liyanage · 5 authors

As 6G networks introduce increasingly diverse and complex applications, network slicing is a key enabling technology for partitioning network resources to meet these dynamic demands. However, efficiently managing and allocating these finite resources has become vital. This necessity drives the adoption of an open marketplace model. To address the business and technical complexities associated with such open marketplaces, this paper presents the demonstration of a non-fungible token (NFT)-enabled resource trading marketplace tailored for 6G network slicing. The proposed solution is implemented on an Ethereum-based blockchain system to assess its viability.

Ricardo Manzano, Marzia Zaman, Darshana Upadhyay, Nishith Goel · 5 authors

The integration of Internet of Things (IoT) devices into smart environments has become increasingly prevalent, resulting in the collection of valuable user and service data. However, effectively utilizing this data often requires its aggregation on a central server to train algorithms capable of identifying and preventing malicious attacks, such as reconnaissance, DoS (Denial of service), DDoS (Distributed denial of service) within IoT networks. This transmission of raw data not only incurs substantial bandwidth costs but also raises significant privacy concerns. In this paper, we propose a federated learning framework for intrusion detection on IoT networks that incorporates a distributed storage system based on the Ethereum blockchain, enhancing the security of the federated learning process. This design offers several key benefits, including scalability, high availability, redundancy, and the capacity to process large datasets. Despite these advantages, relying solely on federated learning may not yield accurate results, particularly when dealing with highly imbalanced datasets. To address this challenge, we have integrated a diffusion model for data augmentation at each local node, which strengthens model robustness. Furthermore, to protect data privacy at each local node, we utilize transmitting and averaging model parameters instead of raw data. The proposed framework is trained and evaluated in two datasets. The MNIST (Modified National Institute of Standards and Technology) dataset and BoT-IoT dataset. Our results indicate significant improvements in detecting zero-day attacks, achieving an average F1-score of 98.3% on the short version of the BoT-IoT dataset as well.

Radhey Shyam, Sheetal Sheetal, Ali Khalaf

No abstract is available for this record.

Yuhao Jin, Zhuo Zhang, Yang Lin

This study aims to explore a method for the de-anonymization of Bitcoin addresses based on Formal Concept Analysis (FCA).Although Bitcoin, as a decentralized cryptocurrency, offers user privacy protection, its anonymity has also been exploited by criminals, leading to an increase in illegal activities such as money laundering and terrorist financing.To address this challenge, we propose a novel deanonymization framework that constructs a formal context using Bitcoin transaction data and generates the corresponding concept lattice.By extracting the attribute weight vectors for each category, our model can effectively classify Bitcoin addresses, thereby identifying potential high-risk addresses.

Zhixian Zhou, Binbin Chen, Chen Sun, Peichang Zhang · 5 authors

Dynamic Spectrum Sharing can enhance spectrum resource utilization by promoting the dynamic distribution of spectrum resources. However, to effectively implement dynamic spectrum resource allocation, certain mechanisms are needed to incentivize primary users to proactively share their spectrum resources. This paper, based on the ERC404 standard and integrating Non-Fungible Token and Fungible Token technologies, proposes a spectrum securitization model to incentivize spectrum resource sharing and implements it on the Ethereum test net.