Federated Learning (FL) has emerged as a distributed paradigm enabling multiple participants to collaboratively train machine learning models without sharing their raw data. By keeping data local, FL mitigates many privacy risks inherent to centralized learning architectures. However, despite this promise, recent research has revealed that exchanged gradients can still leak sensitive information about local datasets. Furthermore, most existing approaches rely on strong and often unrealistic trust assumptions toward the central server, while providing no means to verify whether privacy-preserving mechanisms have been correctly enforced. These limitations expose a critical gap between theoretical privacy guarantees and their practical implementation in real-world federated systems.This thesis investigates how to bridge this gap by combining differential privacy (DP) with cryptographic and verifiability protocols to achieve verifiable and trust-reduced federated learning. First, we explore the use of additive homomorphic encryption to protect client updates and minimize reliance on a trusted aggregator. Second, we introduce a non-interactive verifiability protocol based on zk-SNARKs and cryptographic hashes, allowing third parties to prove and verify the correct application of DP without revealing sensitive information. Finally, we propose ProoFed, a distributed framework that leverages secret sharing to decentralize noise generation and integrate verifiable aggregation proofs in zero knowledge, thereby eliminating single points of trust.
Damilare E. Bakare, Adekemi Olawunmi Amoo, Mary T. Onifade
The health insurance sector has been facing many challenges recently, such as fraudulent activities in insurance claims, data breaches, and high transaction costs, particularly with existing systems built on the Ethereum network, which negatively affect its efficiency and effectiveness.These challenges undermine the trust and financials of insurance providers while compromising the privacy of the patient's health records.To address this issue, this study proposes a conceptual framework that uses zero-knowledge proof within the blockchain system and is deployed on the Polygon Network for its low transaction fees and higher throughput.The proposed model allows the verification of an insurance claim without revealing sensitive patient health records, ensuring privacy while preventing fraudulent activities.In this conceptual design, the hospital can issue verifiable proof of treatment, appointment, and bill that shows the validity of the insurance claim without revealing the underlying health record to the insurer.This study, therefore, contributes to supporting research in decentralized applications for healthcare insurance by presenting a conceptual model and comprehensively analyzing the feasibility, rather than a full-scale implementation.It also emphasizes the need to preserve privacy in sensitive domains and the potential benefits of blockchain and ZKP integration.In conclusion, the research's findings show that, in theory, integrating ZKP with blockchain technology can enhance healthcare insurance processes in terms of reliability, efficiency, privacy, and security.However, further research and practical development are required to realize and evaluate a fully operational system.
Chaoming Shi, Haomeng Xie, Zheng Yan, Laurence T. Yang
Blockchain is a decentralized ledger with a secure and immutable chain structure. The advanced attributes of blockchain, including decentralization, anonymity, transparency, and zero trust support, have positioned it as a transformative technology across different areas of expertise, like medicine, finance, and the Internet of Things (IoT). Nonetheless, blockchain’s progress has been constrained in various aspects, revealing inefficiency, privacy, high transaction fees, and challenges with on-chain storage. To address these limitations, off-chain technology has emerged as a solution by moving computation and storage overhead away from the blockchain. However, a comprehensive survey on off-chain schemes is lacking in the current literature. In this article, we conduct a thorough survey on off-chain technologies. We first introduce the fundamental concepts and characteristics of both blockchain and off-chain technologies. Furthermore, we establish a thorough taxonomy of off-chain technologies based on distinct application scenarios. We put forth a series of evaluation criteria, based on which we seriously review and analyze the existing off-chain schemes to assess their strengths and limitations. Conclusively, we outline a list of open issues and propose promising future research directions based on our thorough review and analysis on off-chain technologies.
Blockchain is the root behind rise of cryptocurrencies. Blockchain minimizes the dependency of intermediaries that are required to accomplish a transaction, thus moving towards a more simplified procedure with reduced price and increased speed. It acts as a distributed ledger where each and every transaction or procedure is auditable and verified in real time. Once the data is recorded on the ledger then it can’t be altered later on. Along with rapid digitalization, the rise of cyber vulnerabilities can adversely impact organizations, resulting in detrimental outcomes. Organizations must be prepared with an effective cyber strategy so that they don’t fall prey to cybersecurity breach. Blockchain’s unique features can be considered as defensive wall that promotes robust workflow within the organizations against cyber attackers. Decentralization and enhanced security of blockchain can evolve the area of cybersecurity. Scalability is a substantial problem in blockchain. With the increase in total number of records, the block size also expands, that leads to slower verification procedure. Flaws in digital signatures, weak or inaccurate keys and incompetent encryption strength can result in critical security concerns. Downsides of transparent ledger should be considered to gain wider acknowledgment. In this chapter we will go through introduction to blockchain and cybersecurity, literature work conducted by different researchers in this domain, comparative analysis of different blockchain solutions, blockchain cybersecurity concerns, real-world case studies of this area, prevention of security issues of blockchain and conclusion.
The rise in Bitcoin and Ethereum is well-known. Research shows that public sentiment greatly affects their price changes. Thus, public sentiment analysis is a key factor in making investment decisions. This study analyzes public sentiment towards Bitcoin and Ethereum on platform X using Graph Neural Networks (GNN), specifically the GCN and AGN-TSA models. GCN is utilized for its ability to capture syntactic relationships between words, while AGN-TSA integrates textual content with user-level social interactions through an attention mechanism. The dataset is collected from$X$using the keywords Bitcoin, BTC, Ethereum, and ETH, followed by preprocessing and labeling based on FinBERT and Vader as a benchmark labeling technique to construct the training and test sets. Evaluation employs a confusion matrix to compare model performance. The results show that GCN without addressing class imbalance achieves 83.89 % accuracy, whereas AGN-TSA achieves 86.21%. However, confusion matrix analysis revealed severe bias toward the majority class, so we needed to use extreme class weighting (ratio 17:1:10), which improved minority-class recall. However, it caused training instability and reduced accuracy: GCN dropped to 80.58 %, and AGN-TSA dropped to 86.15% (a decrease of$0.06 \%)$. Despite the decrease, AGN-TSA still achieves the best accuracy compared to GCN, this result reinforcing our initial hypothesis that attention-based graph modeling which leverages social ties yields superior performance for sentiment classification in crypto related discourse. Furthermore, the accuracy of the data labeling technique and the imbalance in the label distribution also affect the final accuracy results.
The article presents an empirical comparison of three contemporary Layer-2 scaling solutions for the Ethereum blockchain: Scroll, Linea, and Base, representing zk-rollup and optimistic rollup architectures. The study aims to evaluate the transaction processing speed and stability of selected Layer-2 networks using real-time data collected from blockchain explorers (Blockscout, Lineascan, Basescan). The dataset comprises 45,000 transactions processed in October 2025 and aggregated at one-second resolution (1 Hz). Statistical analyses include ANOVA, Kruskal–Wallis, Levene, and Brown–Forsythe tests, as well as ADF and KPSS stationarity diagnostics, used to assess diferences in throughput and operational stability across the examined networks. The results indicate that the Base network achieves the highest mean throughput (≈ 102 TPS) and the lowest temporal volatility, whereas Linea and Scroll exhibit non-stationary, highly variable transaction dynamics driven by periodic batching. The fndings confrm the persistence of the scalability trilemma—where improvements in performance may come at the cost of higher centralization and operational dependency. This research contributes to the quantitative assessment of rollup efciency and provides a reference point for further empirical studies on blockchain scalability.
Blockchain and artificial intelligence (AI) are reshaping the financial landscape by improving security, operational efficiency, and intelligent automation. Blockchain&s;s decentralized and tamper-proof ledger fosters transparency and trust in financial transactions, while AI enhances decision-making through advanced data analysis, fraud detection, and risk management. Together, their convergence supports a wide range of applications, including decentralized finance (DeFi), asset tokenization, algorithmic trading, and robo-advisory services. Blockchain provides a secure infrastructure for AI-driven financial innovations by ensuring data integrity and minimizing dependence on intermediaries. In parallel, AI improves blockchain performance by automating smart contracts, refining predictive models, and streamlining compliance mechanisms. To better understand and structure this integration, the chapter introduces the Techno-Financial Synergy Framework (TFSF), which connects the technological enablers, strategic drivers, and outcomes of AI-blockchain convergence. While the synergy holds great promise, it is also accompanied by challenges such as computational overhead, scalability constraints, interoperability gaps, and evolving regulatory landscapes. However, emerging solutions such as zero-knowledge proofs, homomorphic encryption, and next-generation consensus protocols are gradually addressing these limitations. As these technologies continue to evolve, their convergence is expected to drive the next phase of digital financial transformation, fostering a smarter, more secure, and inclusive financial ecosystem.
Blockchain technology, and more broadly distributed ledger systems, hold the promise of transforming financial infrastructures by enabling more transparent, auditable, and tamper-resistant systems. It is used to facilitate cross-border payments, bond issuance, and insurance processes. This technology has also fostered the emergence of decentralized finance (DeFi), which allows financial operations, such as lending and asset tokenization, to be conducted without relying on traditional financial intermediaries. However, beyond this ambition of disintermediation, the economic and technical reality reveals a more nuanced landscape. While the original promise of blockchain was to eliminate trusted third parties, in practice we are witnessing a transformation rather than a disappearance of their role. The landscape of trust has been reshaped around new intermediaries, such as cryptocurrency exchanges, digital asset custodians, and stablecoin issuers, which now play central roles within the ecosystem. The adoption of blockchain technology nonetheless remains constrained by energy costs, scalability limitations, and strict regulatory requirements. Achieving a sustainable balance between performance, security, and compliance therefore represents a key challenge for blockchains in financial applications. In this context, this thesis explores how the judicious integration of trusted components can enhance the efficiency and resilience of financial blockchains. It makes three main contributions. First, we demonstrate that adding a trusted component to each node is insufficient to improve Byzantine Reliable Broadcast resilience: even with attestations preventing equivocation, the threshold remains at n >= 3t+1 because malicious processes retain the selective omission strategy. This negative result establishes that naive integration of trusted hardware is ineffective and that a fundamental protocol redesign is necessary. Second, it introduces new Byzantine Reliable Broadcast protocols that leverage a minimal trusted component, a Trusted Monotonic Counter at the initiator, to achieve optimal resilience with only n >= 2f+1 processes, while reducing message complexity by nearly half compared to classical approaches. Third, it introduces TenderTEE, a new consensus protocol extending Tendermint (recently renamed Ignite) through TEE attestations, thereby reducing the number of required validators and communication costs while maintaining both safety and liveness. TenderTEE not only decreases the number of necessary nodes (or, for a given number of nodes, increases Byzantine fault tolerance) but also enables governance schemes characteristic of financial systems, such as tripartite structures, which were previously infeasible under classical consensus protocols tolerating one-third of Byzantine nodes. In addition to that, the thesis provides broader design guidelines for building efficient blockchains in the financial sector, highlighting the complementary role of trusted components alongside Proof-of-Stake mechanisms, sharding, and optimistic protocols.
Blockchain consensus mechanisms form the backbone of decentralized systems by ensuring agreement among distributed nodes without a central authority. At the core of these mechanisms lie number-theoretic foundations, including cryptographic primitives such as modular arithmetic, hash functions, elliptic curve cryptography, and zero-knowledge proofs. These mathematical constructs enable secure transaction validation, identity verification, and resistance against adversarial attacks. This paper presents a systematic review of number-theoretic foundations underpinning blockchain consensus mechanisms, focusing on methods, architectural implementations, and emerging research directions. The study analyses widely adopted consensus algorithms such as Proof of Work (PoW), Proof of Stake (PoS), and Byzantine Fault Tolerant (BFT) protocols, highlighting their dependence on number theory for ensuring security, randomness, and fairness. A comprehensive review of 30 studies published between 2018 and 2023 is conducted to examine advancements in cryptographic techniques such as verifiable random functions (VRFs), homomorphic encryption, and zero-knowledge proofs. These techniques play a crucial role in improving scalability, privacy, and efficiency of blockchain systems. The findings reveal that while number-theoretic approaches provide strong security guarantees, challenges such as computational overhead, scalability, and energy consumption persist. The paper concludes by identifying future research directions, including post-quantum cryptography, lightweight cryptographic protocols, and AI-assisted consensus optimization.
The continuous growth of interconnected systems, cloud services, and Internet-of-Things (IoT) devices has expanded the attack surface and intensified modern cyber risks, revealing significant weaknesses in centralized security architectures. Blockchain technology, characterized by decentralized control, immutable record-keeping, and cryptographic verification, offers a robust alternative for strengthening cybersecurity across multiple operational domains. This review analyzes the core technical components of blockchain such as distributed ledgers, consensus mechanisms, and network models and explains their relevance to enhancing security functions. It further examines practical applications in network protection, identity and access management, IoT device security, cloud data governance, and software supply-chain assurance. It highlights emerging research directions, including lightweight blockchain solutions for constrained IoT environments, cross-chain security architectures, artificial intelligence-based blockchain threat analytics, and quantum-resilient cryptographic infrastructures.
Mutiullah Shaikh, Uffe Kock Wiil, Ali Ebrahimi, Yumna Memon
Blockchain technology has revolutionized digital systems by ensuring trust, transparency, decentralization, and security. However, in the democratic nature of blockchain networks, there is a huge underlying dependency on consensus mechanisms, but the challenges associated with these, such as energy costs, network attacks, preservation of privacy, centralization, and limited scalability, hinder miners and stakeholders from adopting appropriate consensus mechanisms. In this paper, we present a conceptual literature overview of most consensus mechanisms by highlighting potential areas of exploration and considerations before adopting blockchain technology for various applications. This exploration turned our focus toward analyzing three prominent underlying aspects of consensus mechanisms, i.e. energy consumption, security, and decentralization. A simulation-based comparative analysis of five prominent blockchain consensus mechanisms, such as Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), Proof of Authority (PoA), and Proof of Capacity (PoC), is presented in various network load scenarios to further evaluate their performance metrics. The simulated metrics were cross-validated using empirical data from real blockchain networks (e.g., Ethereum, Bitcoin, VeChain, and Chia) collected between 2022 and 2025, ensuring alignment between theoretical performance models and observed on-chain behavior across diverse consensus mechanisms. Results overall indicate that PoW excels in decentralization and security while costing the highest energy, making it less scalable for high-throughput scenarios. PoS balances energy efficiency and moderate decentralization, while DPoS achieves scalability at the expense of decentralization. PoA and PoC are shown to be energy-efficient alternatives, but vary in their levels of centralization and security. Our findings constitute a comprehensive guide for researchers, miners, and practitioners aiming to optimize blockchain performance for diverse applications.
Raghavan Sheeja, Sherwin Richard R., Shreenidhi Kovai Sivabalan, Srinivas Madhavan
<p>The generational improvement has significantly converted several industries, and the area of intellectual property rights (IPR) isn’t any exception. IPRs, being as important as they are, need to be securely managed in some way. Blockchain, with its decentralized and immutable nature, gives a promising answer for enhancing the management of intellectual property (IP). This paper explores the strategic integration of blockchain generation for the control of IPR. The proposed system consists of a complete system, from registration and validation to predictive evaluation and royalty distribution, all facilitated through clever contracts. The use of zero-knowledge proofs guarantees the safety and confidentiality of sensitive information. The paper discusses the advantages and future implications of implementing this type of device.</p>
The proliferation of Internet of Things (IoT) and Cyber-Physical Systems (CPS) has ushered in an era of unprecedented data generation and automation. However, this interconnectivity introduces profound security and privacy challenges, particularly concerning the centralized aggregation of sensitive data for machine learning model training. Traditional cloud-centric approaches are vulnerable to single points of failure, data breaches, and privacy infringements. This paper investigates the synergistic integration of two transformative technologies-Blockchain and Federated Learning (FL)-as a robust framework to address these critical shortcomings. Federated Learning enables the collaborative training of machine learning models across distributed devices without centralizing raw data, thereby preserving data locality and privacy. Blockchain technology complements this by providing a decentralized, immutable, and transparent ledger to orchestrate the FL process securely. It facilitates trustworthy model aggregation, verifiable participant contribution, and resilient consensus mechanisms, mitigating risks such as model poisoning and malicious node attacks. The integration establishes a verifiable and auditable trail for all transactions within the FL lifecycle, from participant selection to global model updates. This research delineates the architectural principles of this integration, analyzes its efficacy in enhancing security and privacy for IoT and CPS, and discusses prevailing challenges and future research trajectories aimed at realizing a secure and trustworthy intelligent infrastructure.
In the decentralized Internet environment, growing awareness of user data sovereignty has raised higher requirements for privacy protection in blockchain scenarios. To enhance the security and controllability of data authorization, this study develops a model integrating zero-knowledge proof (ZKP), field disclosure control, and multi-party joint verification. The ZKP ensures verifiable privacy, field disclosure control minimizes data exposure, and multi-party verification strengthens consistency and tamper resistance. Through this collaborative integration, the model forms a unified framework for secure and transparent data authorization. Experimental results on two blockchain datasets show that the model outperforms comparison approaches in authorization accuracy, field matching consistency, and verification efficiency, achieving a minimum verification loss of 0.248 and a true positive rate of 96.8%. Under simulation conditions, it maintains stable performance across different complexity levels, with authorization accuracy of 95.1% and field validation consistency of 96.5%. Compared with traditional single-mechanism methods, the model delivers comprehensive improvements in privacy strength, verification transparency, and collaborative trust, demonstrating strong potential for application in high-sensitivity blockchain privacy protection scenarios, particularly in privacy-critical domains such as healthcare record management, financial data exchange, and supply chain traceability.
With the increasing maturity of blockchain technology, its characteristics such as decentralization, data immutability, and consensus mechanisms can effectively address issues in supply chain finance, including high risk control costs, difficulties in credit endorsement for small and medium-sized enterprises, and cumbersome operational processes. By synthesizing research on the integration of blockchain technology into supply chain financial services and analyzing a case study of JD.com’s application of blockchain technology in supply chain finance ABS business, this paper proposes future development prospects for “blockchain technology + financial services”. The aim is to provide decision-making references for the modern financial services industry to expand operations, improve service performance, and reduce financial risks.
Federated learning (FL), which allows collaborative machine learning without requiring the centralisation of sensitive data, has become a game-changing concept for private and safe data analysis in decentralised networks. FL enables edge devices or local nodes, such as smartphones, IoT devices, or healthcare facilities, to learn shared models remotely and send only model changes to a central server, in contrast to traditional methods that call for raw data aggregation. This framework lowers communication overhead, mitigates regulatory problems, and greatly improves data privacy and security. FL provides a workable and scalable way to create superior machine learning models in decentralised networks, where data is naturally dispersed and frequently subject to stringent privacy laws. Nevertheless, there are still issues to be resolved, such as managing non-IID data, making sure that systems are resilient to hostile attacks, and preserving effective communication. To further improve FL's privacy-preserving capabilities, recent developments like homomorphic encryption, safe multiparty computation, and differential privacy are being incorporated. This study examines the fundamentals of federated learning, goes over important methods for improving its security and privacy, and talks about how it may be used in a variety of industries, including as healthcare, finance, and smart cities. FL is one of the most important steps to safe and ethical AI in decentralized environments because it allows the collaboration of intelligence and preserves data ownership.
Tanim Hasan Ovi, Ifftekher Hossain Mrida, Fatema Tuj Tarannom Esty, Safayet Ahmed · 5 authors
Regular crowdfunding platforms are excited by the mystery, centralization, and abnormally high rates, and, in turn, the search for less traditional ways for this purpose. In this paper, we present the work of designing and evaluating a donation system to leverage the Ethereum-supported blockchain along with emerging web tools. The objective is a transparent, auditable system. Users can launch fundraisers, donate Ethereum, and monitor their progress. Actors are struggling to keep up. The core mechanical pieces are driven by smart contracts written in Ethereum’s Solidity. These contracts define the driving terms of the agreement, including funding amounts, degree of wet service access, and agreed-upon payment plan, cash call, and payback. The front end is integrated with React.js, and Ethers.js is an easy-to-use client library that uses blockchain to communicate with MetaMask. Functionality was tested with starting drives, donations, and withdrawals using development environments of Hardhat, Ganache, and Remix IDE. Avg. Transaction processing costs were in the ballpark of 200,000 Gas units, which was acceptable for Layer-1 Ethereum networks. The system is designed with security features, implementing access control, timestamp verification, and reentrancy protection as a step toward addressing common smart contract vulnerabilities. We will soon integrate DAO governance, the ability to convert fiat to crypto, phone support, and enhanced Layer- 2 scaling.
Blockchain technology has evolved from its origin as the foundation of cryptocurrencies into a versatile, decentralized framework for secure data management. Its core features include decentralization, immutability, transparency, and cryptographic security which enable trustworthy interactions without centralized authority. This review presents a comprehensive examination of blockchain fundamentals, including architecture, consensus mechanisms, and smart contracts, followed by applications across finance, supply chain, healthcare, IoT, and government systems are highlighted.
Despite the recognition of Blockchain Technology’s disruptive potential, there is ongoing debate about its ontological and axiomatic foundations. This study develops a theoretical framework to explain the underline structural principles of blockchain technology through the lens of Arthur’s theory of technology, and the framework is developed through adopting Narrative Literature Review. By integrating conceptual analysis with a structural examination of Ethereum, this study reveals that blockchain technology is not a single invention but a composite technological system developed through recursive interactions among sub-technologies. The proposed framework identifies three interrelated structural patterns—the Combinatorial Pattern of Components elucidating blockchain technology’s structural ontology, the Capturing Pattern of Algorithms revealing the operational source of its innovation, and the Recursive Pattern of Technologies characterizing its inner logical structure of components—that together explain blockchain technology’s generative and evolving nature. The study extends Arthur’s theory by clarifying the “technology within technology” dynamic that underlies blockchain technology innovation. The Ethereum case confirms the framework’s applicability and generalizability, showing that blockchain systems, despite their diversity, share a consistent structural logic. Beyond its theoretical contribution, the framework offers practical guidance for sustainable technological innovation. It provides analytical support for designing blockchain-based applications’ architectures that enhance transparency, efficiency, and adaptability, contributing to the sustainable evolution of digital technologies.
Cloud Computing (CC) is an excellent platform that is widely used to share information as well as services between various departments, customers, and other parties. However, during the transmission of sensitive data, effective security management is essential to ensure data privacy by avoiding unauthorised access. To address this, an efficient key agreement protocol, which is a combination of Elliptic Curve Cryptography with Zero Knowledge Proof (ECC-ZKP) model, is proposed to enhance security management in a cloud environment. The ECC-based encryption model provides equivalent security with much smaller key sizes compared to traditional cryptography approaches. Moreover, this lightweight model reduces computational load and speeds up operations in large-scale cloud environments. The integration of the ZKP model allows users to prove their identity and get access without disclosing any sensitive credentials, which leads to an increase the data confidentiality. Initially, the login and registration of users in the cloud is done by submitting all their details to the service provider. Then, a hash value and bound values are computed, and a secret key is generated by the service provider, which is sent to the client for accessing the data from the cloud. The experiment results of the proposed ECC-ZKP model achieved Makespan of 4.8 ms for 100 tasks, which is better than traditional key agreement models, such as ECC-based authenticated key agreement, respectively.
Deepika Dash, Bipin Raj C., B Jnyanadeep, Anala M R
The proliferation of decentralized finance (DeFi) has highlighted critical challenges in cross- chain oracle reliability and performance assessment. Traditional blockchain networks remain isolated from external data sources, creating the fundamental Oracle Problem that hinders institutional adoption of DeFi protocols. This paper presents DeFiLens, a comprehensive benchmarking framework that provides standard- ized performance metrics and real-time analytics across multiple blockchain ecosystems including Ethereum, Binance Smart Chain, Polygon, and Avalanche. Our framework addresses the gap between traditional finance’s seamless market data access and blockchain’s data isolation through systematic oracle assessment. DeFiLens implements a six-layer security scoring system encompassing cryptographic verification, attack detection, and network health monitoring. Through extensive evaluation of major oracle providers including Chainlink, Band Protocol, and Tellor, we demonstrate significant performance variations across chains, with response times ranging from 2.1 seconds to 8.7 seconds and reliability scores varying between 72% and 95%. Our statistical analysis reveals critical arbitrage opportunities with price discrepancies up to 2.3% across chains. The framework serves as a ‘‘Bloomberg Terminal’’ for oracle data, enabling financial institutions, DeFi protocols, and researchers to make data-driven decisions for oracle integration and risk management.
K.S. Weedagamaarachchi, H. D. Vithanage, H. M. S. N. Dehipola, K.D.R. Manditha · 6 authors
Smart contracts on the Ethereum blockchain enable automation and transparency in decentralized applications; however, their scalability is often constrained by high gas costs resulting from inefficient code design. This research investigates the relationship between cyclomatic code complexity and gas consumption, proposing a design pattern-based approach to optimize gas efficiency in Ethereum smart contracts. Three common Solidity patterns Factory, Registry, and State Machine were optimized using both existing and novel techniques, including variable packing, the use of fixed-size data types (uint256, bytes32), immutable variables, and mapping simplification. These optimizations were implemented and tested within a real-world coco peat supply chain management system to measure their impact on gas usage. Experimental results showed that deployment gas costs decreased by approximately 19 % and runtime execution gas by around 14 %, confirming that design-level optimization can significantly reduce costs without affecting functionality. The findings demonstrate that structured refinement of contract design can enhance scalability, making blockchainbased enterprise solutions more efficient and economically sustainable.
Smart contracts have been widely applied in various fields. Due to the immuta-bility of data on the blockchain, it is of great significance to conduct smart con-tract vulnerability detection before data is uploaded to the chain. To address the problems of low accuracy and single vulnerability type in traditional detection methods, a blockchain smart contract vulnerability detection method based on Graph Neural Network (GNN) is proposed. This method abstracts the functions and key code segments in smart contracts into nodes in a graph, and constructs edges by leveraging data and control dependencies during code execution, thereby accurately depicting the specific graph structures of reentrancy attacks and timestamp-dependent vulnerabilities. To further enhance the model’s sensi-tivity to key vulnerability patterns, the multi-head attention mechanism is in-novatively introduced, which can effectively screen out the nodes and edges that contribute the most to vulnerability detection, suppress irrelevant or noisy information, and significantly improve the accuracy and robustness of vulnera-bility detection. Experimental results show that the proposed method achieves an accuracy of 85.19% in reentrancy vulnerability detection and 82.37% in timestamp-dependent vulnerability detection, demonstrating excellent vulner-ability identification capability.
In the contemporary digital landscape, the demand for secure, private, and tamper-resistant communication has never been more critical. Conventional messaging platforms, which predominantly rely on centralized servers, are increasingly vulnerable to data breaches, unauthorized surveillance, and censorship. Even with the adoption of end-to-end encryption, these systems remain susceptible to single points of failure and metadata exposure, undermining user privacy and trust. Blockchain technology has emerged as a transformative solution to these challenges, offering a decentralized, immutable, and transparent infrastructure for secure data exchange. By leveraging distributed ledger technology, Blockchain-based messaging systems eliminate the need for trusted intermediaries, enhance resistance to censorship, and ensure data integrity through consensus-driven validation.