Srinivas. D, K. K. Rawat, Jiwanjot Kaur Hira, Sangeeta Bagga · 6 authors
The digital transformation of financial systems demands solutions that are secure, scalable, and capable of real-time processing. This paper presents an integrated framework that combines sixth-generation (6G) communication networks, blockchain technology, and artificial intelligence (AI) to address the growing challenges in financial data security and integrity. The proposed architecture leverages the low-latency and high-reliability features of 6G, the immutability of blockchain, and the adaptive capabilities of AI to support secure and automated financial operations. Key components include a layered network model, AI-driven anomaly detection, and privacy-preserving techniques such as federated learning and secure multi-party computation. The study outlines practical use cases including cross-border payments, central bank digital currencies (CBDCs), smart insurance, and decentralized identity verification. It also discusses limitations in scalability, regulatory compliance, and system interoperability. Future directions include quantum-resilient systems and the deployment of autonomous agents for real-time governance. The findings contribute to the design of secure and adaptive infrastructures for next-generation financial services.
Blockchain has evolved from cryptocurrency infrastructure to a foundation for decentralized finance, supply chain, and digital identity. However, widespread adoption faces three main barriers that are high energy use from traditional consensus, fragmented networks, and static, rule-based smart contracts. This work presents EcoChainX, a modular framework integrating AI-driven automation, sustainable consensus, robust interoperability, and advanced privacy features. Its four-layer architecture consists of AI modules for anomaly detection and smart contract optimization, energy-efficient consensus protocols, cross-chain interoperability, and privacy-preserving technologies such as zero-knowledge proofs and quantum-resistant cryptography. Through theoretical modeling, prototyping, and empirical testing, EcoChainX addresses scalability, sustainability, security, and privacy. By addressing them, this framework paves the way for responsible blockchain ecosystems capable of supporting the next generation of decentralized applications. Empirical results demonstrate that EcoChainX achieves a 97% reduction in energy consumption compared to traditional Proof-of-Work systems, increases transaction throughput by over 20 times (exceeding 10,000 TPS), reduces smart contract vulnerabilities by 60 % through AI-driven anomaly detection, and enables cross-chain transactions with a latency reduction of 80 %, establishing a new benchmark for sustainable and interoperable blockchain infrastructures.
M Savitha Devi, Ningthoujam Chidananda Singh, Thoudam Basanta Singh
Abstract - The explosion of Internet of Things (IoT) devices calls for the design of computationally light blockchain consensus mechanisms immune to quantum threats. The conventional consensus protocols such as Proof-of-Work (PoW) and Proof-of-Stake (PoS) may have quantum cryptanalysis and incur high computational overhead on resource-limited IoT devices. In this paper, we introduce QR-LightChain, a new quantum-robust light weight consensus algorithm with the combination of lattice-based cryptography and a brand-new Proof-of-Lightweight-Work (PoLW). Our proposal is based on formalism Learning With Errors (LWE) as a quantum resistant based scheme, also, but with the use of the adaptive difficulty tuning and energy efficient mechanism to validate the hashing. Experimental results show that QR-LightChain reduces the computational overhead by 52.3% with respect to traditional quantum-resistant approaches, while preserving security against both classical and quantum adversaries. The protocol shows good performance in IoT: The average block validation time of 1.2 sec is achieved and there is 40% less energy consumed than for current quantum-resistant consensus in the literature. Our work fills the important research challenge of providing 1 Post-Quantum Cryptography and Blockchain Modern internet of things (IoT) blockchain net- works are being developed in resource-constrained environments such as smart cities, while QCs Key Words: Quantum resistance, IoT blockchain, lightweight consensus, lattice-based cryptography, post-quantum cryptography, Proof-of-Lightweight-Work, resource-constrained devices
Open access
Blockchain Technology Applications and Security
Big Data and Digital Economy
Advanced Steganography and Watermarking Techniques
Aiming at the problems of insufficient integrity assurance, high risk of privacy leakage, and low cross-departmental circulation efficiency in the field of government data security, this study proposes an optimized blockchain solution integrating an improved Practical Byzantine Fault Tolerance (PBFT) consensus algorithm and dynamic Attribute-Based Encryption (ABE). By constructing a government data security storage model, the data layer's hash verification mechanism (SHA-256 + Merkle Patricia Tree) and network layer's P2P protocol (with packet loss retransmission) are optimized based on typical government blockchain platforms (e.g., Nanjing Electronic License Sharing Platform). Experiments with 200 nodes simulated government networks, comparing TPS, data verification delay, and privacy protection before and after optimization. The results show that after optimization: TPS increased by 37.2% (from 89.6 to 123.0), cross-departmental data verification time was shortened to 0.42 seconds, and zero-knowledge proof was used to realize anonymized query of sensitive fields (such as ID card numbers and real estate information). The research indicates that this technical solution can effectively resolve the contradictions among integrity, privacy, and circulation of government data, providing technical references for the engineering implementation of government blockchain systems.
As smart cities continue to evolve through the integration of IoT, AI, and big data analytics, the complexity and scale of cybersecurity challenges have intensified. This chapter examines key vulnerabilities ranging from data privacy breaches to cyber-physical system attacks and highlights the critical role of modern cryptographic technologies in addressing these risks. Techniques such as encryption, blockchain, PKI, homomorphic encryption, zero-knowledge proofs, and quantum cryptography are analyzed for their potential to strengthen smart city infrastructures. Drawing on recent case studies and emerging research, we further explore future directions, including post-quantum security and AI-driven cybersecurity frameworks. The findings of this chapter suggest that strategically embedding cryptographic solutions is essential for building secure, resilient, and citizen-centered smart cities capable of withstanding both current and emerging cyber threats.
In recent times., the accelerated growth of data has established big data analytics as an integral part of decision making in various industries., including healthcare., finance., manufacturing., retail etc. With the rise of big data., cloud computing., the relatively recent trend of using remote servers to hold and process data., has become a powerful tool for managing large datasets., providing storage and computational capabilities that are scalable., economical., and also elastic. But., when data is getting complex and distributed., security., privacy and data integrity have emerged as major concerns. The inherent features of blockchain technology like decentralization., immutability., and transparency can serve as a revolutionary alternative to tackle these challenges., while improving the efficiency and trustworthiness of big data analytics based on cloud. Data integrity., tampering prevention., and transparency-based auditing mechanisms help to secure data on cloud systems., and blockchain can improve these aspects. Additionally., the distributed nature of blockchain makes it possible to securely share data across different organizations without sacrificing privacy., which is especially important in industries like healthcare and finance. In addition., incorporating blockchain technology enables efficient tracking of data provenance., guaranteeing the verifiability of data origins and transformations., thereby enhancing trust and transparency. Blockchain has applied the security enhancement of cloud-based big data analytics platforms., which can also improve processing efficiency. Similarly., various consensus mechanisms used by blockchain i.e.., Proof of Work., Proof of Stake etc.., aid in cutting down the amount of time and resources required to validate and authenticate data in real-time., thus making a faster yet more secure processing time possible. Moreover., self-executing contracts (smart contracts) define terms that are directly built into the blockchain., and can automate data workflows and decisions., thus improving efficiencies. By ushering in new efficiencies that could sharply trim operational budgets and accelerate data- based decision-making across an array of sectors., these abilities hold immense potential.
Fine-tuning large language models (LLMs) is crucial for adapting them to specific tasks, yet it remains computationally demanding and raises concerns about correctness and privacy, particularly in untrusted environments. Although parameter-efficient methods like Low-Rank Adaptation (LoRA) significantly reduce resource requirements, ensuring the security and verifiability of fine-tuning under zero-knowledge constraints remains an unresolved challenge. To address this, we introduce VeriLoRA, the first framework to integrate LoRA fine-tuning with zero-knowledge proofs (ZKPs), achieving provable security and correctness. VeriLoRA employs advanced cryptographic techniques -- such as lookup arguments, sumcheck protocols, and polynomial commitments -- to verify both arithmetic and non-arithmetic operations in Transformer-based architectures. The framework provides end-to-end verifiability for forward propagation, backward propagation, and parameter updates during LoRA fine-tuning, while safeguarding the privacy of model parameters and training data. Leveraging GPU-based implementations, VeriLoRA demonstrates practicality and efficiency through experimental validation on open-source LLMs like LLaMA, scaling up to 13 billion parameters. By combining parameter-efficient fine-tuning with ZKPs, VeriLoRA bridges a critical gap, enabling secure and trustworthy deployment of LLMs in sensitive or untrusted environments.
With the extensive growth in terms of data and AI adoption across various fields such as finance, healthcare and insurance, data security and privacy have become significant barriers to innovation. This paper provides a privacy-aware framework for distributed AI as a possible solution which is integrated with cloud-native architectures. By leveraging decentralized model training without sharing raw data, this solution offers a compliant and secure framework for deploying machine learning at scale. A scalable and cost-effective system architecture is proposed that aligns with data protection regulations while maintaining high performance and model accuracy. This approach empowers organizations to leverage AI responsibly, unlocking the potential of sensitive data without compromising privacy.
The rapid growth of Internet of Things (IoT) devices has revolutionized digital ecosystems, enabling real-time automation and decision-making. However, the large volume and sensitivity of data from distributed IoT networks pose serious challenges in ensuring secure, scalable, and trustworthy data sharing. Centralized architectures are prone to failures, breaches, and limited auditability. This paper proposes a blockchain-integrated framework to overcome these limitations in heterogeneous IoT environments. The model utilizes distributed ledger technology (DLT) to ensure tamper-proof data integrity, decentralized control, and verifiable audit trails. Smart contracts dynamically enforce access policies, while attribute-based encryption (ABE) provides fine-grained control over data access. Lightweight blockchain nodes are deployed at the edge to reduce latency and reliance on cloud infrastructure. A layered architecture ensures seamless interaction among IoT devices, edge nodes, and cloud systems. To evaluate the framework, a prototype was built using ESP32 sensors, Raspberry Pi 4 edge nodes, and a Hyperledger Fabric blockchain network. Data encryption and smart contract execution were tested under realistic conditions. Results show enhanced data security, access control, and system scalability with minimal performance overhead. This framework provides a practical solution for secure data sharing in IoT ecosystems and is well-suited for deployment in smart cities, healthcare, and industrial automation.
With the widespread application of blockchain technology, the security of private transactions has become a bottleneck restricting further development. This project presents a blockchain privacy transaction optimization model utilizing zero-knowledge proof (ZKP). By extracting data features such as transaction volume, transaction frequency, and counterparty trustworthiness, the model dynamically assigns weights through an entropy-based framework for different transaction scenarios. It also adaptively modifies certificate generation and verification strategies using reinforcement learning to enhance efficiency and security. In terms of experiments, a blockchain simulation environment is constructed, and 100,000 transaction data points are used as samples to compare the DA-ZKP algorithm and the traditional zero-knowledge proof algorithm. The experimental results show that the DA-ZKP algorithm reduces the generation time by 35%, the verification time by 28%, and the memory overhead by 22% on average. At the same time, the algorithm has a privacy protection capability comparable to traditional algorithms and can resist replay and tampering attacks. The optimization model and algorithm proposed in this project can effectively improve the efficiency and security of blockchain privacy transactions and provide a new idea for developing blockchain privacy protection technology.
Abstract In the context of the IoT platform, the smart home represents a quintessential application scenario. Here, device-to-device (D2D) collaboration serves as the core element of its ecosystem, playing a crucial role in implementing diversified automated execution scenarios that are customized to fulfill user requirements. The progressive integration of edge computing and AI technologies has enhanced the collaboration among heterogeneous devices. Nevertheless, the conventional centralized D2D collaboration authorization decision-making supported by a single IoT Hub violates the Principle of Least Privilege (PoLP), which is a foundational design tenet that has been empirically validated as an optimal engineering practice for enhancing system security and reliability in IoT ecosystems. If there is a trade-off of PoLP violations, it fails to meet the users’ Quality of Experience (QoE). To address this issue, we propose D2DA, a distributed authorization decision-making model for smart home D2D collaboration, which constructs a distributed decision-making consensus network suitable for the edge side of smart homes by leveraging distributed ledger technology. D2DA presents a machine learning algorithm with a time complexity of O ( n ). Through this algorithm, consensus nodes can be efficiently and dynamically selected. Furthermore, D2DA ensures the security of the D2D collaboration process via wallets and hash verification. Extensive experiments conducted on a real-world smart home scenario validate that the decision-making latency of D2DA is on par with that of a single IoT Hub mode. The average latency for verifying the correctness of the newly added execution results is only 0.08% of the system time of D2DA, which is negligible.
The fact that data generated in distributed sources grows exponentially poses great hurdles on the centralized machine learning workflow, especially regarding data privacy, latency and bandwidth overhead. Federated Feature Stores (FFS)Constitute a new paradigm that perpetrates real-time learning in which data is not aggregated. In this paper, one such architecture is provided in which feature engineering, storage and access, are decentralized and co-located with data sources taking advantage of the principles of federated learning. To satisfy the need to maintain data locality but guarantee consistency, low latency inference and privacy regulations, FFS uses a combination of on-device feature computation combined with updating global models. We are working on federated feature synchronization, version control and optimal caching for heterogeneous environments. The experimental analysis over edge clusters and cloud back ends reveals significant bumps in the end-to-end training throughput, inference latency, and privacy preservation over conventional centralized feature pipelines. The given FFS framework provides a scalable, privacy-sensitive, and efficient replacement of older data engineering pipes in the fields of healthcare, finance and the Internet of Things.
Blockchain-based systems ensures data immutability and traceability, making them well suited for decentralized content governance. However, conventional metadata anchoring strategies remain static and task-agnostic, resulting in suboptimal trade-offs between auditability, privacy, and scalability. To address this limitation, we propose a Task-Driven Dynamic Metadata Mapping (TDMM) mechanism that adapts anchoring strategies based on task semantics and user roles. TDMM classifies metadata into three distinct anchoring types: full on-chain anchoring for audit-critical tasks, selective disclosure via zero-knowledge proofs (ZKPs) for sensitive attributes, and off-chain reference anchoring for general-purpose metadata. A dedicated mapping controller dynamically routes metadata fields to the appropriate anchoring mode according to predefined task policies. To preserve privacy without sacrificing verifiability, TDMM incorporates a hybrid ZKP architecture that processes proofs off-chain while anchoring verification results on-chain. We implement TDMM on a Hyperledger Fabric network, augmented with IPFS and Circom-based ZKP tooling. Experimental results show that TDMM significantly reduces on-chain storage overhead, lowers re-identification risk, and supports task-appropriate latency and throughput trade-offs, demonstrating its effectiveness in balancing transparency, privacy, and scalability in decentralized metadata governance.
Securing and protecting privacy of patient records are crucial challenge in block chain. In this paper, proposed Secure Hybrid Framework for Blockchain(SHFB) is confronted by integrating AES-GCM and ECC with Zero Knowledge Proofs and Fully Homomorphic Encryption (FHE) to enhance the security efficiency. The exponential growth of healthcare data necessitates secure and efficient frameworks to handle real time processing and protection against cyber threats. This paper presents the SHFB (Confronted) model, a block chain integrated architecture optimized for high throughput, robust denial of service (DoS) resistance, and low latency performance in electronic health record (EHR) systems. Comparative analysis reveals that SHFB surpasses conventional models such as Decentralized Security, AI Privacy and Secure EHR in key metrics, making it a scalable and secure solution for modern healthcare infrastructure. Patient records were used to conduct experimental evaluation and demonstrate that SHFB significantly reached high to compare with existing models. Particularly SHFB achieves an encryption time of 0.02 seconds, security efficiency of 95%.The proposed method shows the effectiveness of hybrid method by enhancing the performance and security of block chain in management of data using healthcare.
Amjad Almaghthawi, Wael M.S. Yafooz, Nasser S. Albalawi
In decentralized apps, smart contracts are used to conduct trusted transactions on the Blockchain (BC). While smart contracts are highly effective, they are also highly susceptible to security flaws, leading to serious financial consequences. However, the combination of BC technology and artificial intelligence provides a solution for powerful, secure, and decentralized applications in various sectors. Furthermore, large language models (LLMs), which are essential advanced machine learning frameworks, are now used in various applications, including customer service, chatbots, code generation, vulnerability detection, and language translation. This study investigates the use of LLMs for automated vulnerability detection in Solidity-based smart contracts. Specifically, three models are evaluated and compared: GPT-3.5-turbo, DeepSeek R1, and LLaMA-3. With a labeled, multi-class dataset including four vulnerability types, the models are assessed across three reasoning strategies: zero-shot, few-shot, and chain of thought. A prompt-based evaluation and performance comparison is conducted using standard metrics such as accuracy, precision, recall, F1-score, and average detection time. Results show that in the zero-shot setting, GPT-3.5-turbo achieves the highest accuracy of 94.59%, followed closely by LLaMA-3 with 92%, while DeepSeek R1 achieved 78.95%. In the few-shot setting, LLaMA-3 outperformed other models. Furthermore, in the CoT setting, LLaMA-3 demonstrates the strongest overall performance with 96% accuracy and an F1-score of 0.82, surpassing DeepSeek R1's average of 78.95% and GPT-3.5's CoT performance, which is notably lower. Hence, this study develops an evaluation framework for LLM-based vulnerability detection, and we have demonstrated that prompt engineering has the potential to enhance the security of smart contracts.
Aydin Abadi, Basil Aldali, Benito Vincent, Elliot A. J. Hurley · 13 authors
Weather forecasting plays a vital role in disaster preparedness, agriculture, and resource management, yet current centralized forecasting systems are increasingly strained by security vulnerabilities, limited scalability, and susceptibility to single points of failure. To address these challenges, we propose a decentralized weather forecasting framework that integrates Federated Learning (FL) with blockchain technology. FL enables collaborative model training without exposing sensitive local data; this approach enhances privacy and reduces data transfer overhead. Meanwhile, the Ethereum blockchain ensures transparent and dependable verification of model updates. To further enhance the system's security, we introduce a reputation-based voting mechanism that assesses the trustworthiness of submitted models while utilizing the Interplanetary File System (IPFS) for efficient off-chain storage. Experimental results demonstrate that our approach not only improves forecasting accuracy but also enhances system resilience and scalability, making it a viable candidate for deployment in real-world, security-critical environments.
The exponential growth of IoT data demands efficient, secure, and scalable storage solutions on one hand, and efficient data migration and retrieval on the other hand are essential for the systems to be practical and acceptable for different applications. The traditional cloud-based models face latency, security, and high operational costs, while existing bi-directional data storage and retrieval-based IPFS models are not computationally efficient and incur high gas costs at the cost of a necessary blockchain deployment. To overcome the challenges of efficient data migration, we initially developed a 2-way data storage and retrieval system as well as a scalable framework that dynamically monitors and transfers device-generated data to IPFS, records the content identifier(CID) on a blockchain, and enables secure, real-time access via smart contracts. Experimental results demonstrate that the existing work achieved an average data upload time of 117.12 sec for a file size of 500 MB; our framework achieves a faster upload time of 7.63 sec, marking a 93.47% improvement. We further optimize the proposed framework to reduce the file upload time incurred from the smart contracts by introducing a blockchain-inspired, lightweight, and customizable Python framework that replicates the storage and retrieval functionalities of a traditional blockchain, where the file upload time is 4.2 sec, further optimized by 45% from our previous approach, thus demonstrating its efficiency, security and suitability for deploy ment in real-time and critical IoT applications and outperforming the existing IPFS-smart contract based solutions.
Digital payments play a pivotal role in the burgeoning digital economy. Moving forward, the enhancement of digital payment systems necessitates programmability, going beyond just efficiency and convenience, to meet the evolving needs and complexities. Smart contract platforms like Central Bank Digital Currency (CBDC) networks and blockchains support programmable digital payments. However, the prevailing paradigm of programming payment logics involves coding smart contracts with programming languages, leading to high costs and significant security challenges. A novel and versatile method for payment programming on DLTs was presented in this paper - transforming digital currencies into token streams, then pipelining smart contracts to authorize, aggregate, lock, direct, and dispatch these streams efficiently from source to target accounts. By utilizing a small set of configurable templates, a few specialized smart contracts could be generated, and support most of payment logics through configuring and composing them. This approach could substantially reduce the cost of payment programming and enhance security, self-enforcement, adaptability, and controllability, thus hold the potential to become an essential component in the infrastructure of digital economy.
Gunasekar Thangarasu, Kayalvizhi Subramanian, Nattar Kannan, Carmel Mary Belinda M J · 6 authors
Blockchain technology has become a changing agent in many different sectors when open, permanent decentralized ledgers are presented. But the spread of several blockchain systems has upset ecosystems, so networks find it more difficult to exchange data and operate coherently. Currently mostly applied is the answer for interoperability issues in homogeneous blockchain systems, so creating a major hole in communication between many platforms. This work intends to develop an algorithm and conceptual framework to enable communication between two independent blockchain systems, Ethereum and Hyperledger Fabric. Using an Interoperability between Hyperledger Fabric and Ethereum Using Hyperledger Cactus (IHFEUC) approach, it evaluates numerous blockchain interoperability for organ donation system. Then criteria are created to choose Hyperledger Fabric and Ethereum as models of interoperability testing. Hyperledger Cactus conceptual framework employing IHFEUC makes possible the elements and connections between the two networks. By way of simple interoperable communication between Ethereum and Hyperledger Fabric, experimentally achieved results guarantee efficiency and interoperability in organ donation transactions.
Purpose: This research addresses critical limitations in existing blockchain-based data sharing solutions by developing an innovative framework integrating zero-knowledge proofs, homomorphic encryption, and smart contract automation for comprehensive big data privacy protection while maintaining utility and regulatory compliance. Methodology: A hierarchical distributed architecture comprising four layers was designed: data owner layer for encryption, blockchain network layer for consensus, privacy protection layer for cryptographic protocols, and application service layer for user interactions. Experimental evaluation was conducted on distributed networks with 20-100 nodes processing$100 ~\text{GB}-5 ~\text{TB}$datasets. Findings: The proposed framework achieves$\text{9 4. 1 \%}$privacy protection strength with$\text{2 2 \%}$computational efficiency improvement compared to existing approaches. The system supports 100 -node deployments while maintaining 131-158 TPS throughput, significantly outperforming traditional zero-knowledge implementations that achieve only 89.3 % privacy strength. Conclusion: The framework represents significant advancement in blockchain-based big data privacy protection, successfully balancing security guarantees with computational efficiency. Practical Implications: The solution demonstrates substantial value for healthcare, financial services, and IoT applications requiring secure collaborative analytics and enterprise-scale data sharing scenarios.
With the increasing security issues in blockchain, smart contract vulnerability detection has become a research focus. Existing vulnerability detection methods have their limitations: 1) Static analysis methods struggle with complex scenarios. 2) Methods based on specialized pre-trained models perform well on specific datasets but have limited generalization capabilities. In contrast, general-purpose Large Language Models (LLMs) demonstrate impressive ability in adapting to new vulnerability patterns. However, they often underperform on specific vulnerability types compared to methods based on specialized pre-trained models. We also observe that explanations generated by generalpurpose LLMs can provide fine-grained code understanding information, contributing to improved detection performance. Inspired by these observations, we propose SAEL, a LLMbased framework for smart contract vulnerability detection. First, we design prompts targeting specific smart contract vulnerabilities to guide general-purpose LLMs in detecting vulnerabilities and providing explanations. The detection results generated by LLMs serve as prediction features. Then, we employ prompt-tuning on CodeT5 and T5 respectively to process contract code and explanations, enhancing model performance on specific tasks. To leverage the strengths of each component, we introduce Adaptive Mixture-of-Experts, a dynamic architecture for smart contract vulnerability detection. This mechanism dynamically adjusts feature weights through a Gating Network, which selects the most relevant features by applying TopK filtering and Softmax normalization, and a Multi-Head Self-Attention mechanism, which enhances cross-feature relationships by processing multiple attention heads in parallel. This design ensures that prediction results for LLMs, explanation features, and contract code features are effectively integrated through gradient optimization. The loss function focuses on the independent prediction performance of each feature and the overall performance of weighted predictions. Experimental results show that SAEL outperforms existing methods in detecting various vulnerabilities.
One of the main concerns in blockchain (BC) systems is throughput growth. To solve this issue, several improvements and a new gossip algorithm are put forth; however, they are inevitably limited by the message synchronization latency of the underlying peer-to-peer (P2P) network. The current gossip algorithms still have certain drawbacks, such as high latency, low throughput, energy waste, and higher network connection requirements. A very effective BC gossip algorithm is necessary to reduce delivery latency and boost transaction throughput for BC systems. All transactions and activities are centrally stored in the distributed ledger, or BC. Bitcoin and other public blockchains emphasize decentralization, which necessitates high throughput and increases communication costs. Consequently, the hyperledger fabric BC technology employs the joint-graph based delegated practical byzantine fault tolerance (JG-DPBFT) consensus mechanism. The enhanced gossip algorithm (IGA) is used to finish consensus authentication and information exchange. NS-3 is used to implement the JG-DPBFT protocol, and its performance is assessed by contrasting it with other approaches. Consequently, the testing results demonstrated that the JG-DPBFT outperformed the current techniques in terms of throughput and latency.