Blockchain has emerged as a robust foundation for decentralized trust, secure data sharing, and immutable record keeping. However, its inherently transparent architecture creates significant privacy challenges when applied in sensitive domains such as healthcare, finance, identity management, and IoT. Although privacy-preserving techniques including Zero-Knowledge Proofs (ZKPs), Attribute-Based Encryption (ABE), homomorphic encryption, ring signatures, mixers, and hybrid off-chain storage mechanisms have demonstrated partial effectiveness, they remain limited by high computational overhead, poor scalability, interoperability constraints, and regulatory complications. These challenges hinder the practical deployment of blockchain in real-world, data-intensive environments. This review examines key blockchain privacy issues and synthesizes major research contributions from contemporary literature. It further emphasizes the importance of hybrid privacy-preserving models to balance transparency, confidentiality, and storage efficiency. The analysis reinforces the relevance of solutions such as ChainGuard, a dual-chain architecture that maintains sensitive data on a private blockchain while using a public chain to store verifiable hash references. This approach directly mitigates the transparency–privacy conflict, storage inefficiencies, and cryptographic performance limitations identified across existing studies. The paper concludes by outlining research gaps and proposing future directions for scalable, interoperable, and regulation-aligned blockchain privacy systems.
Incident reporting systems are integral to maintaining accountability and transparency across critical domains such as cybersecurity, healthcare, and public governance. However, existing centralized mechanisms are prone to manipulation, data loss, and unauthorized modifications. This paper proposes 'IntegriChain', an intelligent and decentralized incident reporting framework that combines Blockchain technology and Artificial Intelligence (AI). The system ensures tamper-proof data storage through SHA-256 hashing and distributed ledger technology while leveraging AI for incident classification, anomaly detection, and risk prediction. This hybrid approach improves security, reliability, and efficiency in reporting workflows. The framework is designed to serve as a scalable solution applicable to multi-domain reporting systems where trust, immutability, and intelligent analysis are critical.
<p>Document forgery remains a pervasive problem across education, government, and trade sectors. This paper presents a blockchain-based digital document verification system built on the Internet Computer Protocol (ICP). The approach computes SHA‑256 hashes of documents and anchors them to ICP canister smart contracts, ensuring integrity and non-repudiation without storing document contents. The system manages a registry of approved verifiers so that only trusted institutions can enroll documents. In evaluation with 15 documents (85–3025 KB) and five repeated trials per document, the prototype achieved an average verification time of 1.54 s and an accuracy of 99%. Compared with Ethereum-based baselines in prior work, the ICP-based design avoids gas fees and reduces verification latency. The proposed architecture supports future integration of zero-knowledge proofs (ZKP) to validate authenticity while preserving privacy.</p>
Open access
Blockchain Technology Applications and Security
Big Data and Digital Economy
Physical Unclonable Functions (PUFs) and Hardware Security
Smart contracts have significantly advanced blockchain technology, and digital signatures are crucial for reliable verification of contract authority. Through signature verification, smart contracts can ensure that signers possess the required permissions, thus enhancing security and scalability. However, lacking checks on signature usage conditions can lead to repeated verifications, increasing the risk of permission abuse and threatening contract assets. We define this issue as the Signature Replay Vulnerability (SRV). In this paper, we conducted the first empirical study to investigate the causes and characteristics of the SRVs. From 1,419 audit reports across 37 blockchain security companies, we identified 108 with detailed SRV descriptions and classified five types of SRVs. To detect these vulnerabilities automatically, we designed LASiR, which utilizes the general semantic understanding ability of Large Language Models (LLMs) to assist in the static taint analysis of the signature state and identify the signature reuse behavior. It also employs path reachability verification via symbolic execution to ensure effective and reliable detection. To evaluate the performance of LASiR, we conducted large-scale experiments on 15,383 contracts involving signature verification, selected from the initial dataset of 918,964 contracts across four blockchains: Ethereum, Binance Smart Chain, Polygon, and Arbitrum. The results indicate that SRVs are widespread, with affected contracts holding $4.76 million in active assets. Among these, 19.63% of contracts that use signatures on Ethereum contain SRVs. Furthermore, manual verification demonstrates that LASiR achieves an F1-score of 87.90% for detection. Ablation studies and comparative experiments reveal that the semantic information provided by LLMs aids static taint analysis, significantly enhancing LASiR's detection performance.
Software services are crucial for reliable communication and networking; therefore, Site Reliability Engineering (SRE) is important to ensure these systems stay reliable and perform well in cloud-native environments. SRE leverages tools like Prometheus and Grafana to monitor system metrics, defining critical Service Level Indicators (SLIs) and Service Level Objectives (SLOs) for maintaining high service standards. However, a significant challenge arises as many developers often lack in-depth understanding of these tools and the intricacies involved in defining appropriate SLIs and SLOs. To bridge this gap, we propose a novel SRE platform, called SRE-Llama, enhanced by Generative-AI, Federated Learning, Blockchain, and Non-Fungible Tokens (NFTs). This platform aims to automate and simplify the process of monitoring, SLI/SLO generation, and alert management, offering ease in accessibility and efficy for developers. The system operates by capturing metrics from cloud-native services and storing them in a time-series database, like Prometheus and Mimir. Utilizing this stored data, our platform employs Federated Learning models to identify the most relevant and impactful SLI metrics for different services and SLOs, addressing concerns around data privacy. Subsequently, fine-tuned Meta's Llama-3 LLM is adopted to intelligently generate SLIs, SLOs, error budgets, and associated alerting mechanisms based on these identified SLI metrics. A unique aspect of our platform is the encoding of generated SLIs and SLOs as NFT objects, which are then stored on a Blockchain. This feature provides immutable record-keeping and facilitates easy verification and auditing of the SRE metrics and objectives. The automation of the proposed platform is governed by the blockchain smart contracts. The proposed SRE-Llama platform prototype has been implemented with a use case featuring a customized Open5GS 5G Core.
Luiz Eduardo Folly de Campos, Reinaldo Cézar de Morais Gomes
Trust on the internet is an essential pillar for online interactions, and blockchain technologies offer a new paradigm of trust based on data integrity and decentralization, enabling innovative solutions such as theWeb3 applications. This paper presents the experimental infrastructure for blockchain research and development currently being built within the ILIADA project at RNP, and its use for the development of new Web3 applications.
Ejiro U, Osiobe, Waleed A., Hammood, Safia, Malallah, Nyore E., Osiobe · 6 authors
Quantum mechanics principles underpin quantum computing, signaling a major shift in how we process information. While it offers immense processing power and potential advantages, it also presents significant challenges for the cryptocurrency industry. This sector has grown rapidly, supporting decentralized finance and empowering users worldwide, but it also attracts malicious actors looking to exploit its vulnerabilities. Traditional cryptography remains strong, yet increasingly sophisticated computational attacks threaten security. As the cryptocurrency market expands, quantum computing offers both opportunities, such as improved transaction security, and risks, like easier decryption for hackers. Understanding quantum technology’s benefits and challenges is crucial as it develops. Currently, data is protected by traditional cryptography, but future, more powerful quantum computers could weaken this security. This article explores potential uses of quantum computing in daily life and business, explains its functions simply, and discusses societal impacts. Its goal is to help students and general readers understand how quantum technology might transform our world through clear language and real-life examples. Topics include the basics of quantum computing, its present and future applications across industries, and its societal effects. We provide a thorough analysis of how quantum computing could reshape society through mathematical insights, practical examples, and future perspectives.
Ihunanya Udodiri Ajakwe, Victor Ikenna Kanu, Simeon Okechukwu Ajakwe, Dong‐Seong Kim
The Korean Emission Trading Scheme (K-ETS) is vital for reducing carbon emissions in South Korea. However, issues in transparency, security, and computational overhead limit its effectiveness. This work proposes an energy-efficient blockchain-based framework (eBCTC) to enhance the system with a decentralized blockchain architecture, Purechain. The framework leverages an improved consensus mechanism, the Proof of Authority and Association (PoA 2 ). This is to address key challenges in the current K-ETS, such as centralization, lack of transparency, and high energy consumption. The PoA 2 significantly reduces gas usage, with experimental results showing a 22 % reduction in gas consumption compared to traditional Proof of Work (PoW) and Proof of Authority (PoA) mechanisms. Also, PoA 2 recorded a ×6 and ×2 reduction in gas price compared to PoW and PoA. The system also achieves faster transaction finality and lower computational costs, with transaction costs reduced by up to 83 % across the key K-ETS activities, including emissions reporting, credit allocation, and trading. Also, the system achieved moderate throughput, high latency, doubling scalability, high reliability, and a high success rate compared with DPoS and PBFT based on transaction stress validation tests. With an improved smart contract, intelligent automation of key functions, the system achieved a high energy gain for improved incentives. The proposed framework not only enhances the scalability and transparency of K-ETS but also aligns with South Korea's carbon neutrality goals by minimizing the environmental impact of blockchain operations. This study provides a solid foundation for sustainable carbon trading systems and an accountable carbon economy, contributing to global efforts to combat climate change in achieving the 2050 net-zero carbon emissions goal. • Purechain PoA2 enables secure, low-energy carbon trading in K-ETS. • 22 % less gas usage and 83 % lower costs than PoW and PoA. • Smart contracts automate K-ETS compliance and incentives. • Improves scalability, transparency, and network reliability. • Supports South Korea's 2050 net-zero carbon goal.
Based on the characteristics of blockchain such as decentralization, independence, security and anonymity, audit entities can explore the feasibility and application logic of its application in food security audits by leveraging technologies such as smart contracts, consensus mechanisms, asymmetric encryption and distributed ledgers. The article first analyzes the current situation and problems of food security auditing at both the practical and theoretical levels. Then, it constructs a logical framework for the application of blockchain technology in food security auditing by combining the advantages of blockchain technology, and specifically elaborates on the food security auditing process under blockchain technology. Finally, fully consider the problems faced by blockchain technology in its application and make prospects for its future development.
Joel Poncha Lemayian, Ghyslain Gagnon, Kaiwen Zhang, Pascal Giard
Ethereum leverages smart contracts (SCs) to power decentralized applications (dApps), with execution handled by the Ethereum virtual machine (EVM) within an Ethereum client. Other blockchain platforms, including Avalanche, Polkadot, Aurora, and Cardano, have also adopted the EVM. However, the performance of the EVM is often constrained by the limitations of general-purpose processors, a challenge that has been explored in the literature. This work aims to further address the limitation by proposing EVMx, a dedicated single-core SC execution engine implemented on a field programmable gate array (FPGA). EVMx follows a processor-like architecture inspired by the RISC philosophy. By exploiting the parallelism and high-speed processing capabilities of FPGA hardware, EVMx achieves a 61% to 99% reduction in execution time for commonly used operation codes compared to traditional central processing unit (CPU)-based environments. Furthermore, EVMx executes entire Ethereum blocks with a percentage reduction in execution time between 6% and 56% against comparable FPGA implementations and 98% to 99% compared to CPU-based EVMs in the literature. These results demonstrate the potential of EVMx to significantly accelerate SC execution and enhance the performance of EVM-compatible blockchains.
With the advancement of the information age, the widespread application of electronic evidence in fields such as justice and finance has brought new challenges. Although existing blockchain electronic evidence sharing schemes have immutability and transparency, they still have shortcomings in access control, data privacy protection, and efficiency. In addition, traditional attribute encryption strategies lack effective revocation mechanisms and cannot fully protect privacy when implementing fine-grained access control. Therefore, in order to address the above limitations, a blockchain electronic evidence sharing scheme based on an improved ciphertext policy attribute encryption combined with zero knowledge proof technology has been proposed. The research innovatively introduces revocable ciphertext strategy encryption, which addresses the security risks caused by decryption key leakage through revocation function, ensuring the secure storage and sharing of electronic evidence. Meanwhile, the study also improved the PBFT consensus algorithm to enhance its performance in handling large volumes of transactions. The results showed that the storage TPS of the research model reached 492, and the query TPS reached 655. The computational cost of improving the PBFT consensus algorithm is 1.94 × 10 4 , and the maximum computational cost of the electronic evidence access control model based on zero knowledge proof is 509. Compared with traditional blockchain based electronic evidence sharing methods, the improved method not only enhances storage and sharing efficiency, but also further strengthens privacy protection capabilities by combining zero knowledge proof technology. In summary, the research method effectively achieves secure sharing and privacy protection of electronic evidence on blockchain, providing support and reference for electronic evidence storage in fields such as justice and finance. However, there are still challenges in terms of scalability and data storage in the research, so algorithms can be optimized in the future to further improve the application scope of the system.
Because of the rapid acceleration of cloud computing, data transfer security and intrusion detection in cloud networks have become emerging areas of concern. All traditional security mechanisms have central vulnerabilities, cannot detect real-time threats, and are ineffective against zero-day attacks. Signature-based approaches of existing intrusion detection systems (IDS) do not cover the dynamically changing nature of cyber threats. Conventional blockchain security methods suffer from poor scalability and dynamic threat analysis. Therefore, this research proposes integrating Ethereum Blockchain and Deep Learning to construct a well-founded security framework for cloud networks with data migration security and real-time intrusion detection. The architecture has five distinct methods, each of which deals with particular security issues. Blockchain-Aware Federated Learning for Secure Model Training (BAFL SMT) guarantees tamper-proof and decentralized deep learning model training, which reduces model poisoning attacks by 98.4%. Graph Neural Networks for Adaptive Intrusion Detection (GNN-AID) captures graph structures for real-time anomaly detection in networks while reducing false positives to 1.2%. Quantum-inspired Variational Autoencoders (QI VAE ZDAD) provide enhanced zero-day attack detection, with an improved detection rate of 92%. Self-Supervised Contrastive Learning for Blockchain Security Auditing (SSCL-BSA) detects smart contract vulnerabilities automatically, resulting in an 87% reduction in fraud risk. Finally, Hierarchical Transformers for Secure Data Migration (HT SDM) enhance the transfer security of large-scale cloud data, achieving an attack classification accuracy of 99.1%. Overall, this multi-layer security framework will greatly enhance cloud security by preserving data integrity, cutting down the intrusion detection time by up to 65%, and enhancing response mechanisms. By marrying the immutable transparency of blockchain with superior anomaly detection at deep learning, this research provides a scalable, real-time, and intelligent approach to strengthening security against the backed-up transfer of data within cloud networks.
Electronic Health Records (EHR) is the main core of modern healthcare, but interoperability across different blockchain platforms is a key challenge. This work proposes a cross-chain middleware architecture, which facilitates secure and real-time synchronization of EHR data between Hyperledger Fabric (private blockchain) and Ethereum Sepolia Testnet (public blockchain). The framework integrates AES-256 encryption and Inter Planetary File System (IPFS) as decentralized storage to enhance patient privacy. To facilitate interoperability across the blockchains the research introduces a smart middleware layer. This layer autonomously monitors the blockchain events, processes encrypted CIDs, enforces real time cross chain consistency and smart contract-based access control. The experimental evaluation shows that proposed framework achieves low synchronization times (< 195 ms), low gas and latency costs, small encryption overhead (< 4–5 KB), robust file storage and retrieval through IPFS. Such positive evaluations with scalable and real-time deployment, sets the foundation of patient centric interoperable healthcare ecosystems.
A systemic "evidentiary deficit" now characterizes automated global civicsystems, undermining regulatory oversight, institutional accountability, and public trust in high-stakes domains. The increasing opacity of high-speed, algorithmically-driven decisions in finance, public health, and environmental governance creates un-auditable risks. This report posits Ternary Logic (TL) as a neutral, non-ideological infrastructure framework designed to remediate this deficit. TL extends traditional binary logic by introducing a formal, third logical state: 0 (Epistemic Hold), distinct from 1 (Proceed) and -1 (Halt). This 0 state functions as a mandatory, auditable "computational hesitation" triggered by predefined uncertainty or risk thresholds. By instrumenting this pause, TL transforms deliberation and uncertainty from an operational failure into a cryptographically verifiable evidentiary asset. This report details the TL architecture through its Eight Pillars, which provide an integrated "accountability stack" mapping institutional policy to cryptographic proof. It describes the tri-cameral governance model—Technical Council, Stewardship Custodians, and Smart Contract Safeguard—architected for long-term resilience and prevention of institutional capture. Furthermore, it details the technical architecture, including a dual-lane, low-latency (<300ms) design, a hybrid-shield (public/private) ledger system, and a novel cryptographic stack (combining Ephemeral Key Rotation, Zero-Knowledge Proofs, and Cryptographic Erasure) that simultaneously satisfies regulatory demands for auditability, legal requirements for privacy (e.g., GDPR), and commercial protection of trade secrets. This framework provides a sovereign-grade blueprint for establishing provable accountability in systems governed by institutions such as the Bank for International Settlements (BIS), U.S. Securities and Exchange Commission (SEC), U.S. Food and Drug Administration (FDA), and World Health Organization (WHO).
John Adeyemi O, Folasade Yetunde Ayankoya, Kuyoro S. O
The advancement of technology has positioned blockchain and machine learning (ML) as transformative forces in finance. Blockchain’s decentralized structure ensures secure and transparent transactions, while ML processes vast data to identify patterns and enhance decision-making. Their integration offers significant potential for fraud detection, risk assessment, and transaction optimization. Blockchain provides a tamper-proof environment, ensuring data integrity and reducing fraud. Meanwhile, ML detects anomalies, predicts market trends, and automates processes, improving financial security and efficiency. However, challenges such as scalability, computational demands, and data privacy hinder widespread adoption. Blockchain struggles with high costs and limited throughput, while ML requires significant resources and quality data. Emerging solutions like federated learning for privacy-preserving ML, zero-knowledge proofs for secure transactions, and hybrid blockchain models for scalability aim to address these challenges. Overcoming these barriers will enable a more secure, efficient, and data-driven financial ecosystem.
Ethereum ist seit Jahren die größte Smart-Contract-Blockchain und nach Bitcoin die zweitgrößte Blockchain-Plattform. Smart-Contracts, die als dezentrale Anwendungen beschrieben werden können, laufen auf einer gemeinsamen Rechenplattform, auf der alle Teilnehmer auf einer geteilten Codebasis arbeiten. Zur Absicherung ist es nötig, dass ein Konsens über die Ein- und Ausgaben aller Smart-Contracts geschaffen wird. Die Ausführung von Smart-Contract-Code verbraucht sogenannte Gas-Einheiten, die als eine Art Treibstoff betrachtet werden können. Gas-Einheiten zeigen den erforderlichen Rechenaufwand an und haben direkte Auswirkungen auf den realen Energieverbrauch. Daher sollten idealerweise alle Smart-Contracts so implementiert sein, dass sie möglichst wenig Gas-Einheiten verbrauchen. Derartige Codeoptimierungsansätze sind nicht trivial. Zum Zeitpunkt des Verfassens dieser Diplomarbeit gibt es bereits solche Mechanismen, welche teilweise direkt in den gängigen Compilern integriert sind. Solche Mechanismen basieren in der Regel auf festen Mustern, welche manuell beschrieben werden müssen und dann auf Smart-Contracts angewendet werden können. In dieser Arbeit haben wir untersucht, ob klassische Verfahren zur Erkennung von Codeähnlichkeiten verwendet werden können, um Optimierungsmuster automatisch aus Quellcode-Repositories ableiten zu können. Zunächst haben wir einen Symbolic-Execution-Ansatz untersucht, welcher sich aufgrund von technischen Einschränkungen und der Abhängigkeit von veralteten Compiler-Versionen als ungeeignet erwies. Daraufhin haben wir einen Fingerprinting-Ansatz basierend auf Kontrollflussgraph-Blöcken gewählt. Mithilfe von Slither konnten wir Metriken wie Cyclomatic-Complexity, Fan-Out und Informationsfluss-Metriken extrahieren und anschließend Distanzen zwischen Codestücken berechnen, um mit den Ergebnissen potenzielle semantische Code-Klone zu erkennen. Wir haben die Evaluierung unseres Ansatzes auf 1.200 manuell markierten Smart-Contracts aus einem Datensatz mit 160.000 Einträgen durchgeführt, was zu 574 Vergleichen führte und konnten eine korrigierte Genauigkeit von 88% für die Erkennung von semantischen Code-Äquivalenzen auf Blockebene erzielen. Für 1.300 Code-Paare haben wir zusätzlich eine Gasverbrauchsmessung durchgeführt, indem wir die Blöcke in generierte Smart-Contracts verpackt und auf einer lokalen Blockchain ausgeführt haben. Dabei konnten wir tatsächliche gasreduzierende Codeänderungen identifizieren. Trotz einiger wesentlichen Einschränkungen zeigt das, dass das Mining gasoptimiertem Codes aus versionierten Source-Code-Repositories mittels Code-Metriken möglich ist.
Esther Uzoka, Bisola Akeju, Olumide Kumuyi, David Excel Ozowara
The Framework for Data Governance and Compliance Across Distributed Multicloud Infrastructures provides a comprehensive model for managing data integrity, privacy, and regulatory alignment in increasingly complex hybrid and multicloud environments. As organizations adopt distributed computing to enhance scalability, resilience, and performance, they face significant challenges in maintaining consistent governance across heterogeneous platforms operated by multiple providers. This framework establishes a unified governance architecture that integrates policy-based orchestration, automated compliance auditing, and federated identity management to ensure data sovereignty, accountability, and interoperability across diverse cloud ecosystems.At its core, the framework emphasizes data classification, lifecycle management, and access control standardization. Sensitive data are categorized by regulatory requirement and security level, while dynamic policies enforce encryption, anonymization, and retention protocols in accordance with frameworks such as GDPR, HIPAA, and ISO 27001. By leveraging federated metadata catalogs and distributed ledgers, the system enables traceable data provenance and immutable audit trails across hybrid environments. A zero-trust security paradigm further ensures that all access requests are continuously verified, regardless of origin, thereby mitigating insider threats and cross-cloud vulnerabilities.The framework also integrates AI-driven compliance monitoring to detect policy violations, automate reporting, and support adaptive governance in real time. Through interoperable APIs and compliance-as-code implementations, organizations can harmonize data policies across public, private, and edge cloud resources while maintaining jurisdictional and contractual adherence.In promoting transparency and resilience, this framework underscores the importance of cross-sector collaboration among regulators, cloud providers, and enterprises. By unifying governance, security, and compliance strategies, it advances a scalable model for secure data management in distributed infrastructuresenabling innovation, regulatory trust, and sustainable digital transformation in the multicloud era.
Prajakta Sudhir Khade, Aarushi Santosh Gode, Rajeshkumar U. Sambhe
The exponential rise of cyber threats has revealed the vulnerabilities of centralized security systems, including susceptibility to insider attacks, single points of failure, and regulatory inefficiencies. This paper investigates blockchain as a transformative backbone for cybersecurity, focusing on its potential to ensure data integrity, decentralize trust, and mitigate advanced cyber risks. Beginning with a comprehensive literature review, the study examines the fundamentals of blockchain technology—distributed ledgers, consensus mechanisms, and cryptographic primitives—that enable tamper-proof, transparent, and secure digital ecosystems. The challenges of centralized systems are contrasted with blockchain’s resilience, highlighting its role in eliminating bottlenecks and enhancing trust. Applications across identity management, IoT security, supply chains, and e-governance are analyzed alongside a proposed methodology that integrates blockchain with artificial intelligence, IoT, and quantum-resilient models. Real-world case studies demonstrate blockchain’s adoption in healthcare, government, and industrial systems, while challenges such as scalability, interoperability, and compliance are critically assessed. Collectively, this study underscores blockchain’s pivotal role in shaping next-generation cybersecurity architectures.
Since the introduction of Bitcoin in 2008, the blockchain technology as its underlying architecture, has attracted attention from various sides due to its decentralized and distributed computing characteristics. AS the core advantage of blockchain technology, the consensus mechanism determines various characteristics of blockchain, such as security, scalability, and decentralization. Currently, there are many consensus mechanisms suitable for different scenarios. This paper studies the existing consensus mechanisms from the perspectives of algorithm principles, performance, etc. Firstly, this article divides the existing consensus mechanisms into Proof of Work (PoW), Proof of Stake (PoS), and Byzantine Fault Tolerance (BFT). Secondly, for each type of consensus mechanisms, the study analyzes their algorithmic principles, understands typical solutions and latest ones, clarifies the advantages, disadvantages, and the possible attack methods of various consensus mechanisms. Finally, the paper defines the basic requirements for new consensus mechanisms. It aims to help break through the application bottlenecks of blockchain technology and promote the development of blockchain technology in various scenarios.
Denis Wapukha Walumbe, Gabriel Ndugu Kamau, Jane Wanjiru Njuki
Proof of Stake (PoS) models are energy-efficient and require limited computational power. These features are critical in telemedicine environments, where resource-constrained devices must handle sensitive data securely. The growing need for auditable and privacy-preserving data storage in telemedicine underscores the importance of PoS models optimized for lightweight devices while complying with strict regulatory requirements, such as the Health Insurance Portability and Accountability Act (HIPAA).This study was guided by two research questions: (i) Which PoS models are lightweight and suitable for telemedicine? and (ii) What features make lightweight PoS models effective for privacy and efficiency in telemedicine? To address these questions, a systematic literature review (SLR) guided by the PICOC framework was conducted to investigate lightweight PoS models that can enhance privacy in telemedicine systems. Out of 2,394 papers studies screened, 55 were included in the analysis. The findings identified Algorand, Ouroboros Praos, Tendermint, Nxt, and Casper CBC as promising candidates. Key enabling features included lightweight voting mechanisms, such as Byzantine Agreement protocols and Verifiable Random Functions, as well as cryptographic techniques like symmetric encryption and multiparty computation. Performance metrics evaluated included latency, throughput, energy efficiency, and battery consumption, with Grey Relational Analysis ranking Algorand highest due to its low latency, high throughput, and minimal energy consumption.
Harsha Kumar A, Preetham Venkatram C, N. Saran, David Daniel · 5 authors
Traditional Electronic Health Record (EHR) systems suffer from critical vulnerabilities in security, interoperability, and patient data control. This paper introduces PolyMed, a novel decentralized platform designed to address these challenges. PolyMed combines blockchain, Artificial Intelligence (AI), and edge computing into a synergistic architecture. It uses the Polygon blockchain for immutable record-keeping and a Decentralized Autonomous Organization (DAO) for transparent governance. Patient identity is secured through privacy-preserving zero-knowledge proofs (ZKPs) and anchored to non-transferable Soulbound Tokens (SBTs), granting users true sovereignty over their data. The platform also includes a Decentralized Finance (DeFi) module to improve healthcare accessibility. Empirical evaluations on the Polygon Mainnet confirm the system's viability, showing sub-4-second transaction latencies and over 90% cost savings compared to legacy systems. The integrated AI model, leveraging a LightGBM classifier on a rich set of engineered features, achieves an Area Under the Curve (AUC) of 0.8543 and an accuracy of 80.33% in emergency detection, demonstrating high reliability on a clinically relevant and imbalanced dataset. By aligning with global standards like General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA), PolyMed offers an integrated platform for patient-centric digital health management.
Rabia Arshad, Muhammad Milhan Afzal Khan, Saman Rasheed, Irtaza Ijaz · 5 authors
Blockchain technology has transformed decentralized data exchange and digital payments but the consistently high gas prices pose a significant challenge to its scalability and efficiency. This research explores the role of AI-driven gas price prediction and data compression methods on gas utilization in blockchain systems with special emphasis on Ethereum transactions. Using actual Ethereum transaction history, we compare the performance of compressed versus uncompressed payloads with three different compression algorithms: Zlib, Brotli, and Gzip. Beyond that, a linear regression model is also trained to forecast hourly gas Price fluctuations given past transaction history. The methodology includes thorough statistical analysis to provide accurate and reproducible results. Our results show that compressing text data over 141 bytes using the Zlib algorithm prior to making transactions on the Ethereum network decreases the amount of gas Used without altering system time. This validates the efficiency of combining data compression with gas price forecasting in minimizing transaction costs without affecting performance. Moreover, our study further encompasses investigation of actual gas Price trends and provides real-world insights for optimizing timing strategies for economic transaction execution. These results enhance the knowledge of Ethereum gas dynamics and provide valuable solutions for enhancing economic efficiency and resource utilization in applications based on blockchain. Future efforts will involve applying the framework to the Ethereum mainnet, using deep learning models for increased prediction accuracy, and adaptive compression dependent on network state and transaction size.
Eranga Bandara, Sachin Shetty, Ravi Mukkamala, Ross Gore · 12 authors
In recent years, blockchain has experienced widespread adoption across various industries, becoming integral to numerous enterprise applications. Concurrently, the rise of generative AI and LLMs has transformed human-computer interactions, offering advanced capabilities in understanding and generating human-like text. The introduction of the MCP has further enhanced AI integration by standardizing communication between AI systems and external data sources. Despite these advancements, there is still no standardized method for seamlessly integrating LLM applications and blockchain. To address this concern, we propose "MCC: Model Context Contracts" a novel framework that enables LLMs to interact directly with blockchain smart contracts through MCP-like protocol. This integration allows AI agents to invoke blockchain smart contracts, facilitating more dynamic and context-aware interactions between users and blockchain networks. Essentially, it empowers users to interact with blockchain systems and perform transactions using queries in natural language. Within this proposed architecture, blockchain smart contracts can function as intelligent agents capable of recognizing user input in natural language and executing the corresponding transactions. To ensure that the LLM accurately interprets natural language inputs and maps them to the appropriate MCP functions, the LLM was fine-tuned using a custom dataset comprising user inputs paired with their corresponding MCP server functions. This fine-tuning process significantly improved the platform's performance and accuracy. To validate the effectiveness of MCC, we have developed an end-to-end prototype implemented on the Rahasak blockchain with the fine-tuned Llama-4 LLM. To the best of our knowledge, this research represents the first approach to using the concept of Model Context Protocol to integrate LLMs with blockchain.
This paper investigates performance bottlenecks of consortium blockchains under high-throughput and low-latency requirements, focusing on excessive storage burden on full nodes and redundant computation in transaction validation. Based on consortium blockchain, a novel architecture named Server-Side Core Chain (SSC) is proposed. In this architecture, the core functions of blockchain ledger data storage and smart contract execution are delegated from decentralized consensus nodes to a server cluster jointly managed and trusted by consortium members. The consensus node layer is restructured into a lightweight ``Consensus and Audit Network,” dedicated to transaction ordering and state commitment verification. This paper elaborates on the design principles, operational workflow, and security model of the SSC architecture. Theoretical analysis and prototype experiments demonstrate that the architecture significantly enhances the transaction processing capacity of consortium blockchains (experimental results show a throughput improvement of more than 18 times), greatly reduces the entry barriers and operational costs for member nodes (storage overhead reduced by over 99%), and ensures the verifiability of off-chain computations and data privacy through cryptographic commitments and zero-knowledge proofs [1]. The SSC architecture offers a new solution for deploying consortium blockchains in large-scale applications, including finance, supply chain management, and e-government.