This article introduces a hybrid blockchain architecture to enhance Electronic Medical Record (EMR) management and interoperability. It integrates a permissioned public blockchain on Polkadot—managing roles and permissions—with a private blockchain on Hyperledger Fabric responsible for EMR storage. Text data are stored in CouchDB and medical images in IPFS as Non-Fungible Tokens (NFTs), following a patient-centric model. Stress tests yielded average latencies of 2050 ms for EMR creation and 2000 ms for sharing, with 65 % CPU and 170 MB memory usage, indicating system stability and efficiency. The proposed architecture provides a scalable, secure, and interoperable solution suitable for healthcare environments that demand data confidentiality and controlled access.
Edge Artificial Intelligence (Edge AI) represents a transformative shift in the way data is processed, analyzed, and acted upon. By combining the capabilities of artificial intelligence with the decentralized architecture of edge computing, Edge AI enables faster decision-making, enhanced data privacy, and improved operational efficiency. This manuscript explores the essential aspects of Edge AI, including its real-time responsiveness, cost efficiency, and sustainability benefits. It also highlights the wide range of industrial applications — from healthcare and manufacturing to autonomous systems — while addressing key challenges such as limited device computing power and integration complexities. As organizations increasingly adopt Edge AI solutions, the technology is paving the way toward a hybrid future where edge and cloud systems coexist, driving innovation, scalability, and intelligence across every connected ecosystem.
Ozan Solmaz, Lioba Heimbach, Yann Vonlanthen, Roger Wattenhofer
Layer 2 rollups are rapidly absorbing DeFi activity, securing over $40 billion and accounting for nearly half of Ethereum's DEX volume by Q1 2025, yet their MEV dynamics remain understudied. We address this gap by defining and quantifying optimistic MEV, a form of speculative, on-chain MEV whose detection and execution logic reside largely on-chain in smart contracts. As a result of their speculative nature and lack of off-chain opportunity verification, optimistic MEV transactions frequently decide not to execute any trades. In this work, we focus on cyclic arbitrage, which we find is predominantly executed as optimistic MEV on Layer 2s. Using our multi-stage identification pipeline on Arbitrum, Base, and Optimism, we show that in Q1 2025, transactions from cyclic arbitrage contracts account for over 50% of on-chain gas on Base and Optimism and 7% on Arbitrum, driven mainly by "interaction" probes (on-chain computations searching for arbitrage). This speculative probing indicates that cyclic arbitrage on Layer 2s is predominantly executed as optimistic MEV and contributes to generally keeping blocks on Base and Optimism persistently full. Despite consuming over half of on-chain gas, these optimistic MEV transactions pay less than one quarter of total gas fees. Cross-network comparison reveals divergent success rates, differing patterns of code reuse, and sensitivity to varying sequencer ordering and block production times. Finally, OLS regressions link optimistic MEV trade count to ETH volatility, retail trading activity, and DEX aggregator usage. Together, these findings show that optimistic MEV has become a major source of persistent spam-like transaction activity on Layer 2s, dominating blockspace with low-value probes and reshaping the composition of on-chain activity.
La blockchain rappresenta una delle più rilevanti innovazioni tecnologiche degli ultimi decenni, capace di ridefinire il modo in cui la fiducia viene costruita e mantenuta nei sistemi digitali. Il presente contributo intende analizzare le radici tecnologiche di tale paradigma, concentrandosi su due concetti fondativi: l’immutabilità dei dati e il meccanismo di consenso. L’immutabilità deriva dalla struttura crittografica della catena di blocchi, in cui le funzioni di hash collegano ogni elemento al precedente rendendo impraticabile la modifica retroattiva delle informazioni senza invalidare l’intera sequenza. Il consenso, invece, consente ai nodi della rete distribuita di concordare sulla validità delle transazioni senza ricorrere a un’autorità centrale, sostituendo la fiducia istituzionale con una validazione matematica. I principali protocolli di consenso – Proof of Work e Proof of Stake – incarnano due differenti logiche di sicurezza, rispettivamente basate sul costo computazionale e sulla partecipazione economica, ma condividono l’obiettivo di garantire la coerenza del registro distribuito. L’analisi mostra come l’affidabilità della blockchain emerga dall’interazione tra queste componenti, configurandosi come una forma di «disintermediazione tecnica» che produce fiducia attraverso il calcolo. Tuttavia, le stesse caratteristiche che assicurano integrità e trasparenza pongono questioni aperte in termini di scalabilità, interoperabilità, governance e sicurezza di fronte al calcolo quantistico. Comprendere l’equilibrio tra immutabilità e consenso significa, dunque, cogliere la natura dinamica di una tecnologia che coniuga matematica e cooperazione distribuita per creare un nuovo modello di infrastruttura affidabile, capace di dialogare con i principi giuridici e istituzionali della società digitale.
Shahbaz Siddiqui, Sufian Hameed, Muhammad Rafi, Syed Attique Shah · 5 authors
The development of blockchain consensus mechanisms has put sustainability, scalability, and efficiency first in the list of cryptocurrency studies. As Bitcoin still uses the energy-intensive Proof-of-Work (PoW) algorithm, Ethereum is in the process of switching from PoW to Proof-of-Stake (PoS) with the Ethereum Merge in September 2022. The cause of this change was the rising energy prices, bottlenecks in scalability, and increased concerns among the global population regarding the environmental effects of PoW-based mining. Our study provides a rationale for the Ethereum transition by comparing and contrasting the volume of transactions, network performance through hash rate, and electricity consumption of Bitcoin and Ethereum in their PoW stages.With the help of both historical trend and predictive modelling in the form of the ARIMA framework, the obtained results showed that Bitcoin and Ethereum had different dynamics of growth under PoW, which was increased hash and energy expenditures but limited throughput. The ARIMA-based analysis achieved a mean absolute error (MAE) of 27.76, a root mean square error (RMSE) of 31.16, and a mean absolute percentage error (MAPE) of 26.08, which provided an overall forecast accuracy of 73.9%. These measures show that there is a medium predictive reliability, and volatility is mainly caused by sudden changes in mining intensity and market forces. The predictions further indicated that under PoW, Ethereum would have experienced a steep rise in operational energy consumption and congestion of its network transactions, which would have weakened network sustainability.Results thus confirm that the switch of Ethereum to PoS was not only an environmental need but also a strategic optimization of a transaction to scale higher, operational overhead reduction, and alignment with sustainable and ESG (Environmental, Social, and Governance) goals on a global scale. This piece leads to a better comprehension of the trade-offs between PoW and PoS, as well as the Ethereum consensus shift as the turning point in the evolution of blockchains—establishing a precedent of other cryptocurrencies that can select a balance between performance, security, and sustainability.
Abstract: Blockchain technology is a transformative distributed ledger paradigm that enables secure, transparent, and tamper-resistant data management without centralized authorities. At its core lies the consensus mechanism-the protocol through which distributed nodes agree on a single canonical transaction history. This paper presents a structured review of major blockchain consensus schemes including Proof-of-Work (PoW), Proof-of-Stake (PoS), and Practical Byzantine Fault Tolerance (PBFT), as well as emerging hybrid models such as Avalanche and Polkadot. The analysis evaluates sustainability, scalability, security, and decentralization characteristics, offering a comprehensive comparison across these mechanisms. The findings highlight inherent trade-offs related to energy consumption, throughput, finality, validator governance, and fault tolerance. The study concludes by identifying open research challenges important for designing next-generation blockchain systems capable of supporting large-scale, mission-critical applications. Keywords: Avalanche, Blockchain, Consensus Mechanisms, PBFT, PoS, PoW, Scalability, Security, Sustainability. Title: Study and Comparative Analysis of Blockchain Consensus Mechanisms Author: Dr. N. R. Ananthanarayanan, Mr. Suresh Subbu International Journal of Recent Research in Mathematics Computer Science and Information Technology ISSN 2350-1022 Vol. 12, Issue 2, October 2025 - March 2026 Page No: 1-15 Paper Publications Website: www.paperpublications.org Published Date: 25-November-2025 DOI: https://doi.org/10.5281/zenodo.17711245 Paper Download Link (Source) https://www.paperpublications.org/upload/book/Study%20and%20Comparative%20Analysis%20of%20Blockchain-25112025-2.pdf
The public ledger characteristic of blockchain grants data immutability but simultaneously introduces privacy leakage risks, making association analysis between on-chain behaviors and real-world identities possible. Existing privacy protection schemes struggle to balance the anonymity of the querier with the traceability of malicious behaviors. On one hand, legitimate inquiry behaviors are easily reverse-tracked by third parties through on-chain records (i.e., "human flesh search" targeting the querier); on the other hand, a completely anonymous environment may lead to data abuse without the possibility of accountability.To address this issue, this paper proposes an anti-"human flesh search" privacy protection system based on blockchain and zero-knowledge proofs. Addressing the aforementioned contradictions, this paper presents a blockchain data sharing scheme that balances privacy and regulation. The scheme utilizes IPFS to implement graded encrypted storage for large files. The core innovation lies in combining the Schnorr protocol and Chameleon Hash to construct a Blockchain Designated Verifier Proof (BDVP). While verifying user query permissions through blockchain smart contracts, the system utilizes the trapdoor property of the Chameleon Hash to achieve the non-transferability of proofs, preventing third parties from reverse-tracking the querier's identity by analyzing on-chain records<sup>[<xref ref-type="bibr" rid="R2">2</xref>]</sup>. Furthermore, the system introduces a threshold private key held by regulatory agencies to ensure that, in the event of data abuse, malicious users can be de-anonymized and held accountable according to the law.
Wenjie Qu, Yijun Sun, Xuanming Liu, Tao LU · 7 authors
Large Language Models (LLMs) are widely employed for their ability to generate human-like text. However, service providers may deploy smaller models to reduce costs, potentially deceiving users. Zero-Knowledge Proofs (ZKPs) offer a solution by allowing providers to prove LLM inference without compromising the privacy of model parameters. Existing solutions either do not support LLM architectures or suffer from significant inefficiency and tremendous overhead. To address this issue, this paper introduces several new techniques. We propose new methods to efficiently prove linear and nonlinear layers in LLMs, reducing computation overhead by orders of magnitude. To further enhance efficiency, we propose constraint fusion to reduce the overhead of proving non-linear layers and circuit squeeze to improve parallelism. We implement our efficient protocol, specifically tailored for popular LLM architectures like GPT-2, and deploy optimizations to enhance performance. Experiments show that our scheme can prove GPT-2 inference in less than 25 seconds. Compared with state-of-the-art systems such as Hao et al. (USENIX Security’24) and ZKML (Eurosys’24), our work achieves nearly 279× and 185× speedup, respectively.
Ram Kumar Solanki, Ganesh R. Pathak, Amit Gadekar, Abhishek Dhore · 6 authors
The spread of Distributed Ledger Technology (DLT) beyond its roots in cryptocurrency has led to a proliferation of blockchain frameworks with differing architectural philosophies, performance attributes, and applications in mind. This non-uniformity poses a significant problem for enterprises and developers who aim to find the best platform that suits them. The paper is based on a rigorous, multi-dimensional comparison of the four most crucial blockchain frameworks that encompass the breadth of the current DLT: Ethereum as an early smart contracts and decentralized application pioneer, Hyperledger Fabric for permissioned blocks designed to work in enterprise consortia, R3 Corda as a privacy-oriented ledger that is suitable to regulated industries, and Solana as a high-performance public blockchain that was developed to support web-scale applications. The paper breaks down the major architectural building blocks of each framework, including their permissioning models, data models, consensus models, and execution environments for smart contracts. Next, it compares their scalability and performance by combining the findings of notable benchmark experiments with relevant performance metrics (throughput and latency). Moreover, the paper investigates practical adoption by examining notable examples in financial services, supply chain management, and the fundamental growth of the Web3 economy. The most valuable output of this study is a synthesized framework selection matrix, which aligns platform abilities with the particular business and technical requirements as an evidence-based (observed in the field) guide that practitioners can use; at the same time, it will serve as a well-structured point of departure in future academic studies and research on the topic of distributed systems.
Ahmad Musamih, Khaled Salah, Raja Jayaraman, Samer Ellahham · 6 authors
Non-fungible tokens (NFTs) are unique digital assets stored on blockchains. NFTs are ideally suited for tokenizing genomic data, as they empower individuals with complete control over them. Next-generation sequencing (NGS) technology creates repositories of sequenced data from individuals’ raw genomic data, which raises challenges related to data ownership, management, and secure sharing. In this paper, we propose a blockchain and NFT-based solution that addresses the challenges of managing, sharing, and monetizing genomic data while preserving privacy using Threshold Cryptography and Fully Homomorphic Encryption (FHE). We integrate the proposed solution with the Interplanetary File System (IPFS), a decentralized storage system, to handle the substantial amount of genomic data off-chain. We develop three smart contracts to facilitate genomic data management, sharing, and monetization. We introduce composable NFTs to ensure that sequenced genomic data (SGD) NFTs are always linked to the parent raw genomic data (RGD) NFTs to maintain traceability. We present various diagrams and algorithms to illustrate the functionality of our solution. Our testing and validation results demonstrate that smart contracts function as intended. The cost evaluation shows that implementing the solution on a private blockchain is more feasible and user-friendly. Our solution provides a comprehensive framework for genomic data management, sharing, and monetization, with privacy-preserving mechanisms and traceability. We provide guidelines for the generalizability of our solution beyond genomics and outline the challenges and limitations of the proposed solution. We make the source code of the smart contracts publicly available on GitHub.
Blockchain has received a lot of attention for multiple use cases and applications since the first works emerged about 15 years ago, with its use targeting cryptocurrencies. During this period, a wide variety of platforms (e.g. Ethereum, Hyperledger, and others web3 Blockchain platforms), tools, programming languages and other resources such as smart contracts were proposed. With the aim of better understanding the use of smart contracts in Blockchain-based systems, this article presents a systematic review of the literature on Blockchain application architectures that make use of smart contracts, applied in different areas. It is expected to bring together approaches for the design and implementation of smart contracts on the Blockchain.
Blockchain technology, at its core, promises decentralization, transparency, and self-sovereignty. Interacting with a blockchain network involves technical procedures that require specific knowledge, hardware, and ongoing maintenance. This is where providers come in. Providers form the essential infrastructure layer that connects decentralized networks with the users, applications, and developers that rely on them. They are the unsung heroes of the Web3 movement, quietly handling the complex backend operations that enable seamless blockchain interactions.
Michael Wijaya, Franscelino Melvyn, Reina Setiawan, Reinert Yosua Rumagit
Blockchain technology has emerged as a breakthrough in decentralized systems. The development of applications, systems, programs, and financial solutions can now be managed in a decentralized manner, revolutionizing the previously centralized paradigm. In this new system, performance, scalability, transaction costs, and network security present both challenges and compelling topics for research. This paper conducts a comparative analysis through experiments and evaluations of Ethereum and Solana, the two largest Layer 1 blockchain networks today. The comparison focuses on performance, consensus mechanisms, security, and ecosystem development. Performance is assessed through Transactions Per Second (TPS) and latency, while gas fees are compared under different network conditions. The study also analyzes Ethereum’s Proof-of-Stake (PoS) versus Solana’s Proof-of-History (PoH) + PoS consensus mechanisms. Network security is examined by reviewing historical vulnerabilities and corresponding responses. The DApp (Decentralized Application) ecosystem is evaluated based on Adoption Efficiency Index (AEI) and sector specialization (DeFi, NFT, gaming). Through this analysis, the paper aims to provide a deeper understanding of the differences, strengths, and weaknesses of both networks, helping developers choose the most suitable blockchain platform.
Saad Mutlaq Alluhaydan, Mohammed Obaid Alshammari, Yousef Jazaa Obaid Alshmilan, Ahmed Hamoud Alshammari · 12 authors
Background: The rise of AI health assistants and digital tools raises concerns about data security and consent management. Traditional systems are prone to failures and provide limited transparency in data sharing. Blockchain technology offers a decentralized, immutable, and secure solution to these issues. Aim: This narrative review critically examines the real-world implementations and security trade-offs of blockchain technology when applied specifically to health assistant audit trails and consent management, moving beyond theoretical propositions. Methods: A systematic search of peer-reviewed literature (2010-2024) was conducted across Scopus, IEEE Xplore, PubMed, and ACM Digital Library. Implementation case studies, prototypes, and theoretical frameworks were analyzed to assess technical architectures, performance metrics, and security evaluations. Results: Findings indicate an emerging landscape where blockchain proves useful for creating secure audit logs in AI decision-making and dynamic consent models using smart contracts. However, challenges persist, including performance and scalability issues, key management complexities, data linkage risks, and conflicts between immutability and regulatory requirements such as the GDPR's "right to be forgotten." Conclusion: Blockchain serves as a foundational layer to improve security and transparency in health assistant ecosystems. Its future potential relies on hybrid architectures, advanced cryptographic methods such as zero-knowledge proofs, and an awareness of the new security and operational challenges that arise. It is not merely a database but a comprehensive solution for integrity and control.
This article presents a blockchain reconciliation framework that improves transparency, automation, and trust within the SAP supply chain and finance processes. The implemented system with smart contracts on SAP modules FI, MM, and SD permits real-time verification of supply chain activities and financial transactions, thus minimizing manual matching and third-party verification processes. The framework enables automated three-way matching and updates across modules by storing transaction states in a distributed ledger that captures changes. Based on experiments conducted using SAP simulation data, the accuracy of reconciliations increased by 92%, processing time was cut by 41%, and manual processing steps were reduced by 67%. The results demonstrate the capability of blockchain technology to solve pervasive challenges related data integrity and reconciliation within enterprise ERP systems.
This chapter explores the critical intersection of consensus mechanisms and performance metrics in blockchain systems for the Internet of Things (IoT). As IoT networks expand, the demand for scalable, efficient, and secure blockchain solutions intensifies. This chapter evaluates the trade-offs between energy efficiency, latency, scalability, and security within IoT-integrated blockchain frameworks. Key consensus protocols such as PoW, Proof of Stake (PoS), and Delegated DPoS) are analyzed for their applicability in IoT environments, highlighting their impact on transaction speed, resource utilization, and network resilience. Hybrid consensus models are examined for their potential to balance performance with robust security. The chapter also provides real-world case studies that benchmark latency and energy efficiency, offering insights into the practical challenges and opportunities for IoT-based blockchain applications. Key findings contribute to the ongoing development of optimized blockchain solutions for IoT networks.
This study focuses on data encryption and privacy protection technologies in cloud computing environments. By systematically implementing and evaluating various encryption algorithms (such as AES, RSA, and homomorphic encryption) and privacy protection techniques (including data masking, differential privacy, secure multi-party computation, and zero-knowledge proofs), the feasibility and effectiveness of these technologies in cloud environments are explored. A simulated cloud environment was constructed for experiments, and the results indicate that AES performs excellently in large-scale data processing, while homomorphic encryption demonstrates unique advantages in specific scenarios. Privacy protection techniques can achieve a balance between protecting user privacy and maintaining data availability. System performance and security tests confirm that the proposed solutions effectively support the data security and privacy protection needs in large-scale cloud environments. This research provides a comprehensive technical implementation and evaluation reference for data security and privacy protection in cloud computing environments, while also highlighting some challenges and offering valuable insights for future research directions.
In recent years, the utilization of Ethereum has significantly increased, positioning it as a favored platform among criminal entities. A recently proposed blacklisting method offers a compelling approach; however, its implementation faces numerous challenges. For instance, criminals may circumvent the blacklisting mechanism by creating new addresses and there are several ambiguities in their explanation. This paper explores the increasing use of Ethereum for criminal activities, focusing on the challenges of enforcing blacklisting to curb illegal transactions. We analyse blacklisting within cryptocurrency networks, particularly Ethereum, and develop features to detect illegal patterns. The study identifies unique issues in transaction networks that require specialised solutions beyond general cryptocurrency techniques. We propose a detection model based on these features and validate its effectiveness using real Ethereum datasets. The paper also reviews regulatory guidelines, highlighting ambiguities in their interpretation. Experiments on real-world data underscore the need to integrate technical methods and consider Shapley-value-based frameworks in designing effective solutions. The novelty of the method lies in its development of a feature-based detection model, leveraging Shapley-value frameworks to enhance explanation, address Ethereum’s unique challenges, and empirically validate its effectiveness using real Ethereum data, offering a more robust solution than traditional blacklisting approaches.