Joshua Ellul, Gordon J. Pace, Ryan Falzon, Matthew Scerri
Blockchain and smart contracts promise to increase transparency and provide guarantees for supply chains, yet existing process modeling languages either impose unnecessary complexity or lack the primitives needed to capture attestation-centric workflows that are simpler in nature. This paper introduces Attestation-Flow Diagrams (AFDs), a domain-specific graph-based notation that focuses exclusively on how attestations are issued along a supply chain. We first formalize AFD syntax and trace-based operational semantics. To demonstrate practical viability, we provide an implementation of the semantics in EVM-based smart contracts by generating three implementations that span across the decentralization spectrum: (i) logging-only contracts that immutably store attestations; (ii) hybrid contracts that record both attestations and off-chainâcomputed state transitions; and (iii) fully on-chain contracts that execute the entire verification logic in Solidity. A detailed gas-cost study on a representative viticulture use case shows that event-based logging reduces deployment and per-transaction costs by up to an order of magnitude, while full on-chain verification offers maximal trust guarantees at the highest cost. The resulting framework enables designers to tune cost, transparency and computational load according to application needs, and lays a formal foundation for further research on optimizing and verifying attestation-driven supply-chain processes.
Background/purpose. This research was conducted to address the growing need for more secure, transparent, and efficient solutions for the issuance and management of university degrees. The main purpose of the study is to evaluate which Blockchains are best suited for this application, aiming to enhance the reliability and speed of degree-related transactions. Materials/methods. The study is based on a comparative evaluation of several leading Blockchains (Ethereum, Polygon, Cardano, Stellar, and EOS), using criteria such as scalability, security, ease of implementation, transaction speed, and associated costs. Results. The results show that Stellar is the best-suited Blockchain for managing degrees, due to its very low transaction fees and high speed. EOS ranks second, offering low fees but requiring more complex resource management. Ethereum has higher transaction costs, while Polygon and Cardano offer a balanced mix of cost and performance. Conclusion. The main conclusion of the study is that Stellar is the most appropriate option for university degree management. However, the final choice should be based on the specific needs of each institution.
R. Aarthi, P. Vanitha, S Reshma, C Mounisha ¡ 5 authors
Bitcoin (BTC) and Ethereum (ETH) price and trends prediction is performed by long short-term memory (LSTM) networks, gated recurrent unit (GRU) and Random Forest machine learning algorithm, the authors explain. Feature selection techniques were effectively and widely adopted to preprocess and feed real cryptocurrency market data as input data. LSTM performs have an accuracy of 96%, GRU performs have accuracy of 97%, and Random forest 98%, meaning they are satisfactory in predicting cryptocurrency trends theme. These models were used to construct two real worlds advert based knowledge driven investment strategies which were simulated through the period under study and show the potential of this class of models. Results of which showed across different times period cases how well your prediction works [7], and all pointed out on the huge probably availability of the presence of profit making opportunity and hence the way in which your predictive way of prediction the unpredictable market crypto currency.
The rapid growth of the cryptocurrency market has raised the need for an effective model to predict opening prices and assist investors and policymakers in decision-making. Traditional econometric models often struggle with the high volatility and nonlinear patterns inherent in digital asset prices. Long short-term memory networks are effective at recognizing complex patterns, yet they lack interpretability. This study bridges this gap by integrating the error correction model with long short-term memory to improve prediction of Ethereumâs opening price. Using daily price data from January 2018 to June 2024, the model captures both long-term equilibrium relationships and short-term fluctuations, resulting in more accurate forecasts. The findings confirm a significant long-run equilibrium relationship between Bitcoin and Ethereum prices. The integrated model outperforms standalone models, by achieving a mean absolute error of 46.76, a mean squared error of 5,544.05, and an R-squared of 88%. This study contributes to both econometric and deep learning literature, highlighting Bitcoinâs influence on Ethereum, and offering a practical framework for financial forecasting. Future research could expand this work by incorporating additional macroeconomic variables, exploring alternative deep learning architectures, and testing the robustness of the model across time and market conditions.
Addressing the shortcomings of the Voluntary Carbon Markets (VCMs), a nascent blockchain industry has disrupted this area with an ever-growing number of projects leveraging open-source, decentralised base-layer platforms (e.g. Ethereum, Cosmos) and business-oriented decentralized applications (Dapps). Building on this emerging digital infrastructure over the Internet, community-driven decentralized autonomous organizations (DAOs) are building new socio-technical systems for decentralized finance (DeFi) and, more recently, regenerative finance (ReFi). Both areas are making their way into VCMs promising improved transparency, efficiency and greater accessibility. This paper examines the goals, scope, and intended outcomes of prominent blockchain-based ReFi projects in the VCM space. In particular, it explores the potential for commons-based outcomes emerging from peer-to-peer configurations in the VCM. Using a qualitative approach, the study analyses text-data from industry whitepapers focusing on the cases of Celo, Regen, Toucan, Klima and Moss. The findings show the ReFi ecosystem as a low-transaction-cost environment fostering open-source prototypes of peer-production for carbon accounting and trading. These innovations materialize through application interfaces operating on base-layer blockchains running smart-contracts and tokens. The tokenization of real-world assets (RWA) or rights (e.g. renewable energy generation, storage capacity, or forestry land) and the automation of operations (e.g. exchanges) via smart contracting, provides participants with new affordances for secure, bi-directional coordination in near-real time. The preliminary conclusion is that, while the ReFi organizations considered seem to be Ostrom-compliant with regard to some principles (e.g. clearly defined boundaries, procedures for making own rules, monitoring, or minimal recognition of rights) others are still ad-hoc practices or work in progress (e.g. graduated sanctions or dispute resolution mechanisms). This study contributes to the critical exploration of enhanced governance mechanisms, enabled by technological innovation, that can address climate action challenges and provide viable alternatives to traditional market-based approaches.
The rise of real-world asset (RWA) tokenization-converting property, bonds, or commodities into blockchain-based tokens-and its integration with decentralized finance (DeFi) protocols is altering how liquidity is created, credit is intermediated, and monetary signals propagate through the financial system. Rather than replacing central banks, these innovations are rerouting traditional channels: for example, tokenized Treasury bonds on Ethereum now serve as collateral in DeFi lending pools, effectively creating parallel money markets outside regulated banking. This paper investigates the mechanisms through which tokenization and DeFi interact with-and strain-existing monetary and regulatory frameworks. Using a mixed-methods approach (quantitative analysis of on-chain RWA flows, plus interviews with central bankers, fintechs, and supervisors in the EU, Singapore, and Brazil), we ask: (1) How does RWA-backed liquidity in DeFi respond to interest rate changes compared to traditional repo markets? (2) To what extent do DeFi protocols substitute for-or complement-bank intermediation for SMEs? (3) Where do current regulatory perimeters fail to capture systemic linkages (e.g., stablecoin runs affecting tokenized bond markets)? Early evidence suggests that while RWA tokenization can improve market access, it also fragments liquidity and weakens conventional policy transmission-especially when stablecoins (e.g., USDC) become de facto settlement layers. We propose a "functional equivalence" regulatory approach: supervise activities by economic function (e.g., maturity transformation, liquidity provision), not legal form. This work provides timely, empirically grounded input for central banks and regulators navigating the co-evolution of digital and traditional finance.
Developers and users are drawn to Ethereum due to its rapidly growing asset count. However, potential vulnerabilities and malicious behaviors during the execution of smart contracts have led to an increasing demand for security detection technology. Conventional static and dynamic analysis methods are less useful in the case of complex opcode sequences and multiple execution paths. To tackle this problem, this paper proposes an Ethereum intrusion detection method based on Bidirectional Long Short-Term Memory (Bi-LSTM) network with multi-head attention. It examines the opcode execution paths generated from the intra-and-inter-function Control Flow Graphs (CFGs) using the EPP algorithm and captures the rich feature representations and long dependencies. This combination increases the precision and efficacy of detecting malicious activity and smart contract vulnerabilities while simultaneously enhancing the modelâs robustness and interpretability and handling variable-length sequences. For the five selected vulnerabilities, the precision, recall and F1-score of this model are above 89.9%, 87.3%, and 88%, respectively.
Mohamed Fartitchou, Ismail Lamaakal, Khalid El Makkaoui, Zakaria El Allali ¡ 5 authors
Moroccoâs Vision 2030, known as Maroc Digital 2030, aims to position the kingdom as a regional leader in digital technology by boosting digital infrastructure, fostering innovation, and advancing digital skills. Complementing this initiative, the Pacte ESRI 2030 strategy, launched in 2023, seeks to transform the higher education, research, and innovation sectors by integrating state-of-the-art digital technologies. In alignment with these national strategies, this paper introduces BlockMEDC, a blockchain-based system for securing and managing Moroccan educational digital certificates. Leveraging Ethereum Layer 2 (zk-Rollups) smart contracts and the InterPlanetary File System, BlockMEDC automates the issuance, management, and verification of academic credentials across Moroccan universities. The proposed system addresses key issues such as document authenticity, manual verification, and lack of interoperability, delivering a secure, transparent, and significantly low-cost solution that aligns with Moroccoâs digital transformation goals for the education sector.
Ch. V. Raghavendran, K. Chandra Mouli, Manu Hajari, A. Anil Kumar Reddy ¡ 6 authors
Predictive modeling has emerged as a key focus for cryptocurrency market asset valuation due to its complex nature and high market volatility. The research looks into Ethereum price forecasting with the methods of autoregressive integrated moving average (ARIMA) and Facebook Prophet model and long shortâterm memory (LSTM) networks. These models operate on historical Ethereum prices and show their efficiency regarding temporal pattern recognition and prediction accuracy. The ARIMA model helps reveal trends as well as seasonal patterns and irregularities within Ethereum price fluctuations. The Facebook Prophet model serves as a forecasting tool because it automatically handles peculiarities present within cryptocurrency price data. Time series forecasting with LSTMs becomes an advanced technique used to detect intricate patterns along with sustained dependency relationships between data points. The systematic process of preparing data and constructing models and assessing results enables proper utilization of LSTMs for predicting time series data with accuracy. Ethereum price datasets are applied to train the models which undergo performance evaluation using MPE alongside MAPE and RMSE along with MAE to reveal strengths and weaknesses during Ethereum price predictions. The evaluation shows that ARIMA and Facebook Prophet together with LSTM demonstrate success in modeling Ethereum price fluctuations. This research explores the effectiveness of time series forecasting methods for cryptocurrency price prediction yielding vital knowledge about reliable tools for financial market trend modeling. Current research findings will provide knowledge to investors and risk management professionals making decisions within the volatile digital asset space.
Muhammad Rashid, Imran Rasool, Nazir Ahmad Zafar, Hamra Afzaal
Ethereum 2.0 stands out as a progressive decentralized blockchain platform, drawing attention for its security, scalability, and flexibility. Central to Ethereum 2.0 is the Beacon Chain, serving as the cornerstone managing validator rewards, penalties, attestations, and slashing mechanisms. Rewards and Penalties Mechanism (RPM) is of particular importance within the Beacon Chain as it includes validator balances based on their attestation behavior. Despite the critical role of RPM in maintaining the reliability and security of the Beacon Chain, the absence of formal verification work employing model checking is notable. Therefore, this research endeavors to fill this gap by employing formal verification technique to assess the RPMâs behavior concerning Friendly Finality Gadget (FFG) attestations. Utilizing Process Meta Language (PROMELA), a formal model of the RPM is specified, encompassing safety and liveness properties crucial for its robust functioning. The properties, including invalid attestation, integrity, fairness, availability, failure to attest, and inactivity imposition, are formalized through Linear Temporal Logic (LTL). Subsequently, the formal model alongside the specified properties is subjected to verification using the SPIN model checker. The properties are analyzed with respect to verification time, states visited, and memory usage. The outcome of this research contributes to a rigorous analysis of the RPMâs behavior. This work not only enhances an understanding of Beacon Chainâs operational dynamics but also underscores the importance of formal verification in ensuring the reliability and security of blockchain protocols.
A sharding framework has been proposed by Ethereum 2.0, and researchers have tried to enhance its applicability and scalability to real blockchain networks. Each shard can execute transactions requested by users, so the number of transactions dealt with by the shard-based blockchain grows as the number of shards increases. Dealing with cross-shard transactions, however, is a major hindrance to blockchain performance, because each such transaction requires cooperation among different shard validators in the network. Given this background, this paper proposes a novel cross-shard architecture in which each shard has its own transaction server (TS) and each TS has the role of dealing with transaction requests/results coming from different users or different shards. In this architecture, it is possible for each validator to concentrate on dealing with the transactions in its own shard, so there is no need to multicast transaction information to the other shard validators. We describe the proposed cross-shard framework in detail and demonstrate how a cross-shard transaction can be executed using the hierarchically connected smart contract functions. In the proposed architecture, the interactions among different shard validators can be reduced by having each validator select its neighbors from among members of its own shard after each epoch. This frequent change of neighbors for each validator makes it robust against eclipse attacks. In addition, this architecture speeds up the validation process of transactions/blocks in each shard; we show the effect by measuring the block transmission delays in the proposed architecture in a simulation environment.
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 Ethereumâs Proof-of-Stake (PoS) versus Solanaâs 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.
This paper presents a multi-contract blockchain framework for inter-provider agreements in 6G networks, emphasizing performance analysis under a realistic Proof-of-Stake (PoS) setting on Ethereum's Sepolia testnet. We begin by quantifying Ethereum Virtual Machine (EVM)-based gas usage for critical operations such as provider registration, service addition, and SLA penalty enforcement, observing that cold writes and deep data structures can each inflate gas consumption by up to 20\%. We then examine block-level dynamics when multiple transactions execute concurrently, revealing that moderate concurrency (e.g., 30--50 simultaneous transactions) can fill blocks to 80--90\% of their gas limit and nearly double finalization times from around 15~seconds to over 30~seconds. Finally, we synthesize these insights into a practical design guide, demonstrating that flattening nested mappings, consolidating storage writes, and selectively timing high-impact transactions can markedly reduce costs and latency spikes. Collectively, our findings underscore the importance of EVM-specific optimizations and transaction scheduling for large-scale decentralized applications in 6G telecom scenarios. The implementation is available online.
Open access
3 source records
Blockchain Technology Applications and Security
IoT and Edge/Fog Computing
Innovative Microfluidic and Catalytic Techniques Innovation
Abstract In the rapidly evolving landscape of IoT-enabled smart devices, significant challenges persist in integration to web3, security, and data reliability. This research presents the design and integration of IoT assets, particularly devices, through the Novel Decentralized Smart City of Things (DSCoT) framework. ESP32 microcontrollers serve as Ethereum clients, generating Externally Owned Accounts (EOA) for device identification and authentication. Despite resource constraints, including limited computational capabilities, essential libraries that manage tasks such as Wi-Fi module control, interaction with Ethereum-based blockchains, TCP connection management, and EEPROM operations for persistent data storage. The code is structured with functions for Wi-Fi setup, TCP API requests, and secure communication challenges. Integration involves compiling and flashing the code onto ESP32 devices, verifying EOA generation, and mapping devices, fog nodes, and users through smart contract interactions. The deployment process culminates in the generation of Non-Fungible Tokens (NFTs) for user authentication, with transaction verification on the Goerli testnet confirming successful DSCoT edge system implementation. This research underscores the importance of secure and decentralized integration of IoT-enabled smart devices to the blockchain, enhancing performance while ensuring security and transparency.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
Blockchain technology is revolutionizing digital asset exchange by eliminating the need for central authority control. However, the decentralized nature of blockchain attracts malicious actors, leading to the proliferation of financial scams, with Ponzi schemes being particularly prevalent. Consequently, there is a growing need to develop automatic detection mechanisms for such scams. So far, the problem has been tackled by considering only classifier performances and paying little attention to explaining and interpreting the results. However, interpretability and explainability are crucial when classifier decisions may have economic consequences. This paper introduces X-SPIDE (XAI Smart Ponzi Identification and Detection), an explainable machine learning pipeline for Ponzi scheme detection within Ethererum blockchain that aims to find the trade-off between performance and explainability. X-SPIDE allows comparing the results of different classifiers; computing a small set of features providing good performance; and understanding how such features contribute to classification, highlighting specific characteristics of malicious contracts. Moreover, we introduce and make publicly available a new comprehensive dataset comprising 7446 smart contracts, incorporating features derived from transaction history, creation, and deployment bytecodes to train and test our pipeline.
Cryptocurrencies are based on blockchains that are distributed ledgers for recording transactions. In 2024, these had a value of more than $3.70 Trillion in market capitalization. Ethereum is one of the most representative cryptocurrencies and dominates approximately 10% of total cryptoassets in 2025. The Ethereum blockchain system includes two types of account; contract accounts (CAs) and externally owned accounts (EOAs). We focus on the unilateral sending of cryptocurrencies to CAs and EOAs on Ethereum blockchain. We call this unilateral sending of cryptocurrencies without the recipientâs awareness, the recipientâs unapproved transactions. If the sender were malicious or criminal, the recipient of the unilateral sending might have been involved in some crime. In this paper, we provide two protection methods for CAs and EOAs on the Ethereum blockchain system and combine the two methods into a two-phase receipt mechanism for recipients to selectively reject unilateral sending of cryptocurrencies. We introduced and implemented this mechanism on open-source software for Ethereum developers, called Truffle Suite, and then executed experiments on a personal Ethereum blockchain called Ganache, provided by Truffle Suite. The experimental results demonstrated that the overhead of introducing the mechanism is reasonable at a few US dollars.
Reentrancy vulnerabilities in Ethereum smart contracts have caused significant financial losses, prompting the creation of several automated reentrancy detectors.However, these detectors frequently yield a high rate of false positives due to coarse detection rules, often misclassifying contracts protected by anti-reentrancy patterns as vulnerable.Thus, there is a critical need for the development of specialized automated tools to assist these detectors in accurately identifying anti-reentrancy patterns.While existing code analysis techniques show promise for this specific task, they still face significant challenges in recognizing anti-reentrancy patterns.These challenges are primarily due to the complex and varied features of anti-reentrancy patterns, compounded by insufficient prior knowledge about these features.This paper introduces AutoAR, an automated recognition system designed to explore and identify prevalent anti-reentrancy patterns in Ethereum contracts.AutoAR utilizes a specialized graph representation, RentPDG, combined with a data filtration approach, to effectively capture anti-reentrancy-related semantics from a large pool of contracts.Based on RentPDGs extracted from these contracts, AutoAR employs a recognition model that integrates a graph auto-encoder with a clustering technique, specifically tailored for precise anti-reentrancy pattern identification.Experimental results show AutoAR can assist existing detectors in identifying 12 prevalent anti-reentrancy patterns with 89% accuracy, and when integrated into the detection workflow, it significantly reduces false positives by over 85%.* The corresponding authors.a reentrancy vulnerability in Ethereum, leading to substantial financial losses.
Orestis Melkonian, Wouter Swierstra, James Chapman, Sub Software Technology ¡ 6 authors
Distributed ledgers nowadays manage substantial monetary funds in the form of cryptocurrencies such as Bitcoin, Ethereum, and Cardano. For such ledgers to be safe, operations that add new entries must be cryptographically sound - but it is less clear how to reason effectively about such ever-growing linear data structures. This paper demonstrates how distributed ledgers may be viewed as computer programs, that, when executed, transfer funds between various parties. As a result, familiar program logics, such as Hoare logic, are applied in a novel setting. Borrowing ideas from concurrent separation logic, this enables modular reasoning principles over arbitrary fragments of any ledger. All of our results have been mechanised in the Agda proof assistant.
The utilization of Zero-Knowledge Proofs (ZKPs) in blockchain technology enhances privacy while simultaneously preserving transparency. Given that blockchain networks frequently elicit privacy concerns owing to the inherently public nature of transaction data, ZKPs present a viable solution by enabling parties to authenticate transactions without disclosing sensitive information. This study primarily concentrates on zk-SNARKs and zk-STARKs, which represent advanced iterations of ZKPs that enhance both privacy and scalability. By analyzing established blockchain protocols, such as Zcash and Ethereum, this research illustrates that ZKPs can effectively safeguard privacy while also facilitating scalability through mechanisms such as zkrollups, which consolidate multiple transactions into a single proof, thereby alleviating congestion on the blockchain. Additionally, ZKPs enhance the verification efficiency, thereby reducing the computational burden on blockchain networks and promoting expedited transactions. However, challenges such as computational overheads and regulatory hurdles persist, hindering the widespread implementation of ZKPs. Future research endeavors should focus on overcoming these challenges by developing more efficient algorithms and collaborating with regulatory authorities to establish clear guidelines for ZKP-based systems. The potential implications of ZKPs extend beyond blockchain technology, offering substantial advantages to sectors such as finance, healthcare, and identity management, in which secure and confidential transactions are paramount. In summary, although ZKPs possess the capacity to transform privacy within decentralized networks, further advancements are required to fully harness their potential and ensure their extensive adoption.
Proof of Stake (PoS), as an important consensus algorithm in blockchain technology, has received widespread attention and research in recent years. PoS aims to solve the problems of high energy consumption and low efficiency in Proof of Work (PoW) algorithms. By allowing validators to participate in the consensus process based on their economic interests, it achieves low energy consumption and high efficiency in block verification. This article examines the latest developments in five consensus algorithms for proof of stake, with the aim of identifying errors, omissions, and shortcomings in the original proof of stake. This article finds that although proof of stake has been widely used in the market, PoS algorithm still faces challenges such as how to further improve consensus efficiency, enhance system security, and effectively respond to malicious attacks. In order to address these challenges, this article presents some of its own opinions and proposes some summarized solutions and areas for improvement. This article aims to promote the continuous development of PoS algorithm through these perspectives, in order to adapt to more complex and diverse blockchain application scenarios.
Traceability in food supply chains is crucial for ensuring safety, enabling effective quality control, and maintaining consumer trust. However, traditional paper-based or digital tracking systems often prove too slow and opaque during food safety incidents or investigations into fraud. To address these limitations, this paper presents a modular Web3 architecture that integrates Ethereum blockchain smart contracts, Internet of Things (IoT) sensors, and machine learning (ML) to achieve end-to-end traceability and sustainability in agrifood supply chains, and to support auditable, partially automated decision-making. The system design separates concerns into layers: an on-chain layer of Ethereum smart contracts for tamper-proof event logging and automated business logic, and an off-chain layer for secure storage of detailed sensor data and documents, linked by crypto-graphic hashes to ensure data provenance. Low-cost IoT sensors are deployed from farm to distributor, continuously monitoring environmental conditions (temperature, humidity, geolocation) and uploading signed, time-stamped summaries to the blockchain. In addition, ML models perform predictive quality control by estimating expected conditions, detecting anomalies, and scoring the conformity of product batches, which enables smart contracts to automatically trigger state transitions (acceptance or dispute escrow of shipments) based on real-time data. Using Ethereum smart contracts, a prototype that manages the life cycle of a specific food product was implemented, and two cases (conformant vs non-conformant shipments) were studied to demonstrate how cryptographically verifiable data and events make decisions transparent and trustworthy.
Healthcare systems face challenges in ensuring robust security, achieving scalable operations, and maintaining optimal efficiency, mainly attributable to the inherent constraints of conventional centralized architectural frameworks. These traditional systems have limitations that compromise healthcare delivery, affecting patient care quality, data integrity, and operational effectiveness. This research proposes a hybrid blockchain framework that integrates Hyperledger Fabric, Ethereum, and the Interplanetary File System (IPFS) to enable secure, scalable, and interoperable healthcare data management. Testing with simulated healthcare data across three institutional nodes demonstrated a significant 64% reduction in data retrieval time compared to traditional systems while maintaining exceptional 99.8% uptime reliability. Chainlink oracles serve as sophisticated bridges for cross-chain communication, securely transmitting data between blockchain networks while preserving complete data integrity. The framework ensures HIPAA and GDPR compliance through advanced AES-256 encryption protocols, immutable audit trails, and patient-controlled access mechanisms. The implementation results conclusively demonstrate that the hybrid model improves data security and interoperability while reducing operational costs by approximately 37% compared to current centralized solutions. This research establishes the transformative potential of hybrid blockchain frameworks in revolutionizing healthcare data environments by providing technically viable and economically efficient solutions to manage sensitive medical information.
Huma Jamshed, Urooj Waheed, Shahid Iqbal, Muhammad Faheem ¡ 6 authors
Abstract Blockchain technology enables the recording of information in an immutable manner, making it extremely difficult or nearly impossible to alter, hack, or manipulate. Its adoption is expected to enhance longâterm economic sustainability across various industries, including real estate. Traditional real estate transactions typically involve thirdâparty intermediaries to record and validate informal transactions. However, blockchain technology has the potential to revolutionize the real estate sector by transforming how properties are bought and sold. Features such as efficiency, transparency, process automation through smart contracts, robust consensus mechanisms, and enhanced security measures can reshape the real estate landscape by increasing efficiency and reducing costs. This paper explores the challenges currently faced by the real estate industry and reviews the literature on the disruptive impact of blockchain technology in this sector. A conceptual framework for a private blockchain is proposed based on the Ethereum platform, utilizing proof of authority as the consensus mechanism, specifically designed for property transactions. The proposed model integrates selfâsovereign identity for secure and decentralized identity management, incorporates digital wallets for transaction management, and leverages smart contracts to automate processes. This approach enhances transparency in digital transactions, thereby fostering greater trust between users and service provider.