The exponential growth of digital data has intensified reliance on cloud storage, yet conventional centralized architectures remain persistently vulnerable to unauthorized access, data tampering, and privacy violations. This paper presents a novel blockchain-driven approach for decentralized cloud storage that addresses these concerns through a multi-layered security mechanism. The proposed system fragments a user’s file into multiple independent blocks, encrypts each block using the AES-256 algorithm with PBKDF2-derived keys, and distributes them across distinct nodes of the InterPlanetary File System (IPFS). The corresponding IPFS hash addresses are then recorded on an Ethereum-based blockchain through a Solidity smart contract, ensuring immutability and tamper-resistance of the entire storage index. During file retrieval, the system queries the blockchain to collect all block hash addresses, fetches encrypted blocks from IPFS, reassembles them in the correct sequence, and delivers the decrypted output to the user. The implementation is built using Python, Django, Web3.py, and the Truffle/Ganache development environment, and has been functionally verified across all core user-facing modules.
Intherapidlyevolvingdigitallandscape,freelancing platforms face significant challenges due to a lack of transparency,trust,andcentralizedcontrol.Thispaperpresents the design and implementation of a blockchain-powered web- based project management system integrated with a visual data dashboard. The proposed system leverages Ethereum smart contractstoensuresecure,tamper-proofuserregistration,project posting, bidding,assignment, work submission, payment release, and rating. The backend is developed using Django, while blockchain integration is achieved via Web3.py, enabling secure and transparent interactions. The platform provides real-time analyticsonusers,jobstatus,fundmovement,andratingsthrough a dashboard. The solution enhances trust, transparency,and de- centralization,provingeffectiveforfreelanceprojectecosystems
Reentrancy remains one of the most critical classes of vulnerabilities in Ethereum smart contracts, yet widely used detection tools and datasets continue to reflect outdated patterns and obsolete Solidity versions. This paper adopts a dependability-oriented perspective on reentrancy detection in Solidity 0.8+, assessing how reliably state-of-the-art static analyzers and AI-based techniques operate on modern code by putting them to the test on two fronts. We construct two manually verified benchmarks: an Aggregated Benchmark of 432 real-world contracts, consolidated and relabeled from prior datasets, and a Reentrancy Scenarios Dataset (RSD) of \chadded{143} handcrafted minimal working examples designed to isolate and stress-test individual reentrancy patterns. We then evaluate 12 formal-methods-based tools, 10 machine-learning models, and 9 large language models. On the Aggregated Benchmark, traditional tools and ML models achieve up to 0.87 F1, while the best LLMs reach 0.96 in a zero-shot setting. On the RSD, most tools fail on multiple scenarios, the top performer achieving an F1 of 0.76, whereas the strongest model attains 0.82. Overall, our results indicate that leading LLMs outperform the majority of existing detectors, highlighting concerning gaps in the robustness and maintainability of current reentrancy-analysis tools.
Integrating blockchain into the Industrial Internet of Things (IIoT) has emerged as a promising solution for preserving data privacy and ensuring IoT security. Among various blockchain platforms, Ethereum stands out due to its support for smart contracts and its interoperability with lightweight communication protocols. Despite these advantages, particularly within Ethereum-based networks, IIoT systems remain vulnerable to large-scale threats such as Sybil attacks. These attacks pose a critical security risk because an adversary generates numerous fake entities to infiltrate and compromise the network, ultimately undermining its integrity and availability. Existing approaches utilize Ethereum smart contracts and lightweight protocols such as MQTT to secure IIoT communications, but often overlook sophisticated threats such as Sybil attacks, which introduce fraudulent nodes into the network. Conventional detection methods typically depend on centralized monitoring, undermining scalability and privacy, and there remains a lack of publicly available datasets representing adversarial behaviors in IIoT environments. In this paper, an Ethereum-based IIoT network is first developed, and a publicly available dataset is released through the GitHub repository. An advanced method is then proposed to detect and prevent Sybil attacks in a PoA-based IIoT network using decentralized federated learning. During the detection phase, a convolutional neural network (CNN) is employed within the decentralized federated learning framework, achieving an average detection accuracy and recall of 91.13% and 91.37% among clients, respectively. In the prevention phase, a secure smart contract is designed to manage a dynamic reputation system, effectively preventing Sybil nodes from remaining active on the network.
In this study, it is aimed to compare quantitative forecasting methods (traditional and learning based) in cryptocurrency market. For his purpose the daily prices between 16 September 2017 – 15 September 2022 of Bitcoin, Ethereum, Binance Coin and Monero were analyzed with five different methods: ARIMA, exponential smoothing, artificial neural networks, RNN and LSTM.In the results it is indicated that exponential smoothing method is the most successful method at forecasting daily prices. The method has high performance in forecasting BTC, ETH and BNB daily prices. But at forecasting daily XMR prices, artificial neural networks method was the most successful one.The other point which was detected in this study is deep learning based methods made some unsuccessful forecasts. This is thought to be due to the fact that deep learning methods require more data. In future studies, using other quantitative methods (e.g. GRU, XGBoost, transformer models) on other cryptocurrencies will contribute to the literature.
Solana has experienced rapid growth due to its high performance and low transaction costs, but the extremely low barrier to token issuance has also enabled widespread Rug Pulls. Unlike Ethereum-based Rug Pulls, which often rely on malicious smart-contract logic, Solana's unified SPL Token program shifts fraudulent execution toward on-chain behavioral manipulation. However, existing research has not systematically examined these Solana-specific Rug Pull patterns, and no public Solana Rug Pull dataset is available for empirical research. To bridge this gap, we present a large-scale measurement study of Rug Pulls on Solana. We manually verify 68 community-reported incidents and curate a benchmark of 117 confirmed Rug Pull tokens, from which we distill three representative on-chain behavioral patterns: Freeze Authority Abuse, Liquidity Withdrawal, and Pump-and-Dump. Guided by these patterns, we design a behavior-guided candidate identification and human-validation pipeline. We apply this pipeline to 100,063 tokens newly issued on Orca, Raydium, and Meteora during the first half of 2025, identifying 76,469 Rug Pull tokens. A random manual audit of 382 samples estimates a labeling false-positive rate of 0.26\%, supporting the reliability of the dataset. We release the resulting dataset and use it to characterize the Solana Rug Pull ecosystem. Our analysis shows that Rug Pulls on Solana exhibit extremely short lifecycles, strong price-driven dynamics, severe economic losses, and highly organized group behaviors. These findings provide new insights into the Solana Rug Pull landscape and support the development of effective on-chain defense mechanisms.
Document authentication remains a pressing challenge in various domains, including financial services, academic credentialing, healthcare, and supply chain management. Existing centralized verification systems are vulnerable to manipulation, inefficiency, and limited transparency. Blockchain technology, with its immutability and tamper-resistant capabilities, offers a strong decentralized alternative; however, many current implementations lack structured, issuer-bound relationships for documents. This paper proposes a blockchain-based model that leverages a hierarchical token structure to authenticate and trace the provenance of high-value digital documents, with a focus on financial records. The model introduces the concept of an issuer-bound parent token and document-linked child tokens, enforcing a structured trust relationship between a legitimate institution and the documents it issues. By combining on-chain cryptographic hashing with off-chain file references, the approach is designed to balance verifiability with scalability. We implement a proof-of-concept using Ethereum-compatible smart contracts on a permissioned blockchain and evaluate it in a consortium-style financial setting. Our functional analyses demonstrate the model’s ability to ensure document integrity, provenance, and resistance to document fraud. This work offers a practical and extensible foundation for secure digital document authentication and verification in financial and other trust-sensitive settings.
Open access
Blockchain Technology Applications and Security
Cryptography and Data Security
Advanced Steganography and Watermarking Techniques
Blockchain technology is often discussed as if it emerged from nowhere, yet its architectural DNA traces directly to the decentralized computing principles James~N. Gray articulated in 1986. This paper maps the conceptual lineage from Gray's requestor/server model to modern blockchain architectures, showing how his emphasis on modularity, autonomy, data integrity, and standardized communication anticipated the design of systems like Bitcoin and Ethereum, and, more recently, the Web3 movement and Layer-2 scaling architectures. We examine consensus mechanisms, cryptographic foundations, rollup-based Layer-2 protocols, and cross-chain interoperability through this historical lens, identify persistent challenges in scalability and modularity, and outline future directions toward Web4: an intelligent, decentralized internet integrating blockchain, artificial intelligence, and the Internet of Things.
Peer-discovery protocols within P2P networks are often vulnerable: because creating network identities is essentially free, adversaries can eclipse honest nodes or partition the overlay. This threat is especially acute for blockchains, whose security depends on resilient peer connectivity. We present AetherWeave, a stake-backed peer-discovery protocol that ties network participation to deposited stake, raising the cost of large-scale attacks. We prove that, with high probability, either the honest overlay remains connected or a $(1{-}δ)$-fraction of nodes in every smaller component raise an attack-detection flag -- even against a very powerful adversary. To our knowledge, AetherWeave is the first peer-discovery protocol to simultaneously provide Sybil resistance and privacy: nodes prove they hold valid stake without revealing which deposit they own, and gossiping does not expose peer-table contents. A cryptographic commitment scheme rate-limits discovery requests per round; exceeding the limit yields a publicly verifiable misbehavior proof that triggers on-chain slashing. Beyond deposit and slashing, the protocol requires no on-chain interaction, with per-node communication scaling as $O(s\sqrt{n})$. We validate our design through a mean-field analysis with closed-form convergence bounds, extensive adversarial simulations, and an end-to-end prototype built by forking Prysm, a leading Ethereum consensus client.
On-chain lending has expanded across multiple distributed ledgers as DeFi becomes increasingly multi-chain. This environment introduces novel technical and financial mechanisms, particularly cross-blockchain communication and asset transfer protocols, yet cross-chain elements remain understudied in lending protocol risk management. To address this gap, we applied panel regression fixed effects and OLS models to empirically analyze cross-blockchain interoperability solutions, using TVL and total revenue as performance proxies from October 2022 to January 2025. Our data set covers 15 decentralized lending protocols and 53 cross-chain bridges across 9 EVM-compatible blockchains, categorized as Ethereum, alternative layer-1s, and Ethereum layer-2 networks. Results reveal that cross-chain activity impacts on protocol performance. Bridge volume emerges as a critical driver, exerts a significant effect on TVL and revenue across different categories, though the direction of this effect varies heterogeneously. Increased bridge integrations are associated with decreased TVL and protocol revenue across categories, indicating liquidity escapes from those lending ecosystems. Liquidations produce heterogeneous effects across categories. New network launches do not have as significant relationships with TVL and revenue while bridge hacks show a significant and positive relationship. High R-squared values confirm meaningful explanatory power. We further show Ethereum attracts large depositors, while layer-2s skew toward retail participation. We conclude that effective DeFi risk models should incorporate cross-chain metrics and adopt a layer-aware approach to accurately reflect the evolving multi-chain landscape.
Youssef Said, Al Mahdi Khaddar, Lahcen Hassine, Ahmed Eddaoui · 5 authors
Gas Gas consumption is a critical factor influencing the efficiency, scalability, and operational cost of Ethereum smart contracts.As contract complexity grows, identifying structurally gas-inefficient patterns becomes essential for improving development workflows and preventing costly deployment decisions.This study presents a graph-based deep learning framework for detecting gas-inefficiency risk patterns at the function level, leveraging multi-relational Graph Attention Networks (GAT) applied to function-level contract graphs.By modeling call dependencies, control-flow interactions, and storage-based data dependencies, the model learns structural indicators associated with excessive gas consumption while explicitly excluding direct gas metrics from the feature space to prevent data leakage.Experimental results under a strict contract-level data split protocol demonstrate strong classification performance and stable generalization across held-out contracts under the main split protocol, and consistent behavior under an additional time-forward temporal robustness check.Ablation analysis confirms the contribution of dependency-aware edges and semantic features to predictive accuracy, highlighting the importance of modeling cross-function interactions rather than isolated code metrics.Beyond predictive performance, the proposed approach provides interpretable attention weights that identify structurally influential functions, supporting predeployment analysis and developer-guided manual refactoring decisions.By framing gas inefficiency as a global structural property emerging from function interactions, this work contributes aa scalable and explainable methodology for structural gas-inefficiency detection in smart contracts.The proposed model performs structural detection only and does not automatically modify or optimize smart contract code.
In this research, the energy consumption models of Bitcoin, Ethereum, and Dogecoin are analyzed using Explainable Artificial Intelligence (XAI) models aided by the three stages of analysis involving Digiconomist data from 2022 to 2025: (1) exploratory data analysis for the nature of energy consumption, (2) model identification of influential variables using Random Forest models enhanced with SHAP values, and (3) an LSTM transfer learning method for predicting the energy consumption of Ethereum and Dogecoin using a model developed with Bitcoin data. The initial results show that while both assets vary largely when it comes to their normal usage level, Ethereum sees a sharp drop after the changeover from Proof-of-Work to Proof-of-Stake as a mechanism. The XAI analysis indicates that energy use is largely a consequence of past use, seasonality, and annual patterns. In addition to this, the models show a high level of accuracy for Dogecoin (R²: 88.4%, MAPE: 13.45%) and Ethereum (R²: 86.2%, MAPE: 11.47%) when it comes to predicting energy usage using the concepts of transfer learning.
The centralization of digital content creation and credentialing platforms has resulted in opaque monetization structures, monopolistic data silos, and a persistent absence of verifiable user sovereignty over intellectual contributions. This paper introduces Metaplay, a decentralized content marketplace architecture engineered to disintermediate the content creation and talent development lifecycle. Leveraging a modular blockchain framework, Metaplay utilizes Zero-Knowledge Rollups (zkEVM) for high-throughput, low-latency execution, and EIP-4844 blob-carrying transactions to minimize data availability costs. We introduce a privacy-preserving credentialing mechanism utilizing Soulbound Tokens (SBTs) and zk-SNARKs, enabling non-transferable, cryptographically verifiable proof of skill acquisition without compromising user privacy. Platform moderation employs a Decentralized Autonomous Organization with Identity-Gated Quadratic Voting to mitigate plutocratic governance capture. A dual-token incentive model (PLAY utility token and CRED reputation token) aligns creator economic incentives with verifiable content quality. Comparative benchmarks demonstrate transaction cost reductions exceeding 95% relative to Ethereum Layer-1 baselines.
K. Sundara Krishnan, R. Chithra Devi, Christo Ananth, D. Easwaramoorthy · 8 authors
E-commerce platforms incorporate reviews and reputation systems, allowing retailers and customers to manage and track their financial transactions. Consequently, it is crucial to design a reliable reputation system for the e-commerce environment, as it faces well-documented threats, including sybil attacks, feedback collusion, impersonation, review tampering, and whitewashing attacks. Current centralized systems are vulnerable to impersonation attacks, feedback manipulation, and lack automated verification against collusion-based reputation distortion. These unwanted ratings and reviews are highly correlated with abnormal cyber-attacks that damage both seller reputations and buyer experiences. To address these challenges, we propose a Blockchain-based Two-Level E-Commerce Trustable Reputation Framework (BTL-ETRF) utilizing deep learning-embedded transformers and redactable blockchain systems. Initially, we implement Multi-Factor Authentication for e-commerce users, utilizing three factors: PIN, OTP, and biometric fingerprint, to mitigate impersonation attacks. Only authenticated users are allowed to proceed to the reputation verification stage, where the proposed work considers five major metrics to classify user reputation using the Residual Dilated Convolution Transformer. To automate the reputation verification process, we design and employ two smart contracts, the Authentication Smart Contract and the Reputation Smart Contract which trigger automated actions based on the BTL-ETRF results. All transactions include reputation classification, and triggered actions are stored in the redactable blockchain, which can modify the stored transactions if needed. Finally, we demonstrate the performance of the proposed BTL-ETRF using Python and Ethereum Solidity, and conduct a formal analysis that shows the proposed model outperforms the compared works.
Cloud storage systems have become an essential component of modern data management, enabling users to store and access data remotely. However, traditional cloud storage architectures rely on centralized servers, which introduce critical challenges such as single-point failure, redundant data storage, high storage costs, and security vulnerabilities. In earlier systems, data was stored in centralized data centers where duplicate files were often saved multiple times, leading to inefficient utilization of storage resources. Although basic deduplication techniques were used, they frequently compromised data confidentiality and lacked transparency in metadata management. Moreover, failure of the central server could result in permanent data loss. To overcome these limitations, this research system integrates blockchain technology, InterPlanetary File System (IPFS), Convergent Encryption (CE), and heuristic-based chunking techniques to create a secure and decentralized storage framework, hereafter named Blockchain-enabled Heuristic Optimized Deduplication Model (BHODM). In this system, files are divided into optimized chunks using a heuristic method based on file size. Each chunk undergoes CE, where the encryption key is derived from the hash of the data itself, enabling secure deduplication without exposing plaintext information. Duplicate chunks are identified using hash comparison, ensuring that only unique data is stored. The encrypted chunks are stored in IPFS, a decentralized peer-to-peer storage network that eliminates reliance on a single server. Metadata such as file names, block numbers, and hash values are securely stored in an Ethereum blockchain smart contract, ensuring immutability and transparency. The system is implemented using Django for the web application, Web3 for blockchain interaction, IPFS Application Program Interface (API) for distributed storage, and Advanced Encryption Standard in Counter Mode (AES-CTR) encryption for security. By combining decentralized storage, blockchainbased metadata management, and secure deduplication, the proposed model effectively reduces storage overhead, enhances data integrity, and mitigates single-point failures. The system is further evaluated using storage utilization and computation time analysis, demonstrating improved efficiency compared to traditional approaches
We study Arbitrum's Timeboost mechanism following the adoption of Kairos by its main users -- Wintermute and Selini Capital -- to understand how the emergence of a just-in-time secondary market affects the dynamics of an ahead-of-time primary auction. We find that competition in the primary auction declines significantly and that Arbitrum captures a smaller share of the value generated around Timeboost. After the transition, paid bids in the primary auction correspond to only 14.8\% of the highest bid (compared with nearly 62.7\% in the \textit{Pre-Kairos} era) and to a smaller share of searcher profit-and-loss (PnL), even though total PnL remains of similar magnitude across regimes. While the exact distribution of the remaining surplus between searchers and Kairos remains unclear, the evidence suggests that a substantial share is no longer captured through the primary auction. More broadly, our findings suggest that ahead-of-time allocation mechanisms may be particularly vulnerable to secondary-market intermediation when competition among dominant participants is weak. We conclude by outlining possible ways for Arbitrum to improve revenue capture and discussing how these lessons may apply to Ethereum L1.
This study examines short-term return forecasting for Bitcoin, Ethereum, and Litecoin over 2020–2024, comparing autoregressive benchmarks with Kitchen Sink and VARX-type models using point and density accuracy measures supported by Diebold–Mariano and Model Confidence Set inference. The results demonstrate that the AR(1) benchmark and parsimonious specifications incorporating cryptocurrency-specific variables consistently outperform the more elaborate linear frameworks considered, while the inclusion of macro-financial predictors offers limited benefits. Findings highlight the robustness of autoregressive dynamics for short-term cryptocurrency forecasting and underscore the importance of parsimony over model complexity. These results are consistent with a market environment characterised by high structural uncertainty, sentiment-driven trading and rapidly shifting regimes, in which additional macro-financial information contributes little to forecastability beyond short-run return momentum and crypto-specific volatility.
This paper develops a deep reinforcement learning (DRL) framework for cryptocurrency portfolio management in which transaction costs are derived from the Riemannian geometry of the underlying volatility model rather than assumed constant. A Proximal Policy Optimisation (PPO) agent is trained on a reward function derived from non-equilibrium thermodynamics: the free-energy Bellman equation, in which (i) transaction costs are the geodesic slippage S∗ on the Fisher information manifold of a maximum-entropy Markov-switching GARCH model, and (ii) regime-transition costs are the Wasserstein-2 distance Wt between the calm and turbulent return distributions. The agent is embedded in the WOW-E-W quadrilogy, a four-paper research programme that integrates statistical mechanics, fluid dynamics, Riemannian information geometry, and thermodynamic control into a unified cryptocurrency risk architecture. The PPO agent observes an 11-dimensional state vector ot that combines turbulent-regime probabilities \( \hat{\xi}_t(2) \) and parameter estimates \( \hat{\theta}_t \) from a maximum-entropy Markov-switching GARCH model, a viscosity-filtered velocity signal ht and gate states zt, rt from a GRU viscosity filter, and the Fisher curvature Gt, Ricci scalar κt, Betti numbers β0,t, β1,t,Wasserstein dissipation Wt, and topological alarm dI(t) from the Riemannian execution geometry layer. The framework establishes a thermodynamic Carnot bound on portfolio efficiency: η ≤ 1 − Hturb/Hcalm, where Hturb and Hcalm are the maximum-entropy values of the turbulent and calm regime distributions. Five hypotheses are tested across Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash over January 2017 to March 2026: the geometric-cost PPO agent achieves higher Sharpe ratio than Buy-and-Hold, Greedy signal-following, and flat-fee PPO baselines (bootstrap p < 0.05 for four of five assets); portfolio turnover is reduced by 56 to 83 percent relative to signal-following; the thermodynamic friction point at which the agent prefers no-trade is asset-specific and ranges from 0.6 percent (Bitcoin) to 1.8 percent (Ethereum), ordered by turbulent half-life (Spearman ρ = 0.94, p = 0.017); a joint topological and geometric circuit breaker reduces Maximum Drawdown by 28 to 38 percent; and ablation confirms that every component of ot contributes a statistically significant performance gain (Diebold-Mariano p < 0.05 for at least four of five assets per component). The framework requires liquid cryptocurrency markets with validated parametric volatility models; transferability to other asset classes requires upstream recalibration and is an explicitly bounded limitation.
The rapid evolution of cloud computing has revolutionized digital data storage and sharing, enabling users to access information anytime and anywhere. Despite these advantages, cloud-based systems face major challenges related to data security, privacy protection, and trust management, particularly when handling sensitive user information. Conventional cloud storage solutions operate on centralized architectures, where a single cloud service provider manages and controls the stored data. This centralized model introduces significant risks, including single points of failure, unauthorized data access, data manipulation, and limited visibility into data-sharing activities. In many traditional systems, data protection mechanisms rely on basic encryption methods without strong auditing or verification features, leaving them vulnerable to insider attacks and external cyber threats. Furthermore, the absence of immutable transaction records and robust key management practices reduces accountability and weakens user confidence in cloud environments. To address these shortcomings, the proposed system presents a secure cloud data sharing framework that combines Elliptic Curve Cryptography (ECC) with blockchain technology. In this approach, user files are encrypted using ECC before being uploaded to the cloud, ensuring strong data confidentiality and protection against unauthorized access. Simultaneously, blockchain technology is employed to record file metadata and transaction details in a decentralized and tamper-resistant ledger, enabling transparent and verifiable audit trails. The decentralized architecture eliminates reliance on a single authority, enhances trust, and prevents unauthorized modification of stored records. Additionally, secure authentication and controlled access mechanisms further reinforce system security. By integrating advanced cryptographic encryption with decentralized verification, the proposed solution enhances data integrity, improves transparency, and establishes a reliable and accountable framework for secure cloud data sharing.
International Journal of Technology, Leadership and Sciences
Food and agriculture supply chain transparency is growing in importance for both consumers and states. The fast expansion of blockchain technology's use is being propelled by its inherent trustworthiness and immutability. This technology can offer safe traceability for the management of the agri-food chain, prevent food fraud, and provide information like a food product's provenance. It is far more difficult than in other businesses to create smart contracts that are suitable for certain use cases. Although many agri-food chain management systems based on smart contracts and blockchain have been developed, they are all quite ad hoc and not easily adaptable to different products or production processes. A new method for quickly adapting and developing universal smart contracts for the agri-food business based on Ethereum is presented in this research. We can automate the process and reuse modules and code using this strategy, which shortens development times without sacrificing dependability and safety. In order to set up a semi-automatic system, we want to start with the production process and build the smart contracts that control the system and the user interfaces that automatically connect with them. To further illustrate how our method works, we provide a case research on honey production. The primary goal of future studies will be to find ways to apply the method to different types of supply chains. Even though Ethereum is now in use, our technology can be simply adapted to other blockchain systems.
Andrew Kim, Jarrett Bobrin, David Weinstein, Isabelle Kim
Non-fungible Tokens (NFTs) in Diagnostic ImagingAndrew Kim1, Jarrett Bobrin1, David Weinstein1, Isabelle G. Kim.Temple University Hospital1, Department of Radiology, Philadelphia, PA.Non-fungible tokens (NFTs) have garnered significant media attention in recent years, largely due to the astronomical prices fetched by some digital artworks. They have emerged as a popular medium for buying and selling digital art. Most people associate NFTs with high-profile examples such as the Bored Ape Yacht Club or Beeple’s digital artwork, the latter of which famously sold for over $69 million. Even the world’s first SMS text message was converted into an NFT and sold for over 100,000 euros. In 2021, the NFT market was valued at approximately $41 billion USD, and the term “NFT” ranked among the most popular search terms on Google during both 2021 and early 2022.However, NFTs are more than just digital collectibles; they hold significant untapped potential, particularly in the medical field, including diagnostic imaging. While blockchain technology has been widely explored in healthcare, the specific role of NFTs in diagnostic imaging remains largely unexplored. Although there has been extensive discussion on the use of blockchain in medicine, the application of NFTs in this space is still in its infancy.So, what exactly is an NFT? A non-fungible token is a unique digital asset representing ownership of a specific item or piece of data—whether that be digital artwork, music, or in more recent applications, items in video games or medical records. NFTs are built using the same blockchain technology as cryptocurrencies like Ethereum. However, unlike cryptocurrencies or fiat currencies, NFTs are non-fungible, meaning they are not interchangeable, and each holds a distinct value. Both NFTs and cryptocurrencies rely on blockchain transactions to validate authenticity and ownership. NFTs serve as a digital certificate of ownership, and each time an NFT changes hands, the transaction is recorded on the blockchain decentralized, public ledger.NFTs also incorporate smart contract technology, which is particularly relevant to the field of medicine. For instance, in the art world, the original artist may receive royalty every time their artwork is resold. This same mechanism can be applied to healthcare data, offering both security and potential financial benefits to patients.In the U.S., it is estimated that each patient generates approximately 80 megabytes of health data annually. Utilizing NFTs to manage medical data would allow patients to enhance the confidentiality of their personal health information. Through smart contracts, patients could control and define who has access to their data—whether it’s their primary care physician, an emergency room doctor, a radiologist, or a specialist at a cancer center. Once recorded on a public, decentralized blockchain, this data becomes immutable and highly secure, preventing tampering or unauthorized access.This model empowers patients and shifts control away from commercial or nonprofit institutions that often manage and monetize patient data without individual input. As Dr. Kristin Kostick-Quenet has pointed out, once health information is digitized, it typically falls out of the patient’s control and is commodified by companies for profit. NFTs offer a solution: patients could maintain ownership over their data and even receive financial compensation when it is accessed or utilized.The digital contracts associated with NFTs also allow patients to trace the use of their data—who accessed it, when, how, and why. According to an article from Cointelegraph, the healthcare platform Aimedis plans to tokenize anonymized patient data into NFTs, which can then be sold to pharmaceutical companies. In return, patients may receive revenue from the sales of these NFT tokens. However, a key challenge remains, healthcare IT systems are currently fragmented and not yet optimized for this level of integration. In an ideal future, patients would use a single login interface to manage all their health data.Importantly, NFTs can enhance the quality and accessibility of medical data, making it more suitable for artificial intelligence applications and data mining. Aimedis aims to revolutionize global exchange and monetize de-identified health data using blockchain and NFT technologies.NFTs also have direct applications in radiology. Patients could predefine which radiologists or physicians can access their imaging studies and reports. They could also track who views their data and under what circumstances. If their imaging is later sold or used by a commercial entity—such as a medical center or pharmaceutical company—for research or drug development, the patient could receive royalty payments each time it is used. For example, if a cancer patient undergoes a PET/CT scan and the resulting data is converted into an NFT, a pharmaceutical company using that data in drug research might owe compensation to the patient.Moreover, NFTs could enhance the information available to radiologists. For example, they could include important historical details, such as previous reactions to gadolinium contrast, a history of renal insufficiency, or retained metal that could affect MRI compatibility. Such centralized and accessible data would aid in ensuring patient safety and improving diagnostic accuracy.With the rise of telemedicine, NFTs could also play a key role in verifying transactions between the physical and digital healthcare environments. For example, a doctor’s prescription or imaging order could be tokenized, eliminating any ambiguity regarding its origin or intent. In radiology, this could clarify whether a referring physician wanted a CT scan with or without contrast or preferred a two-view chest X-ray over a portable study—ultimately improving communication between referring clinicians and radiology departments.Teleradiology images could also be tokenized, giving patients visibility over who has accessed their reports and to whom results were sent. In addition, NFTs could be used to verify the credentials of radiologists, such as medical degrees and certifications. Since this information would be recorded on an immutable blockchain, it would be secure and tamper-proof. This could reduce administrative burdens, such as those placed on radiology file rooms by repeated requests for copies of reports or credentials.Tokenized radiology data may also serve as a valuable audit trail, allowing radiologists to confirm that their reports were viewed and used appropriately by referring clinicians.While numerous challenges remain, including legal considerations, government regulations, and the environmental impact of blockchain technology, NFTs are poised to play a growing role in healthcare. Diagnostic imaging, often at the forefront of technological innovation in medicine, is well positioned to benefit from the adoption of blockchain-based NFT applications.References:Conti, R. (2022, August 16). What is an NFT? non-fungible tokens explained. Forbes. Retrieved August 29, 2022, from https://www.forbes.com/advisor/investing/cryptocurrency/nft-non-fungible-token/Culbertson, N. (2021, August 6). Council post: The Skyrocketing Volume of Healthcare Data Makes Privacy Imperative. Forbes. Retrieved August 29, 2022, from https://www.forbes.com/sites/forbestechcouncil/2021/08/06/the-skyrocketing-volume-of-healthcare-data-makes-privacy-imperative/?sh=327ba8536555Diaz, N. (n.d.). What nfts need to achieve before healthcare implementation. Becker’s Hospital Review. Retrieved August 29, 2022, from https://www.beckershospitalreview.com/healthcare-information-technology/what-nfts-need-to-achieve-before-healthcare-implementation.htmlHarrison, S. (2022, April 13). Some medical ethicists endorse nfts-here’s why. Scientific American. Retrieved August 29, 2022, from https://www.scientificamerican.com/article/some-medical-ethicists-endorse-nfts-heres-why/HHMGlobal, C. T. (2022, April 11). Content team HHMGlobal. HHM Global B2B Online Platform Magazine. Retrieved August 29, 2022, from https://www.hhmglobal.com/knowledge-bank/news/can-nfts-be-repurposed-for-the-healthcare-industryJones, C. (2021, September 13). Why nfts, crypto and blockchain can help e-health thrive. Cointelegraph. Retrieved August 29, 2022, from https://cointelegraph.com/news/why-nfts-crypto-and-blockchain-can-help-e-health-thriveKhatri, N. (2021, December 8). Beyond Trendy Investments: Three applications of nfts in healthcare and Pharma Marketing. PM360. Retrieved August 29, 2022, from https://www.pm360online.com/beyond-trendy-investments-three-applications-of-nfts-in-healthcare-and-pharma-marketing/Kostick-Quenet, K., Mandl, K. D., Minssen, T., Cohen, I. G., Gasser, U., Kohane, I., & McGuire, A. L. (2022). How nfts could transform Health Information Exchange. Science, 375(6580), 500–502. https://doi.org/10.1126/science.abm2004Limited, V. M. P. (n.d.). AIMEDIS announces the NFT Healthcare Platform. Newsfile. Retrieved August 29, 2022, from https://www.newsfilecorp.com/release/103552/Aimedis-Announces-the-NFT-Healthcare-PlatformMcGuire, A. (2022, February 4). Can NFT technology benefit healthcare? in. Retrieved August 29, 2022, from https://healthcare-in-europe.com/en/news/can-nft-technology-benefit-healthcare.htmlShyam Sabat MD, M. B. A. (2021, April 27). Blockchain - promises for academic radiology. LinkedIn. Retrieved August 29, 2022, from https://www.linkedin.com/pulse/blockchain-promises-academic-radiology-shyam-sabat-md-sabat-mdTagliafico AS, Campi C, Bianca B, et al. Blockchain in radiology research and clinical practice: current trends and future directions. La Radiologia Medica. 2022 Apr;127(4):391-397.YouTube. (2021, September 20). How nfts will revolutionize medicine. YouTube. Retrieved August 29, 2022, from https://www.youtube.com/watch?v=TnhmUltTGo
Background: Blockchain and Multi-Agent System (MAS) are increasingly combined to support decentralized, secure, and autonomous peer-to-peer energy trading in microgrid environments. Objectives: This systematic review investigates how blockchain and MAS are integrated to support microgrid energy trading, identifies architectural and operational models, examines real-world implementations, and highlights technical, regulatory, and security challenges. Unlike prior reviews that focus on blockchain or MAS in isolation, this study provides a unified and comparative analysis of their joint integration. Methods: Following PRISMA 2020 guidelines, a systematic search was conducted in IEEE Xplore, ACM Digital Library, and ScienceDirect, with the last search performed on 10 January 2025. Eligible studies focused on blockchain–MAS integration in microgrid energy trading; non-energy and non-microgrid applications were excluded. Study selection was performed independently by two reviewers, and methodological quality was assessed using an adapted Joanna Briggs Institute (JBI) checklist. A narrative synthesis categorized integration levels, blockchain platforms, MAS roles, and implementation contexts. Results: A total of 104 studies were included. Three dominant integration levels were identified—basic, intermediate, and advanced—distinguished by how decision-making responsibilities are distributed between MAS and smart contracts. Ethereum and Hyperledger Fabric were the most commonly used platforms. MAS agents perform concrete operational functions such as bid and offer generation, price negotiation, matching, and local energy optimization, fundamentally transforming control and monitoring processes. By enabling distributed, intelligent agents to perform real-time sensing, analysis, and response, an MAS enhances system resilience and adaptability. This architecture allows for proactive fault detection, dynamic resource allocation, and coherent, large-scale operations without centralized bottlenecks. Blockchain ensured transparency, trust, and secure transaction execution. Major challenges include scalability constraints, interoperability limitations with legacy grids, regulatory uncertainty, and real-time performance issues. Limitations: Most included studies were simulation-based, with limited real-world deployment and substantial heterogeneity in evaluation metrics. Conclusions: Blockchain–MAS integration shows strong potential for secure, transparent, and decentralized microgrid energy trading. Addressing scalability, regulatory frameworks, and interoperability is essential for large-scale adoption. Future research should emphasize real-world validation, standardized integration architectures, and AI-enabled MAS optimization. Funding: No external funding. Registration: This systematic review was not registered.
Ethereum Inscriptions (Ethscriptions) repurpose Ethereum calldata into a persistent inscription channel by embedding \texttt{data:}~URI payloads. These transactions typically target externally owned accounts, allowing the payload to bypass EVM execution while remaining permanently replicated across full nodes. Although calldata was originally designed for compact smart-contract parameters, this repurposing enables structured data embedding with long-term storage consequences. We present the first large-scale empirical study of Ethscriptions, treating them as a distinct \emph{calldata-resident workload} rather than merely a subset of general calldata usage. Our analysis focuses on the \textit{Ethscription} operational subset, which consists of payloads that decode to JSON and conform to a token-operation grammar (e.g., \texttt{p}, \texttt{op}, \texttt{tick}, \texttt{amt}). From $6.27$ million Ethscription candidates (\Uone), we extract $4.75$ million Ethscription operations (\Utwo, $75.8\%$ of \Uone). This result shows that structured token-like activity dominates the ecosystem. Our measurements further reveal (i) a complete workload lifecycle compressed into nine months (bootstrap, expansion, saturation), (ii) proliferation of $30$+ competing protocols without convergence toward a dominant standard, (iii) a lifecycle funnel exhibiting $201\times$ deploy-to-mint amplification and a $57.6{:}1$ mint-to-transfer collapse indicative of speculative minting, (iv) extreme participation inequality (Gini~$0.86$), and (v) a measurable permanent data footprint imposed on the Ethereum network.