Patrick Spiesberger, Jan Droll, Hannes Hartenstein
In the Ethereum system, the exclusion of specific transactions is currently feasible with minimal effort due to a power imbalance among entities. This censorship opportunity threatens the dependability of time-sensitive services deployable on Ethereum. In this paper, we look at this threat from an access control perspective and attribute it to a lack of accountability for censorship, a lack of policy definition and enforcement, as well as to the lack of disincentivization of policy violation. We propose an approach to enforceable policies in Ethereum. Furthermore, we demonstrate how a specific policy can address the shortcomings of existing censorship mitigation techniques, particularly Inclusion Lists. Under the assumption that block assemblers are unwilling to incur significant financial penalties as well as that the local view on the network messages is sufficiently consistent, the proposed approach guarantees the inclusion of a transaction in a block within 27 seconds in a non-saturated network. The empirical validation of sufficiently consistent views on outstanding transactions is currently in progress.
This paper sets out to explore how blockchain-based technologies, particularly non-fungible tokens (NFTs), are influencing future business models. Drawing on the relevant literature and a multiple case study of blockchain ventures, we demonstrate how the technology leads to new polyadic mechanisms of value creation and value capture. A clarification of NFTs and related concepts, together with their use values and exchange value determinants, led us to argue that the polyadic mechanisms differ from those in dyadic and triadic business models. Overall, we identify a total of 39 NFT technology affordances that fall into four types: utility, social, financial, and legal affordances. In addition, the NFT business ecosystem is mapped in terms of sources of generativity, mixed-side network effects, and the convergence of complementors within the ecosystem. Finally, this study explores three distinct mechanisms of stakeholder collaboration using NFTs: token distribution and fundraising, polyadic value creation and capture, and smart contract-enabled facilitation of stakeholder interactions. Based on the insights, we discuss the impact of NFTs and blockchain technology on society (illustrated by two cases of NFT ticketing and decentralized apps), and the implications for theory, practice, and policy. • Blockchain technology enables newly emerging business models with polyadic relationships. • Novel business models in Web 3.0 environments involve multiple use values and exchange value determinants. • NFT Technology affordances identified in terms of utility, social, financial, and legal affordances. • The emerging ecosystem is characterized by sources of generativity, mixed-side network effects, and convergence.
Juan Cano-Benito, Andrea Cimmino, Sven Hertling, Heiko Paulheim · 5 authors
Data spaces are emerging as decentralised infrastructures that enable sovereign, secure, and trustworthy data exchange among multiple participants. To achieve semantic interoperability within these environments, the use of semantic web technologies and knowledge graphs has been proposed. Although distributed ledger technologies (DLT) fit as the underlying infrastructure for data spaces, there remains a significant gap in terms of the efficient storage of semantic data on these platforms. This paper presents a systematic evaluation of semantic data storage across different types of DLT (public, private, and hybrid), using a real-world knowledge graph as an experimental basis. The study compares performance, storage efficiency, resource consumption, and the capabilities to update and query semantic data. The results show that private DLTs are the most efficient for storing and managing semantic content, while hybrid DLTs offer a balanced trade-off between public auditability and operational efficiency. This research leads to a discussion on the selection of the most appropriate DLT infrastructure based on the data sovereignty requirements of decentralised data ecosystems.
Ala’a Shamaseen, Mohammad Qatawneh, Basima Elshqeirat
Traditional systems in real life lack transparency and ease of use due to their reliance on centralization and large infrastructure. Furthermore, many sectors that rely on information technology face major challenges related to data integrity, trust, and counterfeiting, limiting scalability and acceptance in the community. With the decentralization and digitization of energy transactions in smart grids, security, integrity, and fraud prevention concerns have increased. The main problem addressed in this study is the lack of a secure, tamper-resistant, and decentralized mechanism to facilitate direct consumer-to-prosumer energy transactions. Thus, this is a major challenge in the smart grid. In the blockchain, current consensus algorithms may limit the scalability of smart grids, especially when depending on popular algorithms such as Proof of Work, due to their high energy consumption, which is incompatible with the characteristics of the smart grid. Meanwhile, Proof of Stake algorithms rely on energy or cryptocurrency stake ownership, which may make the smart grid environment in blockchain technology vulnerable to control by the many owning nodes, which is incompatible with the purpose and objective of this study. This study addresses these issues by proposing and implementing a hybrid framework that combines the features of private and public blockchains across three integrated layers: user interface, application, and blockchain. A key contribution of the system is the design of a novel consensus algorithm, Proof of Energy, which selects validators based on node roles and randomized assignment, rather than computational power or stake ownership. This makes it more suitable for smart grid environments. The entire framework was developed without relying on existing decentralized platforms such as Ethereum. The system was evaluated through comprehensive experiments on performance and security. Performance results show a throughput of up to 60.86 transactions per second and an average latency of 3.40 s under a load of 10,000 transactions. Security validation confirmed resistance against digital signature forgery, invalid smart contracts, race conditions, and double-spending attacks. Despite the promising performance, several limitations remain. The current system was developed and tested on a single machine as a simulation-based study using transaction logs without integration of real smart meters or actual energy tokenization in real-time scenarios. In future work, we will focus on integrating real-time smart meters and implementing full energy tokenization to achieve a complete and autonomous smart grid platform. Overall, the proposed system significantly enhances data integrity, trust, and resistance to counterfeiting in smart grids.
The blockchain oracle problem, which refers to the challenge of injecting reliable external data into decentralized systems, remains a fundamental limitation to the development of trustless applications. While recent years have seen a proliferation of architectural, cryptographic, and economic strategies to mitigate this issue, no one has yet fully resolved the fundamental question of how a blockchain can gain knowledge about the off-chain world. In this position paper, we critically assess the role artificial intelligence (AI) can play in tackling the oracle problem. Drawing from both academic literature and practitioner implementations, we examine how AI techniques such as anomaly detection, language-based fact extraction, dynamic reputation modeling, and adversarial resistance can enhance oracle systems. We observe that while AI introduces powerful tools for improving data quality, source selection, and system resilience, it cannot eliminate the reliance on unverifiable off-chain inputs. Therefore, this study supports the idea that AI should be understood as a complementary layer of inference and filtering within a broader oracle design, not a substitute for trust assumptions.
While Ethereum's discovery protocols (Discv4/ Discv5) incorporate robust cryptographic designs to protect user privacy, real-world deployment reveals critical vulnerabilities when users deviate from security guidelines. In this paper, we design a system called EGNInfoLeaker. Our study is the first work that uncovers widespread public key reuse across Ethereum's peer-to-peer networks - a practice that fundamentally undermines the protocol's privacy guarantees. Through systematic analysis of 300 real-world network snapshots, we identify 83 users controlling 483 service nodes via public key reuse, enabling precise de-anonymization through IP correlation. Using evidence collected by EGNInfoLeaker, our Graph-Based Identity Association Algorithm links users to network entities and generates comprehensive user profiles. For User27, it exposes the public key, IP, network ID, location (country/region/city), and ISP/ORG details. The EGNInfoLeaker system demonstrates how such cryptographic misuse transforms theoretical anonymity into practical identity leakage, exposing users to surveillance and targeted attacks. These findings establish that protocol security depends not only on sound design but also on strict user compliance. Going forward, our detection framework provides a foundation for enhancing real-world privacy preservation in decentralized networks.
Alper Alimoğlu, Kamil Erdayandı, Mustafa Mustafa, Ümit Cali
This paper proposes a new decentralized framework, named EDGChain-E (Encrypted-Data-Git Chain for Energy), designed to manage version-controlled, encrypted energy data using blockchain and the InterPlanetary File System. The framework incorporates a Decentralized Autonomous Organization (DAO) to orchestrate collaborative data governance across the lifecycle of energy research and operations, such as smart grid monitoring, demand forecasting, and peer-to-peer energy trading. In EDGChain-E, initial commits capture the full encrypted datasets-such as smart meter readings or grid telemetry-while subsequent updates are tracked as encrypted Git patches, ensuring integrity, traceability, and privacy. This versioning mechanism supports secure collaboration across multiple stakeholders (e.g., utilities, researchers, regulators) without compromising sensitive or regulated information. We highlight the framework's capability to maintain FAIR-compliant (Findable, Accessible, Interoperable, Reusable) provenance of encrypted data. By embedding hash-based content identifiers in Merkle trees, the system enables transparent, auditable, and immutable tracking of data changes, thereby supporting reproducibility and trust in decentralized energy applications.
Censorship resilience is a fundamental assumption underlying the security of blockchain protocols. Additionally, the analysis of blockchain security from an economic and game theoretic perspective has been growing in popularity in recent years. In this work, we present a surprising rational censorship attack on blockchain censorship resilience when we adopt the analysis of blockchain security from a game theoretic lens and assume all users are rational. In our attack, a colluding group with sufficient voting power censors the remainder nodes such that the group alone can gain all the rewards from maintaining the blockchain. We show that if nodes are rational, coordinating this attack just requires a public read and write blackboard and we formally model the attack using a game theoretic framework. Furthermore, we note that to ensure the success of the attack, nodes need to know the total true voting power held by the colluding group. We prove that the strategy to join the rational censorship attack and also for nodes to honestly declare their power is a subgame perfect equilibrium in the corresponding extensive form game induced by our attack. Finally, we discuss the implications of the attack on blockchain users and protocol designers as well as some potential countermeasures.
Growing applications of Internet of Medical Things (IoMT) devices have revolutionized the healthcare sector because of remote patient tracking, diagnosis, and data-supported decision-making. The kind of medical data collected from these devices, however, is very sensitive, which makes it very vulnerable to issues of security, privacy, and integrity. This paper suggests a way to keep IoMT data safe using the Algorand blockchain, XChaCha20-Poly1305 encryption, and different types of decentralized storage. Using the platform's fast, highly scalable, and highly secure architecture, Algorand blockchain framework makes sure that encrypted patient medical records are stored permanently and cannot be changed. To properly encrypt sensitive IoMT data before storing the data in DSNs including IPFS, Storj, and Filecoin, a modern stream cipher called 'XChaCha20-Poly1305' is used. Decentralized storage ensures data accessibility and distribution simultaneously, minimizing reliance on associated server points that are susceptible to single points of failure. Besides data secrecy, accuracy, and anti-intrusion attack breakout measures, this work explores the security measures implied by this architecture. Additionally, it assesses the efficacy of various decentralized storage options and highlights their benefits and drawbacks when it comes to storing large amounts of medical data. It can be concluded that the proposed framework is cost-effective and capable of expansion and implementation in the modern healthcare environment of IoMT data protection.
Open access
Blockchain Technology Applications and Security
IoT and Edge/Fog Computing
Advanced Steganography and Watermarking Techniques
The rapid advancement of smart grids, propelled by the integration of distributed energy resources (DERs) and renewable energy technologies, has exposed key challenges such as interoperability, standardization, and data security. These issues impede the efficient operation and scalability of smart grid applications, particularly in enabling decentralized, real-time energy trading and demand response management. Effective management of DERs and demand response systems relies heavily on seamless information exchange, data-driven decision-making, and robust digital communication frameworks to maintain system stability and operational efficiency. To address these challenges, this study presents the Blockchain Consortium-Based Demand Energy Trading System (BC-DETS), a blockchain-powered framework designed to enhance interoperability, security, and standardized energy trading mechanisms within smart grid ecosystems. Comprehensive simulations have validated the effectiveness of BC-DETS using Key Performance Indicators (KPIs) such as transaction latency, demand response participation rate, operational cost reductions, and overall grid efficiency under diverse scenarios. Findings reveal a 35% boost in grid efficiency through optimized energy distribution and minimized energy losses, alongside a 15% decrease in operational costs due to reduced transaction overhead and improved energy allocation. Moreover, demand response participation rates increased by 40%, facilitated by secure and transparent blockchain-enabled real-time energy transactions. These numerical findings underscore blockchain’s transformative potential in enhancing the scalability, security, and inclusivity of smart grids, establishing a foundational platform for future advancements in blockchain standardization and sustainable energy management solutions.
S Almeida, Washington Luiz de L. Praxedes, Leone B. Hollanda, Cidcley Teixeira de Souza · 5 authors
The purpose of this work is to analyze the performance of two different blockchain networks in the context of healthcare applications. Blockchain networks are known for their decentralized structure, resiliency, and tamperproofness. Hyperledger Fabric and Ethereum were used to benchmark and compare its performance in two scenarios for a simple blockchain smart contract that stores patient image data. The results showed a slight advantage for Ethereum in increased load tests and it also obtained a lower degree of variation during these tests.
Yusuf Oli Rahat, Md Kamrul Islam, Shah Farhan Rabbani
Blockchain-based financial systems are increasingly evaluated not only as speculative infrastructures but as operational payment, settlement, and record-keeping networks that can be benchmarked against incumbent financial rails. This paper develops a quantitative, data-driven framework for assessing blockchain-based financial systems across four dimensions that matter in global markets: transaction efficiency, transparency, cost optimization, and overall performance resilience. Drawing on evidence from public blockchain networks, payment and remittance statistics, policy experiments, and institutional distributed-ledger pilots, the study synthesizes academic literature with world data from the World Bank, the Federal Reserve Bank of New York, the Bank for International Settlements, Visa, Bitcoin, Ethereum, and Solana documentation and analytics. The paper proposes a metrics architecture that combines latency, throughput, fee burden, settlement certainty, auditability, availability, governance quality, and interoperability into a unified comparative scorecard. It then applies the framework to three categories of blockchain-based finance: public permissionless chains, permissioned institutional distributed ledgers, and hybrid tokenized payment systems. The evidence suggests that blockchain systems create measurable gains in traceability, programmability, and atomic settlement, especially in cross-border and multi-party workflows where reconciliation frictions are costly. However, these gains are uneven. Public chains often face volatility in fees, congestion risk, and governance externalities, while permissioned systems improve control and compliance at the expense of openness and composability. The paper argues that the relevant policy and managerial question is therefore not whether blockchain is universally superior, but under which transaction environments it dominates legacy systems on speed, transparency, cost, and operational risk. The study concludes with a research agenda for standardized blockchain performance metrics, institution-grade benchmarking, and explainable analytics for digital financial infrastructure.
Crowd-sensing is considered a robust model for data collection, yet with challenges related to data availability and privacy. Traditional techniques such as data encryption and anonymization may not fully mitigate these issues, since anonymized data can still be traced back to individual users, and the volume of data generated can reveal user identities. This paper introduces a system that employs smart contracts and blockchain technology to manage crowd-sensing campaigns. The smart contract oversees user subscriptions, data encryption, and decentralized storage, creating a secure data marketplace. Incentive mechanisms within the smart contract promote user participation. Simulation results validate the system’s feasibility, emphasizing the importance of user engagement for data credibility and the impact of geographical data scarcity on rewards.
Nguyen Thi Kim, Ho Thi Hien, Vu Thien Bach, Bui Hong Cuong · 5 authors
In Vietnam, the digital banking model has undergone rapid development in recent years. This paper evaluates the current state of digital banking development in Vietnam from several aspects: (i) the legal framework for digital banking development; (ii) market participants in the digital banking sector; (iii) methods of digital banking development; and (iv) the quality of digital banking services, thereby identifying the current level of digital banking development in Vietnam. According to the research team's assessment, digital banking in Vietnam is currently in the formative stage, with significant potential for growth. The application of digital technology in banking operations in Vietnam has had a positive impact on operational models, driving a strong transformation in internal business systems and in the provision of advanced products and services to customers. The application of digital technology in banking is considered the next phase after digitalization in the digital transformation process, serving as an important support for the digital banking strategy of the banking industry. This study aims to discuss digital technologies applied in banking operations, such as Artificial Intelligence (AI), Machine Learning (ML), the Internet of Things (IoT), Big Data, Cloud Computing, Distributed Ledger Technology (DLT), Blockchain, Biometrics, and the products and services created by financial technology companies (Fintech). The application of Big Data in banking is particularly significant for building and managing data resources in the finance and banking sector. Currently, an increasing number of banks are adopting Big Data in their operations to keep pace with the rapid development of modern society, where customers seek simplified, fast, and convenient procedures. Big Data is a powerful tool that can help banks achieve this goal. However, the implementation of Big Data in banks still faces certain challenges.
Abdullah Alsaleh, Ghanshyam G. Tejani, Shailendra Mishra, Sunil Kumar Sharma · 5 authors
The detection of brain tumors is crucial in medical imaging, because accurate and early diagnosis can have a positive effect on patients. Because traditional deep learning models store all their data together, they raise questions about privacy, complying with regulations and the different types of data used by various institutions. We introduce the anisotropic-residual capsule hybrid Gorilla Badger optimized network (Aniso-ResCapHGBO-Net) framework for detecting brain tumors in a privacy-preserving, decentralized system used by many healthcare institutions. ResNet-50 and capsule networks are incorporated to achieve better feature extraction and maintain the structure of images' spatial data. To get the best results, the hybrid Gorilla Badger optimization algorithm (HGBOA) is applied for selecting the key features. Preprocessing techniques include anisotropic diffusion filtering, morphological operations, and mutual information-based image registration. Updates to the model are made secure and tamper-evident on the Ethereum network with its private blockchain and SHA-256 hashing scheme. The project is built using Python, TensorFlow and PyTorch. The model displays 99.07% accuracy, 98.54% precision and 99.82% sensitivity on assessments from benchmark CT imaging of brain tumors. This approach also helps to reduce the number of cases where no disease is found when there is one and vice versa. The framework ensures that patients' data is protected and does not decrease the accuracy of brain tumor detection.
This study investigates the effect of the regulatory framework in Nigeria, specifically Anti-Money Laundering (AML) and Know Your Customer (KYC) regulations, and their influence on users' preference between centralized exchanges (CEX) and decentralized exchanges (DEX) in Nigeria. Using a quantitative study design, a purposive sampling method was used to select 358 Nigerians with cryptocurrency trading experience. Using a structured, self-administered questionnaire. The data were analysed using binomial logistic regression in SPSS. Results revealed that AML sensitivity was positively correlated with the use of DEX such that sensitivity to AML policy is likely to significantly raise the odds of the user choosing a DEX over a CEX. Conversely, KYC conditions were negatively correlated with the choice of the CEX, where the stricter the KYC requirements, the lower the odds of the user choosing a centralized exchange. The model also estimated overall classification accuracy at 70.7%, indicating the predictive ability of these regulatory forces. The study concludes that AML and KYC frameworks are major impetuses of exchange choice and recommends a tiered KYC system, users' education, and the use of privacy-enhanced protocols. It also emphasized the importance of the bilateral process between regulators and industry players in the creation of evenly weighted mechanisms that maximize compliance as well as the participation and trust of the users.
Cryptocurrencies, particularly Bitcoin, continue to be the most prevalent use case within the blockchain ecosystem. One of the inherent limitations of blockchain is that it can create a false sense of privacy. All transaction history and the amount of cryptocurrency held are publicly available, and this information can be easily associated with specific individuals. Many works have proposed fully-private solutions, which are ideal but not realistic in many scenarios. This paper proposes a technical solution that enables private Bitcoin payments by default, but with the option to conditionally disclose payment data. To do so, this solution relies on unlinkability by a decentralized mixer, which can be reversed by a conditional discloser using a trapdoor unlinkability function. The conditional discloser, which also provides accountability of requests, obeys the payer's policies regarding who can access payment data. To ensure compliance, we propose a mixer that does not learn anything about the payment link, but is guaranteed by Zero-Knowledge Proofs that the payment can be relinked by a specific conditional discloser. Furthermore, we provide a proof-of-concept implementation of the proofs, using Circom and SnarkJS. We also present a benchmark that demonstrates the feasibility of this solution. It incurs only one additional parameter per on-chain transaction, while the remainder of the verification data is managed off-chain.
Open access
Blockchain Technology Applications and Security
Cryptography and Data Security
Advanced Steganography and Watermarking Techniques
A powerful feature in mechanism design is the ability to irrevocably commit to the rules of a mechanism. Commitment is achieved by public declaration, which enables players to verify incentive properties in advance and the outcome in retrospect. However, public declaration can reveal superfluous information that the mechanism designer might prefer not to disclose, such as her target function or private costs. Avoiding this may be possible via a trusted mediator; however, the availability of a trustworthy mediator, especially if mechanism secrecy must be maintained for years, might be unrealistic. We propose a new approach to commitment, and show how to commit to, and run, any given mechanism without disclosing it, while enabling the verification of incentive properties and the outcome—all without the need for any mediators. Our framework is based on zero-knowledge proofs—a cornerstone of modern cryptographic theory. Applications include both private-type settings such as auctions and private-action settings such as contracts, as well as non-mediated bargaining with hidden yet binding offers.
Blockchain technology, renowned for its decentralized and secure nature, has gained substantial attention. Central to its functionality are consensus mechanisms, which are essential for validating transactions and upholding the integrity of the distributed ledger. However, the efficiency and scalability of blockchain are currently impeded by the resource limitations and excessive communication demands of existing consensus mechanisms. To address these challenges, we propose GT-BFT, a streamlined and lightweight blockchain consensus mechanism grounded in a global trust model. This model capitalizes on node behavior to form consensus groups and facilitate consensus achievement. GT-BFT integrates a novel approach of selective broadcasting along with a Byzantine threshold determination algorithm, significantly boosting both the efficiency and security of the network. Our extensive analysis and performance evaluation reveal that GT-BFT surpasses existing mechanisms in key areas such as security, system throughput, and transaction confirmation speed, marking a significant advancement in blockchain consensus technology.
NTF (non-fungible token), as a significant application of blockchain technology, is reshaping the model of art education. This paper explores the potential applications of the NTF in art education, focusing on how it influences teaching methods, the value transformation of students, the evaluation of art education, and international development. The study employs a mixed-method approach, combining questionnaire surveys (N=100) and semistructured interviews (N=15), to analyse the acceptance and impact of the NTF in art education. The results show that the NTF can increase students’ market awareness and copyright protection awareness and promote the global circulation of artworks. However, technical barriers, market volatility, and insufficient educational practice experience remain the main challenges to the widespread application of the NTF in art education. This research not only reveals the value of the NTF in art education but also provides practical guidance for future curriculum design, educational assessment, and academic certification.
Cryptocurrencies, particularly Bitcoin and Ethereum, have reshaped global conceptions of money, ownership, and exchange. With the rise of blockchain technology—distributed, immutable digital ledgers—applications have expanded into areas such as smart contracts, asset tokenization, and non-fungible tokens (NFTs). These shifts present pressing challenges to traditional Islamic legal structures, which have historically grounded financial rulings in well-defined principles such as prohibition of riba (intere... In this context, Islamic jurisprudence must critically engage with these technologies—not by rejection or blind acceptance—but through a measured analysis rooted in legal maxims and maqāṣid al-sharīʿah (the higher objectives of Islamic law). This study explores the Islamic legal perspective on cryptocurrencies and blockchain technologies, providing an analytical review of scholarly opinions, regulatory frameworks, and economic realities across Muslim and global contexts.
Geet Bawa, Harmeet Singh, Sita Rani, Aman Kataria · 5 authors
Existing organ transportation management systems face significant limitations as they do not allow patients to monitor the condition of the transplantable organ during its transportation from the donor's place to the recipient's venue. This undermines patients' confidence that the organ allotted to them remains uncontaminated, healthy, and unaffected by fluctuations in parameters including temperature, humidity, and the container's vibration, and orientation. Additionally, there is a lack of technology to ensure that the organ container remains securely closed throughout the shipment. Furthermore, the shipment data is often stored in centralized or with third-party systems, which are potentially vulnerable to security risks and single points of failure. These limitations highlight the need for a more secure, transparent, and reliable solution to improve organ transportation safety and data integrity. This paper addresses these challenges by proposing a cost-efficient, decentralized, and transparent framework for managing organ transportation that enhances data security and traceability. The proposed framework integrates Internet of Things sensors within the organ container and connects them to smart contracts via the InterPlanetary File System. The blockchain ensures data security, immutability, and decentralization, while sensors safeguard the organ by providing real-time updates on its condition. The smart contracts generate alerts for issues and notify stakeholders for prompt action. A key contribution of this work is the novel use of IPFS for off-chain data storage, which reduces blockchain storage requirements, Ether consumption, and overall system costs. A comparative analysis with existing IoT, IPFS, and blockchain-based transportation approaches demonstrates that the proposed framework consumes a negligible amount of Ether; approximately, 0.000023004, equivalent to approximately 5.52 INR; for deployment, while ensuring safe, transparent, cost-effective, and traceable organ transportation.