The Bitcoin Network is a sophisticated accounting system that allows its underlying cryptocurrency to be trusted even in the absence of a reliable financial authority. Given its undeniable success, the technology, generally referred to as blockchain, has also been proposed as a means to improve legacy accounting systems. Accounting for real-world data, however, requires the intervention of a third party known as an Oracle, which, having not the same characteristics as a blockchain, could potentially reduce the expected integration benefit. Through a systematic review of the literature, this study aims to investigate whether the papers concerning blockchain integration in accounting consider and address the limitations posed by oracles. A broad overview of the limitations that emerged in the literature is provided and distinguished according to the specific accounting integration. Results support the view that although research on the subject counts numerous articles, actual studies considering oracle limitations are lacking. Interestingly, despite the scarce production of papers addressing oracles in various accounting sectors, reporting for ESG already shows interesting workarounds for oracle limitations, with permissioned chains envisioned as a valid support for the safe storage of sustainability data.
Q&A websites rely on user-generated responses, with incentives such as reputation scores or monetary rewards often offered. While some users may find it intrinsically rewarding to assist others, studies indicate that payment can improve the quality and speed of answers. However, traditional payment processors impose minimum thresholds that many Q&A inquiries fall below. The introduction of Bitcoin enabled direct digital value transfer, yet frequent micropayments remain challenging. Recent advancements like the Lightning Network now allow frictionless micropayments by reducing costs and minimising reliance on intermediaries. This development fosters an "Internet of Value," where transferring even small amounts of money is as simple as sharing data. This study investigates integrating Lightning Network-based micropayment strategies into Q&A platforms, aiming to create a knowledge market free of minimum payment barriers. A survey was conducted to address the gap below the $2 payment level identified in prior research. Responses confirmed that incentives for asking and answering weaken as payments decrease. Findings reveal even minimal payments, such as £0.01, significantly encourage higher quality and effort in responses. The study recommends micropayment incentives for service-oriented applications, particularly Q&A platforms. By leveraging the Lightning Network to remove barriers, a more open marketplace can emerge, improving engagement and outcomes. Further research is needed to confirm if users follow through on reported intentions when spending funds.
Peer-to-peer (P2P) energy trading, Smart Grids (SG), and electric vehicle energy management are integral components of the Internet of Energy (IoE) field. The integration of Software-Defined Networks (SDNs) and Blockchain (BC) technologies into the IoE domain offers potential benefits that have only been studied in the literature in a few works. In this paper, we investigate the state-of-art solutions that leverage both SDNs and blockchain within the realm of the IoE. We categorize these solutions based on the method of integrating SDN and BC into two categories. The first category is the blockchain for SDN, where blockchain enhances the SDN directly. The second category is blockchain and SDN, where both technologies are used to enhance the proposed solutions. We identify three distinct blockchain applications based on their usage: decentralizing the SDN control plane, serving as a decentralized platform, and improving security measures. Similarly, we observe that SDN serves as a performance enhancer, a substitute for traditional networking, and solely as a control and management framework. It is posited that integrating SDNs and blockchain into IoE leads to performance enhancements, improves security, enables decentralized operations, and eliminates single points of failure in the SDN control plane. Additionally, some unaddressed issues, such as energy efficiency, smart contract management, and scalability, are discussed as potential future directions.
Autonomous AI agents present transformative opportunities and significant governance challenges. Existing frameworks, such as the EU AI Act and the NIST AI Risk Management Framework, fall short of addressing the complexities of these agents, which are capable of independent decision-making, learning, and adaptation. To bridge these gaps, we propose the ETHOS (Ethical Technology and Holistic Oversight System) framework, a decentralized governance (DeGov) model leveraging Web3 technologies, including blockchain, smart contracts, and decentralized autonomous organizations (DAOs). ETHOS establishes a global registry for AI agents, enabling dynamic risk classification, proportional oversight, and automated compliance monitoring through tools like soulbound tokens and zero-knowledge proofs. Furthermore, the framework incorporates decentralized justice systems for transparent dispute resolution and introduces AI specific legal entities to manage limited liability, supported by mandatory insurance to ensure financial accountability and incentivize ethical design. By integrating philosophical principles of rationality, ethical grounding, and goal alignment, ETHOS aims to create a robust research agenda for promoting trust, transparency, and participatory governance. This innovative framework offers a scalable and inclusive strategy for regulating AI agents, balancing innovation with ethical responsibility to meet the demands of an AI-driven future.
Researchers all over the world are employing a variety of analysis approaches in attempt to provide a safer and faster solution for sharing resources via a Multi-access Edge Computing system. Multi-access Edge Computing (MEC) is a job-sharing method within the edge server network whose main aim is to maximize the pace of the computing process, resulting in a more powerful and enhanced user experience. Although there are many other options when it comes to determining the fastest method for computing processes, our paper introduces a rather more extensive change to the system model to assure no data loss and/or task failure due to any scrutiny in the edge node cluster. RAFT, a powerful consensus algorithm, can be used to introduce an auction theory approach in our system, which enables the edge device to make the best decision possible regarding how to respond to a request from the client. Through the use of the RAFT consensus, blockchain may be used to improve the safety, security, and efficiency of applications by deploying it on trustful edge base stations. In addition to discussing the best-distributed system approach for our (MEC) system, a Deep Deterministic Policy Gradient (DDPG) algorithm is also presented in order to reduce overall system latency. Assumed in our proposal is the existence of a cluster of N Edge nodes, each containing a series of tasks that require execution. A DDPG algorithm is implemented in this cluster so that an auction can be held within the cluster of edge nodes to decide which edge node is best suited for performing the task provided by the client.
Traditional identity management systems, often centralized, face challenges around privacy, data security, and user control, leaving users vulnerable to data breaches and misuse. This paper explores the potential of using the Arweave network to develop an identity management solution. By harnessing Arweave's permanent storage, our solution offers the users a Self-Sovereign Identity (SSI) framework, that uses Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) to allow individuals and other entities to create, own, and manage their digital identities. Further, the solution integrates privacy-preserving technologies, including zero-knowledge proofs and the BBS(+) signature scheme, enabling selective disclosure. This approach ultimately enhances user privacy and supports compliance with European Union legislation and regulatory standards like the General Data Protection Regulation (GDPR) by design.
In this paper, we introduce the Sarv, a novel non-monolithic blockchain-based data structure designed to represent hierarchical relationships between digitally representable components. Sarv serves as an underlying infrastructure for a wide range of applications requiring hierarchical data management, such as supply chain tracking, asset management, and circular economy implementations. Our approach leverages a tree-based data structure to accurately reflect products and their sub-components, enabling functionalities such as modification, disassembly, borrowing, and refurbishment, mirroring real-world operations. The hierarchy within Sarv is embedded in the on-chain data structure through a smart contract-based design, utilizing Algorand Standard Assets (ASAs). The uniqueness of Sarv lies in its compact and non-monolithic architecture, its mutability, and a two-layer action authorization scheme that enhances security and delegation of asset management. We demonstrate that Sarv addresses real-world requirements by providing a scalable, mutable, and secure solution for managing hierarchical data on the blockchain.
This research provides a critical analysis regarding the way blockchain is being implemented in the financial industry, highlighting its vital role in promoting green finance, guaranteeing compliance with regulations, improving supply chain finance, boosting decentralized finance (DeFi), and strengthening the Internet of Things (IoT). It discusses how blockchain's inherent attributes could significantly boost transparency, operational efficiency, and security across these domains while also addressing the pressing challenges of scalability, system integration, and the evolving regulatory landscape.
Proof-of-Work (PoW) systems face critical challenges, including excessive energy consumption and the centralization of mining power among entities with expensive hardware. Static mining pools exacerbate these issues by reducing competition and undermining the decentralized nature of blockchain networks, leading to economic inequality and inefficiencies in resource allocation. Their reliance on centralized pool managers further introduces vulnerabilities by creating a system that fails to ensure secure and fair reward distribution. This paper introduces a novel Collaborative Proof-of-Work (CPoW) mining approach designed to enhance efficiency and fairness in the Ethereum network. We propose a dynamic mining pool formation protocol that enables miners to collaborate based on their computational capabilities, ensuring fair and secure reward distribution by incorporating mechanisms to accurately verify and allocate rewards. By addressing the centralization and energy inefficiencies of traditional mining, this research contributes to a more sustainable blockchain ecosystem.
Bruno Gomes, Samih Eisa, David R. Matos, Miguel L. Pardal
Users increasingly rely on identity providers for accessing online services and resources. However, centralized identity systems often compromise user privacy due to online activity tracking or data breaches. At the same time, many online services require digital copies of physical documents for validation in claims processes, such as providing proof of residence for opening a bank account or verifying medical images for health insurance claims. With centralized solutions, privacy depends entirely on the trusted party, but there are emerging decentralized approaches that offer greater transparency. This article introduces SoverClaim, a decentralized application prototype that empowers users to control their identity and also allows them to present digital documents with privacy. SoverClaim leverages Hyperledger Indy, a blockchain for issuing and presenting self-sovereign digital identities with transparent audit logs, and Storj, a decentralized peer-to-peer service, for secure and decentralized document storage and subsequent deletion. The prototype demonstrates the seamless integration of self-sovereign identities and document-based claims, achieving response times of under 750 ms, making it suitable for timely human interactions.
Francisco Faria, Samih Eisa, David R. Matos, Miguel L. Pardal
Blockchain technology supports decentralized, consensus-driven data storage and processing, ensuring integrity and auditability. It is increasingly adopted for use cases with multiple stakeholders with shared ownership scenarios like digital identity and supply chain management. However, real-world deployments face challenges with mistakes and intrusions. This article presents EvoChain, a chaincode framework extension introducing controlled mutability for data redaction and recovery under time-limited or specific conditions. This mechanism allows corrections during a grace period before immutability takes effect. We validated our approach using WineTracker, a Hyperledger Fabric-based supply chain application. It enables some users to cancel unwanted operations while preserving the blockchain security and maintaining data consistency. Performance evaluations showed minimal overhead with functional benefits.
Modern distributed applications in healthcare, supply chain, and the Internet of Things handle a large amount of data in a diverse application setting with multiple stakeholders. Such applications leverage advanced artificial intelligence (AI) and machine learning algorithms to automate business processes. The proliferation of modern AI technologies increases the data demand. However, real-world networks often include private and sensitive information of businesses, users, and other organizations. Emerging data-protection regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) introduce policies around collecting, storing, and managing digital data. While Blockchain technology offers transparency, auditability, and immutability for multi-stakeholder applications, it lacks inherent support for privacy. Typically, privacy support is added to a blockchain-based application by incorporating cryptographic schemes, consent mechanisms, and self-sovereign identity. This article surveys the literature on blockchain-based privacy-preserving systems and identifies the tools for protecting privacy. Besides, consent mechanisms and identity management in the context of blockchain-based systems are also analyzed. The article concludes by highlighting the list of open challenges and further research opportunities.
In the rapidly evolving landscape of the Metaverse, enhanced by blockchain technology, the efficient processing of data has emerged as a critical challenge, especially in wireless communication systems. Addressing this need, our paper introduces the innovative concept of data processing efficiency (DPE), aiming to maximize processed bits per unit of resource consumption in blockchain-empowered Metaverse environments. To achieve this, we propose the DPE-Aware User Association and Resource Allocation (DAUR) algorithm, a tailored solution for these complex systems. The DAUR algorithm transforms the challenging task of optimizing the sum of DPE ratios into a solvable convex optimization problem. It uniquely alternates the optimization of key variables like user association, work offloading ratios, task-specific computing resource distribution, bandwidth allocation, user power usage ratios, and server computing resource allocation ratios. Our extensive numerical results demonstrate the DAUR algorithm's effectiveness in DPE.
Abraham Itzhak Weinberg, Pythagoras Petratos, Alessio Faccia
Abstract This paper explores the coexistence possibilities of Central Bank Digital Currencies (CBDCs) and blockchain-based cryptocurrencies within a post-quantum computing landscape. It examines the implications of emerging quantum algorithms and cryptographic techniques such as Multi-Party Computation (MPC) and Oblivious Transfer (OT). While exploring how CBDCs and cryptocurrencies might integrate defenses like post-quantum cryptography, it highlights the substantial hurdles in transitioning legacy systems and fostering widespread adoption of new standards. The paper includes comprehensive evaluations of CBDCs in a quantum context. It also features comparisons to alternative cryptocurrency models. Additionally, the paper provides insightful analyses of pertinent quantum methodologies. Examinations of interfaces between these methods and blockchain architectures are also included. The paper carries out considered appraisals of quantum threats and their relevance for cryptocurrency schemes. Furthermore, it features discussions of the influence of anticipated advances in quantum computing on algorithms and their applications. The paper renders the judicious conclusion that long-term coexistence is viable provided challenges are constructively addressed through ongoing collaborative efforts to validate solutions and guide evolving policies.
As a key enabler of Web3, Ethereum has long faced scalability challenges. The recent EIP-4844 upgrade aims to alleviate the scalability issue by introducing the ''blob'', a new data structure for Layer-2 rollups that enables off-chain storage with much reduced costs. Yet, this new mechanism's impact on Ethereum, and the wider Web3 ecosystem, remains largely underexplored. In this paper, we conduct the first large-scale empirical analysis of the post-EIP-4844 ecosystem, leveraging a dataset of 319.5 million transactions, out of which 1.3 million are blob-carrying. Our analysis reveals two major trends: (1) average block size has increased 2.5 times, from 150 KB to 400 KB, while the share of conventional transactions has shrunk from over $150$ KB to around 80 KB; (2) rollups are rapidly migrating from expensive calldata, falling from approximately 7,500 to nearly zero, toward cheap blobs, rising from zero to about 10,000. These shifts introduce a new economic game between block builders and rollups. Thus, we develop a game-theoretic model to characterize their equilibrium strategies: a profit-maximizing inclusion rule for builders, and a cost-minimizing blob batching strategy for rollups. Empirically, however, we find notable economic inefficiencies: for example, 29.48% of blob-containing blocks are built sub-optimally, yielding less revenue than available alternatives. These findings highlight the intricacies of the blob marketplace, and our work has established both methodological and empirical foundations to understand the evolving post-EIP4844 Ethereum ecosystem.
With the rapid development of Decentralized Finance (DeFi) and Real-World Assets (RWA), the importance of blockchain oracles in real-time data acquisition has become increasingly prominent. Using cryptographic techniques, threshold signature oracles can achieve consensus on data from multiple nodes and provide corresponding proofs to ensure the credibility and security of the information. However, in real-time data acquisition, threshold signature methods face challenges such as data inconsistency and low success rates in heterogeneous environments, which limit their practical application potential. To address these issues, this paper proposes an innovative dual-strategy approach to enhance the success rate of data consensus in blockchain threshold signature oracles. Firstly, we introduce a Representative Enhanced Aggregation Strategy (REP-AG) that improves the representativeness of data submitted by nodes, ensuring consistency with data from other nodes, and thereby enhancing the usability of threshold signatures. Additionally, we present a Timing Optimization Strategy (TIM-OPT) that dynamically adjusts the timing of nodes' access to data sources to maximize consensus success rates. Experimental results indicate that REP-AG improves the aggregation success rate by approximately 56.6\% compared to the optimal baseline, while the implementation of TIM-OPT leads to an average increase of approximately 32.9\% in consensus success rates across all scenarios.
Options are fundamental to blockchain-based financial services, offering essential tools for risk management and price speculation, which enhance liquidity, flexibility, and market efficiency in decentralized finance (DeFi). Despite the growing interest in options for blockchain-resident assets, such as cryptocurrencies, current option mechanisms face significant challenges, including a high reliance on trusted third parties, limited asset support, high trading delays, and the requirement for option holders to provide upfront collateral. In this paper, we present a protocol that addresses the aforementioned issues. Our protocol is the first to eliminate the need for holders to post collateral when establishing options in trustless service environments (i.e. without a cross-chain bridge), which is achieved by introducing a guarantee from the option writer. Its universality allows for cross-chain options involving nearly \textit{any} assets on \textit{any} two different blockchains, provided the chains' programming languages can enforce and execute the necessary contract logic. Another key innovation is reducing option position transfer latency, which uses Double-Authentication-Preventing Signatures (DAPS). Our evaluation demonstrates that the proposed scheme reduces option transfer latency to less than half of that in existing methods. Rigorous security analysis proves that our protocol achieves secure option trading, even when facing adversarial behaviors.
BRC20 tokens are a type of non-fungible asset on the Bitcoin network. They allow users to embed customised content within Bitcoin's satoshis. The token frenzy reached a market size of US\$2.811\,b (2023Q3--2025Q1). However, this intuitive design has not undergone serious security scrutiny. We present the first analysis of BRC20's \emph{transfer} mechanism and identify a new attack vector. A typical BRC20 transfer involves two "bundled" on-chain transactions with different fee levels: the first (i.e., \textbf{Tx1}) with a lower fee inscribes the \textsf{transfer} request, while the second (i.e., \textbf{Tx2}) with a higher fee finalizes the actual transfer. An adversary can send a manipulated fee transaction (falling between the two fee levels), which causes \textbf{Tx1} to be processed while \textbf{Tx2} is pinned in the mempool. This locks BRC20 liquidity and disrupts normal withdrawal requests from users. We term this the \emph{BRC20 pinning attack}. We validated the attack in real-world settings in collaboration with Binance researchers. With their knowledge and permission, we conducted a controlled test against Binance's ORDI hot wallet, resulting in a temporary suspension of ORDI withdrawals for 3.5 hours. Recovery was performed shortly after. Further analysis confirms that the attack can be applied to over \textbf{90\%} of inscription-based tokens within the Bitcoin ecosystem.
Lorenzo Gigli, Ivan Zyrianoff, Federico Montori, Luca Sciullo · 6 authors
The rapid expansion of the Internet of Things (IoT) has led to significant data reliability and system transparency challenges, aggravated by the centralized nature of existing IoT architectures. This centralization often results in siloed data ecosystems, where interoperability issues and opaque data handling practices compromise both the utility and trustworthiness of IoT applications. To address these issues, we introduce ZONIA (Zero-trust Oracle Network for IoT Applications), a novel blockchain oracle system designed to enhance data integrity and decentralization in IoT environments. Unlike traditional approaches that rely on Trusted Execution Environments and centralized data sources, ZONIA utilizes a decentralized, zero-trust model that allows for anonymous participation and integrates multiple data sources to ensure fairness and reliability. This paper outlines ZONIA's architecture, which supports semantic and geospatial queries, details its data reliability mechanisms, and presents a comprehensive evaluation demonstrating its scalability and resilience against data falsification and collusion attacks. Both analytical and experimental results demonstrate ZONIA's scalability, showcasing its feasibility to handle an increasing number of nodes in the system under different system conditions and workloads. Furthermore, the implemented reputation mechanism significantly enhances data accuracy, maintaining high reliability even when 40\% of nodes exhibit malicious behavior.
Decentralized Exchanges (DEXs) are pivotal applications in the Decentralized Finance (DeFi) landscape, aiming to facilitate trustless cryptocurrency trading by relying on smart contracts and blockchain networks. The developments in the DEXs sector began with the implementation of an Automated Market Maker (AMM) system using a simple math formula by Uniswap in 2018. Absorbing significant funding and the attention of web3 enthusiasts, DEXs have seen numerous advancements in their evolution. A notable recent advancement is the introduction of hooks in Uniswap v4, which allows users to take advantage of a wide range of plugin-like features with liquidity pools. This paper provides a comprehensive classification and comparative analyses of prominent DEX protocols, namely Uniswap, Curve, and Balancer, in addition to investigating other protocols' noteworthy aspects. The evaluation framework encompasses mechanisms, components, mathematical formulations, and the performance of liquidity pools. The goals are to elucidate the strengths and limitations of different AMM models, highlight emerging concepts in DEX development, outline current challenges, and differentiate optimal models for specific applications. The results and comparative insights can be a reference for web3 developers, blockchain researchers, traders, and regulatory parties.
A niche corner of the Web3 world is increasingly making use of hardware-based Trusted Execution Environments (TEEs) to build decentralized infrastructure. One of the motivations to use TEEs is to go beyond the current performance limitations of cryptography-based alternatives such as zero-knowledge proofs (ZKP), fully homomorphic encryption (FHE), and multi-party computation (MPC). Despite their appealing advantages, current TEEs suffer from serious limitations as they are not secure against physical attacks, and their attestation mechanism is rooted in the chip manufacturer's trust. As a result, Web3 applications have to rely on cloud infrastruture to act as trusted guardians of hardware-based TEEs and have to accept to trust chip manufacturers. This work aims at exploring how we could potentially architect and implement chips that would be secure against physical attacks and would not require putting trust in chip manufacturers. One goal of this work is to motivate the Web3 movement to acknowledge and leverage the substantial amount of relevant hardware research that already exists. In brief, a combination of: (1) physical unclonable functions (PUFs) to secure the root-of-trust; (2) masking and redundancy techniques to secure computations; (3) open source hardware and imaging techniques to verify that a chip matches its expected design; can help move towards attesting that a given TEE can be trusted without the need to trust a cloud provider and a chip manufacturer.
The proxy design pattern allows Ethereum smart contracts to be simultaneously immutable and upgradeable, in which an original contract is split into a proxy contract containing the data storage and a logic contract containing the implementation logic. This architecture is known to have security issues, namely function collisions and storage collisions between the proxy and logic contracts, and has been exploited in real-world incidents to steal users' millions of dollars worth of digital assets. In response to this concern, several previous works have sought to identify proxy contracts in Ethereum and detect their collisions. However, they all fell short due to their limited coverage, often restricting analysis to only contracts with available source code or past transactions. To bridge this gap, we present Proxion, an automated cross-contract analyzer that identifies all proxy smart contracts and their collisions in Ethereum. What sets Proxion apart is its ability to analyze hidden smart contracts that lack both source code and past transactions. Equipped with various techniques to enhance efficiency and accuracy, Proxion outperforms the state-of-the-art tools, notably identifying millions more proxy contracts and thousands of unreported collisions. We apply Proxion to analyze over 36 million alive contracts from 2015 to 2023, revealing that 54.2% of them are proxy contracts, and about 1.5 million contracts exhibit at least one collision issue.
Blockchain is a growing decentralized system built for transparency and immutability. There have been several major attacks on blockchain-based systems, leaving a gap in the trustability of this system. This article presents a comprehensive study of 23 attacks on blockchain systems and categorizes them using a layer-based approach. This approach provides an in-depth analysis of the feasibility and motivation of these attacks. In addition, a framework is proposed that enables a systematic analysis of the impact and interconnection of these attacks, thereby providing a means of identifying potential attack vectors and designing appropriate countermeasures to strengthen any blockchain system.
The large-scale deployment of Solidity smart contracts on the Ethereum mainnet has increasingly attracted financially-motivated attackers in recent years. A few now-infamous attacks in Ethereum's history includes DAO attack in 2016 (50 million dollars lost), Parity Wallet hack in 2017 (146 million dollars locked), Beautychain's token BEC in 2018 (900 million dollars market value fell to 0), and NFT gaming blockchain breach in 2022 ($600 million in Ether stolen). This paper presents a comprehensive investigation of the use of large language models (LLMs) and their capabilities in detecting OWASP Top Ten vulnerabilities in Solidity. We introduce a novel, class-balanced, structured, and labeled dataset named VulSmart, which we use to benchmark and compare the performance of open-source LLMs such as CodeLlama, Llama2, CodeT5 and Falcon, alongside closed-source models like GPT-3.5 Turbo and GPT-4o Mini. Our proposed SmartVD framework is rigorously tested against these models through extensive automated and manual evaluations, utilizing BLEU and ROUGE metrics to assess the effectiveness of vulnerability detection in smart contracts. We also explore three distinct prompting strategies-zero-shot, few-shot, and chain-of-thought-to evaluate the multi-class classification and generative capabilities of the SmartVD framework. Our findings reveal that SmartVD outperforms its open-source counterparts and even exceeds the performance of closed-source base models like GPT-3.5 and GPT-4 Mini. After fine-tuning, the closed-source models, GPT-3.5 Turbo and GPT-4o Mini, achieved remarkable performance with 99% accuracy in detecting vulnerabilities, 94% in identifying their types, and 98% in determining severity. Notably, SmartVD performs best with the `chain-of-thought' prompting technique, whereas the fine-tuned closed-source models excel with the `zero-shot' prompting approach.