Ali RajabiNekoo, Laleh Rasoul, Amirfarhad Farhadi, Azadeh Zamanifar
Traditional methods for identifying impactful liquidity providers (LPs) in Concentrated Liquidity Market Makers (CLMMs) rely on broad measures, such as nominal capital size or surface-level activity, which often lead to inaccurate risk analysis. The SILS framework offers a significantly more detailed approach, characterizing LPs not just as capital holders but as dynamic systemic agents whose actions directly impact market stability. This represents a fundamental paradigm shift from the static, volume-based analysis to a dynamic, impact-focused understanding. This advanced approach uses on-chain event logs and smart contract execution traces to compute Exponential Time-Weighted Liquidity (ETWL) profiles and apply unsupervised anomaly detection. Most importantly, it defines an LP's functional importance through the Liquidity Stability Impact Score (LSIS), a counterfactual metric that measures the potential degradation of the market if the LP withdraws. This combined approach provides a more detailed and realistic characterization of an LP's impact, moving beyond the binary and often misleading classifications used by existing methods. This impact-focused and comprehensive approach enables SILS to accurately identify high-impact LPs-including those missed by traditional methods and supports essential applications like a protective oracle layer and actionable trader signals, thereby significantly enhancing DeFi ecosystem. The framework provides unprecedented transparency into the underlying liquidity structure and associated risks, effectively reducing the common false positives and uncovering critical false negatives found in traditional models. Therefore, SILS provides an effective mechanism for proactive risk management, transforming how DeFi protocols safeguard their ecosystems against asymmetric liquidity behavior.
The rapid integration of blockchain, cryptocurrency, and Web3 technologies into digital banks and fintech operations has created an integrated environment blending traditional financial systems with decentralised elements. This paper introduces the CryptoNeo Threat Modelling Framework (CNTMF), a proposed framework designed to address the risks in these ecosystems, such as oracle manipulation and cross-chain exploits. CNTMF represents a proposed extension of established methodologies like STRIDE, OWASP Top 10, NIST frameworks, LINDDUN, and PASTA, while incorporating tailored components including Hybrid Layer Analysis, the CRYPTOQ mnemonic for cryptocurrency-specific risks, and an AI-Augmented Feedback Loop. Drawing on real-world data from 2025 incidents, CNTMF supports data-driven mitigation to reduce losses, which totalled approximately $2.47 billion in the first half of 2025 across 344 security events (CertiK via GlobeNewswire, 2025; Infosecurity Magazine, 2025). Its phases guide asset mapping, risk profiling, prioritisation, mitigation, and iterative feedback. This supports security against evolving risks like state-sponsored attacks.
Aufa Nasywa Rahman, Bimo Sunarfri Hantono, Guntur Dharma Putra
Open banking framework enables third party providers to access financial data across banking institutions, leading to unprecedented innovations in the financial sector. However, some open banking standards remain susceptible to severe technological risks, including unverified data sources, inconsistent data integrity, and lack of immutability. In this paper, we propose a layered architecture that provides assurance in data trustworthiness with three distinct levels of trust, covering source validation, data-level authentication, and tamper-proof storage. The first layer guarantees the source legitimacy using decentralized identity and verifiable presentation, while the second layer verifies data authenticity and consistency using cryptographic signing. Lastly, the third layer guarantees data immutability through the Tangle, a directed acyclic graph distributed ledger. We implemented a proof-of-concept implementation of our solution to evaluate its performance, where the results demonstrate that the system scales linearly with a stable throughput, exhibits a 100% validation rate, and utilizes under 35% of CPU and 350 MiB memory. Compared to a real-world open banking implementation, our solution offers significantly reduced latency and stronger data integrity assurance. Overall, our solution offers a practical and efficient system for secure data sharing in financial ecosystems while maintaining regulatory compliance.
The emergence of quantum computing presents profound challenges to existing cryptographic infrastructures, whilst the development of central bank digital currencies (CBDCs) has raised concerns regarding privacy preservation and excessive centralisation in digital payment systems. This paper proposes the Quantum-Resilient Privacy Ledger (QRPL) as an innovative token-based digital currency architecture that incorporates National Institute of Standards and Technology (NIST)-standardised post-quantum cryptography (PQC) with hash-based zero-knowledge proofs to ensure user sovereignty, scalability, and transaction confidentiality. Key contributions include adaptations of ephemeral proof chains for unlinkable transactions, a privacy-weighted Proof-of-Stake (PoS) consensus to promote equitable participation, and a novel zero-knowledge proof-based mechanism for privacy-preserving selective disclosure. QRPL aims to address critical shortcomings in prevailing CBDC designs, including risks of pervasive surveillance, with a 10-20 second block time to balance security and throughput in future monetary systems. While conceptual, empirical prototypes are planned. Future work includes prototype development to validate these models empirically.
With the advancement of digital payment technologies, central banks worldwide have increasingly begun to explore the implementation of Central Bank Digital Currencies (CBDCs). This paper presents a comprehensive review of the latest developments in CBDC system design and implementation. By analyzing 135 research papers published between 2018 and 2025, the study provides an in-depth examination of CBDC design taxonomy and ecosystem frameworks. Grounded in the CBDC Design Pyramid, the paper refines and expands key architectural elements by thoroughly investigating innovations in ledger technologies, the selection of consensus mechanisms, and challenges associated with offline payments and digital wallet integration. Furthermore, it conceptualizes a CBDC ecosystem. A detailed comparative analysis of 26 existing CBDC systems is conducted across four dimensions: system architecture, ledger technology, access model, and application domain. The findings reveal that the most common configuration consists of a two-tier architecture, distributed ledger technology (DLT), and a token-based access model. However, no dominant trend has emerged regarding application domains. Notably, recent research shows a growing focus on leveraging CBDCs for cross-border payments to resolve inefficiencies and structural delays in current systems. Finally, the paper offers several forward-looking recommendations for future research.
Non-Fungible Tokens (NFTs) offer a promising mechanism to protect Australian and Indigenous artists' copyright. They represent and transfer the value of artwork in digital form. Before adopting NFTs to protect Australian artwork, we in this paper investigate them empericially. We focus on examining the details of NFT structure. We start from the underlying structure of NFTs to show how they represent copyright for both artists and production owners, as well as how they aim to safeguard or secure the value of digital artworks. We then involve data collection from various types of sources with different storage methods, including on-chain, centralized, and decentralized systems. Based on both metadata and artwork content, we present our analysis and discussion on the following key issues: copyright, security and artist identification. The final results of the evaluation, unfortnately, show that the NFT is NOT ready to protect Australian and Indigenous artists' copyright.
Rugsafe introduces a comprehensive protocol aimed at mitigating the risks of rug pulls in the cryptocurrency ecosystem. By utilizing cryptographic security measures and economic incentives, the protocol provides a secure multichain system for recovering assets and transforming rugged tokens into opportunities and rewards. Foundational to Rugsafe are specialized vaults where rugged tokens can be securely deposited, and anticoin tokens are issued as receipts. These anticoins are designed to be inversely pegged to the price movement of the underlying rugged token. Users can utilize these anticoins within the ecosystem or choose to burn them, further securing the protocol and earning additional rewards. The supply of the native Rugsafe token is dynamically adjusted based on the volume, value, and activity of rugged tokens, ensuring stability and resilience. By depositing rugged tokens into a vault on several chains, and by burning anticoins, users receive incentives on the RugSafe chain. This protocol's vaults are designed to work in heterogenous blockchain ecosystems, offering a practical and effective solution to one of the most significant challenges in the cryptocurrency market.
Blockchain bridges have become essential infrastructure for enabling interoperability across different blockchain networks, with more than $24B monthly bridge transaction volume. However, their growing adoption has been accompanied by a disproportionate rise in security breaches, making them the single largest source of financial loss in Web3. For cross-chain ecosystems to be robust and sustainable, it is essential to understand and address these vulnerabilities. In this study, we present a comprehensive systematization of blockchain bridge design and security. We define three bridge security priors, formalize the architectural structure of 13 prominent bridges, and identify 23 attack vectors grounded in real-world blockchain exploits. Using this foundation, we evaluate 43 representative attack scenarios and introduce a layered threat model that captures security failures across source chain, off-chain, and destination chain components. Our analysis at the static code and transaction network levels reveals recurring design flaws, particularly in access control, validator trust assumptions, and verification logic, and identifies key patterns in adversarial behavior based on transaction-level traces. To support future development, we propose a decision framework for bridge architecture design, along with defense mechanisms such as layered validation and circuit breakers. This work provides a data-driven foundation for evaluating bridge security and lays the groundwork for standardizing resilient cross-chain infrastructure.
This work presents a novel decentralized protocol for digital estate planning that integrates advances distributed computing, and cryptography. The original proof-of-concept was constructed using purely solidity contracts. Since then, we have enhanced the implementation into a layer-1 protocol that uses modern interchain communication to connect several heterogeneous chain types. A key contribution of this research is the implementation of several modern cryptographic primitives to support various forms of claims for information validation. These primitives introduce an unmatched level of privacy to the process of digital inheritance. We also demonstrate on a set of heterogeneous smart contracts, following the same spec, on each chain to serve as entry points, gateways, or bridge contracts that are invoked via a path from the will module on our protocol, to the contract. This ensures a fair and secure distribution of digital assets in accordance with the wishes of the decedent without the requirement of moving their funds. This research further extends its innovations with a user interaction model, featuring a check-in system and account abstraction process, which enhances flexibility and user-friendliness without compromising on security. By developing a dedicated permissionless blockchain that is secured by a network of validators, and interchain relayers, the proposed protocol signifies a transformation in the digital estate planning industry and illustrates the potential of blockchain technology in revolutionizing traditional legal and personal spheres. Implementing a cryptoeconomic network at the core of inheritance planning allows for unique incentive compatible economic mechanisms to be constructed.
Azmat Ullah, Maria Ilaria Lunesu, Lodovica Marchesi, Roberto Tonelli
This paper presents a blockchain-based Internet of Things (IoT) system for monitoring pizza production in restaurants. IoT devices track temperature and humidity in real-time, while blockchain ensures secure and tamper-proof data. A Raspberry Pi processes sensor data, captures images, triggers alerts, and interacts with smart contracts. The system detects abnormal conditions, enabling quick responses. Blockchain adds transparency and traceability, supporting compliance and audits. Experiments show improved ingredient management, reduced waste, and increased kitchen efficiency.
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.
Non Fungible Tokens have changed digital ownership and how creators earn money. Between 2021 and 2024, the market value exceeded 40 billion. However, the fast growth of the NFT ecosystem has revealed serious issues in managing intellectual property rights. There is a lot of confusion about the difference between owning an NFT and owning the copyright for the underlying content. This research looks at the gap between traditional copyright laws and blockchain-based transactions. We use a mixed methods approach to analyze this disconnect. We create a new IP rights matrix that clearly shows how copyright law relates to NFT ownership structures. Additionally, we include a business model taxonomy that sorts new commercial applications by their IP risk and sustainability factors. By examining important legal cases, smart contracts, and interviews with stakeholders, we find key problems in enforcing laws across different regions, standardizing licenses, and assessing business opportunities.
This study investigates the impact of data source diversity on the performance of cryptocurrency forecasting models by integrating various data categories, including technical indicators, on-chain metrics, sentiment and interest metrics, traditional market indices, and macroeconomic indicators. We introduce the Crypto100 index, representing the top 100 cryptocurrencies by market capitalization, and propose a novel feature reduction algorithm to identify the most impactful and resilient features from diverse data sources. Our comprehensive experiments demonstrate that data source diversity significantly enhances the predictive performance of forecasting models across different time horizons. Key findings include the paramount importance of on-chain metrics for both short-term and long-term predictions, the growing relevance of traditional market indices and macroeconomic indicators for longer-term forecasts, and substantial improvements in model accuracy when diverse data sources are utilized. These insights help demystify the short-term and long-term driving factors of the cryptocurrency market and lay the groundwork for developing more accurate and resilient forecasting models.
This paper presents a formal analysis of the Lightning Network as a monetary system structurally diverging from Bitcoin's base-layer settlement model. We demonstrate that under increasing transaction demand, BTC transaction fees rise superlinearly due to throughput constraints, while Lightning Network routing costs approach a bounded asymptote. Using mathematical modeling, game-theoretic proofs, and complexity analysis, we show that Lightning enables indefinite off-chain operation via the emergence of liquidity hub oligopolies. These hubs exhibit properties of unregulated financial intermediaries, including rent extraction, opacity, and systemic fragility. Strategic agent models show that channel closure becomes economically infeasible, and routing problems approach hardness limits in P-Space complexity. We conclude that Lightning does not merely extend Bitcoin, but constitutes a synthetic financial system with shadowbank characteristics, lacking reserve discipline, transparency, or enforceable settlement guarantees.
Dumitrel Loghin, Shuang Liang, S. Liu, Xiong Liu · 6 authors
Zero-knowledge proofs (ZKP) are becoming a gold standard in scaling blockchains and bringing Web3 to life. At the same time, ZKP for transactions running on the Ethereum Virtual Machine require powerful servers with hundreds of CPU cores. The current zkProver implementation from Polygon is optimized for x86-64 CPUs by vectorizing key operations, such as Merkle tree building with Poseidon hashes over the Goldilocks field, with Advanced Vector Extensions (AVX and AVX512). With these optimizations, a ZKP for a batch of transactions is generated in less than two minutes. With the advent of cloud servers with ARM which are at least 10% cheaper than x86-64 servers and the implementation of ARM Scalable Vector Extension (SVE), we wonder if ARM servers can take over their x86-64 counterparts. Unfortunately, our analysis shows that current ARM CPUs are not a match for their x86-64 competitors. Graviton4 from Amazon Web Services (AWS) and Axion from Google Cloud Platform (GCP) are 1.6X and 1.4X slower compared to the latest AMD EPYC and Intel Xeon servers from AWS with AVX and AVX512, respectively, when building a Merkle tree with over four million leaves. This low performance is due to (1) smaller vector size in these ARM CPUs (128 bits versus 512 bits in AVX512) and (2) lower clock frequency. On the other hand, ARM SVE/SVE2 Instruction Set Architecture (ISA) is at least as powerful as AVX/AVX512 but more flexible. Moreover, we estimate that increasing the vector size to 512 bits will enable higher performance in ARM CPUs compared to their x86-64 counterparts while maintaining their price advantage.
This study addresses critical challenges in managing the transportation of spent nuclear fuel, including inadequate data transparency, stringent confidentiality requirements, and a lack of trust among collaborating parties, issues prevalent in traditional centralized management systems. Given the high risks involved, balancing data confidentiality with regulatory transparency is imperative. To overcome these limitations, a prototype system integrating blockchain technology and the Internet of Things (IoT) is proposed, featuring a multi-tiered consortium chain architecture. This system utilizes IoT sensors for real-time data collection, which is immutably recorded on the blockchain, while a hierarchical data structure (operational, supervisory, and public layers) manages access for diverse stakeholders. The results demonstrate that this approach significantly enhances data immutability, enables real-time multi-sensor data integration, improves decentralized transparency, and increases resilience compared to traditional systems. Ultimately, this blockchain-IoT framework improves the safety, transparency, and efficiency of spent fuel transportation, effectively resolving the conflict between confidentiality and transparency in nuclear data management and offering significant practical implications.
Large Language Models (LLMs) are being used more and more for various coding tasks, including to help coders identify bugs and are a promising avenue to support coders in various tasks including vulnerability detection -- particularly given the flexibility of such generative AI models and tools. Yet for many tasks it may not be suitable to use LLMs, for which it may be more suitable to use smaller language models that can fit and easily execute and train on a developer's computer. In this paper we explore and evaluate whether smaller language models can be fine-tuned to achieve reasonable results for a niche area: vulnerability detection -- specifically focusing on detecting the reentrancy bug in Solidity smart contracts.
Ali Rahimi, Babak H. Khalaj, Mohammad Ali Maddah-Ali
Verifiable computing (VC) has gained prominence in decentralized machine learning systems, where resource-intensive tasks like deep neural network (DNN) inference are offloaded to external participants due to blockchain limitations. This creates a need to verify the correctness of outsourced computations without re-execution. We propose \texttt{Range-Arithmetic}, a novel framework for efficient and verifiable DNN inference that transforms non-arithmetic operations, such as rounding after fixed-point matrix multiplication and ReLU, into arithmetic steps verifiable using sum-check protocols and concatenated range proofs. Our approach avoids the complexity of Boolean encoding, high-degree polynomials, and large lookup tables while remaining compatible with finite-field-based proof systems. Experimental results show that our method not only matches the performance of existing approaches, but also reduces the computational cost of verifying the results, the computational effort required from the untrusted party performing the DNN inference, and the communication overhead between the two sides.
Pietro Saggese, Michael Fröwis, Stefan Kitzler, Bernhard Haslhofer · 5 authors
Total Value Locked (TVL) aims to measure the aggregate value of cryptoassets deposited in Decentralized Finance (DeFi) protocols. Although blockchain data is public, the way TVL is computed is not well understood. In practice, its calculation on major TVL aggregators relies on self-reports from community members and lacks standardization, making it difficult to verify published figures independently. We thus conduct a systematic study on 939 DeFi projects deployed in Ethereum. We study the methodologies used to compute TVL, examine factors hindering verifiability, and ultimately propose standardization attempts in the field. We find that 10.5% of the protocols rely on external servers; 68 methods alternative to standard balance queries exist, although their use decreased over time; and 240 equal balance queries are repeated on multiple protocols. These findings indicate limits to verifiability and transparency. We thus introduce ``verifiable Total Value Locked'' (vTVL), a metric measuring the TVL that can be verified relying solely on on-chain data and standard balance queries. A case study on 400 protocols shows that our estimations align with published figures for 46.5% of protocols. Informed by these findings, we discuss design guidelines that could facilitate a more verifiable, standardized, and explainable TVL computation.
Blockchain technology has emerged as one of the most transformative digital innovations of the 21st century. This paper presents a comprehensive review of blockchain's fundamental architecture, tracing its development from Bitcoin's initial implementation to current enterprise applications. We examine the core technical components including distributed consensus algorithms, cryptographic principles, and smart contract functionality that enable blockchain's unique properties. The historical progression from cryptocurrency-focused systems to robust platforms for decentralized applications is analyzed, highlighting pivotal developments in scalability, privacy, and interoperability. Additionally, we identify critical challenges facing widespread blockchain adoption, including technical limitations, regulatory hurdles, and integration complexities with existing systems. By providing this foundational understanding of blockchain technology, this paper contributes to ongoing research efforts addressing blockchain's potential to revolutionize data management across industries.
Eddie de Paula, Niel Bunda, Hezerul Abdul Karim, Nouar AlDahoul · 5 authors
This paper examines how decentralized energy systems can be enhanced using collaborative Edge Artificial Intelligence. Decentralized grids use local renewable sources to reduce transmission losses and improve energy security. Edge AI enables real-time, privacy-preserving data processing at the network edge. Techniques such as federated learning and distributed control improve demand response, equipment maintenance, and energy optimization. The paper discusses key challenges including data privacy, scalability, and interoperability, and suggests solutions such as blockchain integration and adaptive architectures. Examples from virtual power plants and smart grids highlight the potential of these technologies. The paper calls for increased investment, policy support, and collaboration to advance sustainable energy systems.
Parwat Singh Anjana, Srivatsan Ravi, Herlihy, Maurice
This paper presents a comprehensive analysis of historical data across two popular blockchain networks: Ethereum and Solana. Our study focuses on two key aspects: transaction conflicts and the maximum theoretical parallelism within historical blocks. We aim to quantify the degree of transaction parallelism and assess how effectively it can be exploited by systematically examining block-level characteristics, both within individual blocks and across different historical periods. In particular, this study is the first of its kind to leverage historical transactional workloads to evaluate conflict patterns. By offering a structured approach to analyzing these conflicts, our research provides valuable insights and an empirical basis for developing more efficient parallel execution techniques for smart contracts in the Ethereum and Solana. Our empirical analysis reveals that historical Ethereum blocks frequently achieve high independence, with over 50\% independent transactions in more than 50\% of blocks, while, on average, Solana blocks contain longer conflict chains $\sim$58\%, compared to $\sim$18\% in Ethereum, reflecting fundamentally different parallel execution dynamics.
Blockchain technology has rapidly expanded beyond its original use in cryptocurrencies to a broad range of applications, creating vast amounts of immutable, decentralized data. As blockchain adoption grows, so does the need for advanced data analytics techniques to extract insights for business intelligence, fraud detection, financial analysis and many more. While previous research has examined specific aspects of blockchain data analytics, such as transaction patterns, illegal activity detection, and data management, there remains a lack of comprehensive reviews that explore the full scope of blockchain data analytics. This study addresses this gap through a scoping literature review, systematically mapping the existing research landscape, identifying key topics, and highlighting emerging trends. Using established methodologies for literature reviews, we analyze 470 publications, clustering them into six major research themes: illegal activity detection, data management, financial analysis, user analysis, community detection, and mining analysis. Our findings reveal a strong focus on detecting illicit activities and financial applications, while holistic business intelligence use cases remain underexplored. This review provides a structured overview of blockchain data analytics, identifying research gaps and proposing future directions to enhance the field's impact.
The Massive Internet of Things (MIoT) envisions an interconnected ecosystem of billions of devices, fundamentally transforming diverse sectors such as healthcare, smart cities, transportation, agriculture, and energy management. However, the vast scale of MIoT introduces significant challenges, including network scalability, efficient data management, energy conservation, and robust security mechanisms. This paper presents a thorough review of existing and emerging MIoT technologies designed to address these challenges, including Low-Power Wide-Area Networks (LPWAN), 5G/6G capabilities, edge and fog computing architectures, and hybrid access methodologies. We further investigate advanced strategies such as AI-driven resource allocation, federated learning for privacy-preserving analytics, and decentralized security frameworks using blockchain. Additionally, we analyze sustainable practices, emphasizing energy harvesting and integrating green technologies to reduce environmental impact. Through extensive comparative analysis, this study identifies critical innovations and architectural adaptations required to support efficient, resilient, and scalable MIoT deployments. Key insights include the role of network slicing and intelligent resource management for scalability, adaptive protocols for real-time data handling, and lightweight AI models suited to the constraints of MIoT devices. This research ultimately contributes to a deeper understanding of how MIoT systems can evolve to meet the growing demand for seamless, reliable connectivity while prioritizing sustainability, security, and performance across diverse applications. Our findings serve as a roadmap for future advancements, underscoring the potential of MIoT to support a globally interconnected, intelligent infrastructure.