Blockchain Papers

Anam Ahsan, Malahim Nadeem

No abstract is available for this record.

Kangyin Dong, Wenchao Wang, Yang Liu, Farhad Taghizadeh-Hesary

No abstract is available for this record.

G. Vennila, Tatireddy Nandeeswar Reddy, Damarla Pavan, Nallapalem Vinithashri · 5 authors

No abstract is available for this record.

Yue Pan, Xiao-Yang Liu

No abstract is available for this record.

László Papp

This paper argues that trust scores — from credit ratings and ESG scores to AI-generated trust metrics — fail not because of poor implementation, but because trust itself is the wrong abstraction. Trust is not a scalar quantity but a contextual, relational, and topological phenomenon. Any attempt to reduce it to a universal numerical score leads to fragility, manipulation, exclusion, and systemic failure. We identify five structural failure modes (context collapse, Goodhart's Law, epistemic centralization, irreversibility, and metric substitution for truth), supported by historical case studies (Enron, Wirecard, Volkswagen Dieselgate, the 2008 subprime crisis, ESG rating failures). A formal impossibility argument demonstrates that no universal trust score can simultaneously satisfy context independence, temporal stability, observer neutrality, and manipulation resistance. We propose proof-based systems as the alternative paradigm, where trust is not measured but rendered unnecessary through local, irreversible verification. Examples include Bitcoin Proof-of-Work, zero-knowledge proofs, and blockchain-based supply chain traceability.

Michael Crespo

Decentralization has become a buzzword for blockchain and other decentralized technologies. This study explores the link between a system's decentralized/centralized nature and its impact on people's trust in those systems across Governance & Politics, Economics & Finance, and Technology & Information. An analysis of responses from 154 respondents to a questionnaire found that decentralization does result in increased trust when examined idealistically. Governance was one key area where decentralization had a major impact. However, when explored more practically, decentralization was not consistently trusted more relative to some centralized systems. In addition, factors such as a system's history and understandability were found to be more significant than its decentralization for trust.

Xianzheng Meng, Hongli Dong, Yuxuan Shen

No abstract is available for this record.

Dawei Song, Yuheng Zhang

To address the challenges of topological obscurity and extreme label sparsity in large-scale Ethereum transaction networks, a novel self-supervised phishing detection framework named Eth-GBAV is proposed, integrating graph attention, broad learning, and adversarial variational inference. The framework initiates with a biased random walk strategy guided by transaction intensity and temporal dynamics to capture the initial behavioral semantics of nodes. To distill discriminative features from noisy backgrounds, a “Generative-Attention” encoding architecture is constructed, where a graph attention network aggregates weighted structural neighborhoods and a Variational Autoencoder (VAE) characterizes the underlying probability distribution of legitimate transaction patterns. By maximizing the evidence lower bound, anomalous accounts are effectively isolated through reconstruction residuals. Furthermore, the broad learning system is introduced as an efficient analytical decision layer. By mapping VAE-derived latent embeddings and reconstruction errors into an expanded high-dimensional feature space, the framework captures intricate behavioral correlations via mapping and enhancement neurons. Extensive experimental verification on two large-scale datasets demonstrates the superior performance of Eth-GBAV. On the XBlock dataset, it achieves a leading F1-score of 0.9847 and a recall of 0.9839, outperforming the most competitive state-of-the-art model by significant margins. On the Kaggle dataset, the framework maintains high robustness with an accuracy of 0.9592 and an F1-score of 0.9069.

Tina Yi Jin Hsieh, Carl Eriksson, Garth Meckler, Matthew Hansen · 12 authors

Introduction and Objective: Traditional adverse safety events (ASE) identification relies on domain experts to manually review and annotate charts, which hinders the scalability of processing high-volume EMS data. This study explores the use of large language model (LLM) with a knowledge base to automate extraction of adverse safety events (ASE) from unstructured emergency medical service (EMS) notes for pediatric out-of-hospital cardiac arrest (OHCA) as proof of concept. Data Sources and Study Design: Pediatric OHCA records from a national EMS provider were obtained from 2017 to 2020. Leveraging the Pediatric Prehospital Adverse Safety Event Detection System (PEDS) as a foundational knowledge base, we used the LinkML framework to develop an ontology to define ASEs across six essential EMS care domains. To convert unstructured EMS narratives into structured prompts, we used the Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES) method, which generated schema-driven prompts to guide the GPT-3.5 model in identifying ASEs. By mapping unstructured data into structured concepts consistent with PEDS guidelines, the model produced targeted prompts that supported effective entity extraction. Results: We evaluated framework effectiveness with accuracy, recall, precision, F1 score, and specificity across 42 pediatric OHCA cases covering ASE-related entities. RescueGPT showed high accuracy in detecting common ASEs (Patient Rhythm, Age, Weight, Length) but revealed challenges in rare events (Failure to Establish IV Access, Incorrect Airway Equipment Size, Failure to Ventilate Patient) likely due to more inconsistent and complex documentation. Conclusions: RescueGPT demonstrates potential in scaling automated ASE detection, but performance varies by completeness and clarity of EMS narrative, particularly with rare events. Fragmented clinical documentation limits accuracy and highlights the need for standardized collection protocols in EMS systems. Future directions will focus on implementing rebalancing strategies for rare events, applying explainability methods to improve decision-making transparency, and refining text segmentation techniques to handle mixed outcomes to further improve performance.

Wonbin Seo, Jae‐Hyeon Ahn

No abstract is available for this record.

Denis Mutua

Smart contracts are a core component of blockchain-based systems, enabling decentralized applications to autonomously manage assets and enforce program logic. However, vulnerabilities in smart contracts can cause severe financial losses because of their immutability and public accessibility. As a result, analyzing common attack vectors and evaluating static detection techniques before deployment remain critical challenges in blockchain security. This thesis presents an analytical study of high-impact smart contract attack classes and evaluates the effectiveness of AST-based static detection approaches for Ethereum smart contracts. Building on the Aderyn static analysis framework, custom detectors are implemented to analyze structural code patterns that enable reentrancy attacks, authorization bypass vulnerabilities, and unsafe proxy delegation and storage collision risks. Rather than proposing new defensive mechanisms, this work systematically analyzes how known attacks arise from insecure smart contract programming practices and examines how defensive coding patterns can be identified at the source-code level through static analysis. The effectiveness of the analysis is evaluated using vulnerable smart contract implementations and corresponding exploit scenarios developed with the Foundry testing framework. Experimental results show that the implemented detectors successfully identify exploitable vulnerabilities and demonstrate a strong correspondence between attack-enabling code structures and statically detectable patterns, confirming alignment between static analysis findings and real-world attack behavior. This work demonstrates that extensible AST-based static analysis provides a practical foundation for analyzing both smart contract attacks and the defensive patterns intended to mitigate them before deployment.

Pascal Berrang, Mirco Giacobbe, Jacob Swales, Xiao Yang

We introduce a technology to formally verify that a software system satisfies a temporal specification of functional correctness, without revealing the system itself. Our method combines a deductive approach to model checking to obtain a formal certificate of correctness for the system, with zero-knowledge proofs to convince an external verifier that the system -- kept secret -- complies with its specification of correctness -- made public. We consider proof certificates represented as ranking functions, and introduce both an explicit-state and a symbolic scheme for model checking in zero knowledge. Our explicit-state scheme assumes systems represented as transition graphs. We use polynomial commitments to convince the verifier that the public proof certificates correspond to the secret transition relation. Our symbolic scheme assumes systems specified as linear guarded commands and uses piecewise-linear ranking functions. We apply Farkas' lemma to obtain a witness for the validity of the ranking function with public and secret components, and employ sigma protocols for matrix multiplication and range proofs to convince the verifier of the witness's existence. We built a prototype to demonstrate the practical efficacy of our two schemes on linear temporal logic verification examples. Our technology enables formal verification in domains where both the safety and the confidentiality of the system under analysis are critical.

Zibin Zheng, Ruize Ye, Lei Xiao, Xiyue Zhou · 5 authors

No abstract is available for this record.

Seonghyeon Kim, Hayden Wimmer, Jongyeop Kim

No abstract is available for this record.

Ding Ding, Yang Li, Poh Ling Neo, Zhiyuan Wang · 5 authors

This paper develops a tractable theoretical framework to study how network participation shapes the boom–bust dynamics of non-fungible token (NFT) prices. We model NFT pricing under network effects and heterogeneous consumers, and show that prices and participation are jointly determined in equilibrium. The model implies a critical participation threshold that separates expansion from contraction regimes: above this threshold, positive feedback between participation and valuation generates self-reinforcing growth, while below it, weakening network benefits lead to contraction. We provide empirical evidence using data from the aggregate NFT market and prominent collections including Bored Ape Yacht Club (BAYC) and CryptoPunks. Reduced-form regressions show a positive association between prices and network participation, with stronger effects at the collection level than in the aggregate market. Threshold estimation further provides evidence consistent with regime-dependent dynamics, with clearer tipping behaviour in well-defined NFT communities than in the aggregate market. These findings suggest that NFT valuation is closely tied to network structure and participation dynamics. More broadly, this paper contributes a unified framework that links participation, price formation, and threshold behaviour in NFT markets.

Mbark Abouessaouab, Youssef Ait Abi, Anass Khannous, Said Bouchkaren

No abstract is available for this record.

B. Arulmozhi, J. I. Sheeba, S. Pradeep Devaneyan

Electronic Healthcare Records (EHRs) provide distributed access to patient and doctor information through pervasive cloud-based storage. As this data is highly sensitive, robust privacy measures are essential to mitigate adversarial impacts. To ensure optimal privacy across multiple shared EHRs, this article proposes a Session-dependent Zero Knowledge Proof Technique (SZKPT). The framework identifies privacy breaches using two truth values: the first representing optimal session closure, and the second reflecting verification at each sharing instance. Both truth values are validated through iterated session validations, which are managed using a deep learning paradigm. During training, different combinations of truth values are employed to maximize privacy during data sharing, while iterative processes train consecutive validation instances to improve breach detection. Truth values are continuously updated to reflect the session closure and the most recent privacy verification. In practice, if either truth value equals zero, the session is suspended; otherwise, if truth values are valid in consecutive iterations, data sharing is delegated to the authorized user. The process is repeatted at regular intervals with updated truth values, ensuring continuous monitoring and adaptive privacy protection. The proposed technique is rigorously evaluated using key performance metrics, including access verification, computational complexity, privacy breach detection, verification time, and access delegation time. Results demonstrate that SZKPT effectively balances privacy preservation with usability, providing a reliable, scalable, and efficient solution for secure EHR management in cloud-based healthcare systems.

Rajwinder Kaur

The process of efficiently meeting customer needs through the seamless transfer of goods, services, and information is known as supply chain management. Because of centralized control, trustless networks, and occasionally manual processes, modern supply chain management solutions lack traceability, transparency, security, and decentralization. As blockchain technology is decentralized and immutable, it provides a solution that ensures transactions are transparent and viewable in real time. Due to traditional proof-based algorithms like Proof of Work (PoW) and Proof of Stake (PoS) or other voting-based algorithms, the Blockchain utilized for traditional supply chain management (SCM) has issues with high energy consumption, scalability, and centralization dangers. In this study, we will offer an optimal Blockchain-based model that evaluates nodes based on multiple parameters, including processing power, network latency, uptime, and reputation, using a consensus mechanism based on a weighted leader selection technique. By dynamically assigning weights to these factors, this study can overcome the limitations of single factor leader selection and guarantee validator selection that is efficient, adaptable, fair and sustainable. This study also examines the concept to demonstrate how it might support decentralization in modern supply chain management (SCM) systems while enhancing traceability, security, and transparency through more efficient use of resources.

Maitri Hingu, Dr. Kamlendu Pandey

Blockchain systems rely on architectural design choices and consensus protocols to establish decentralized trust in distributed environments. This paper presents a focused survey of blockchain architecture and protocol evolution, emphasizing structural components, peer-to-peer networking, consensus mechanisms, forking models, and security-scalability trade-offs. Core elements such as blocks, cryptographic hashing, distributed ledgers, node roles, transaction propagation, and validation processes are examined to explain how integrity and immutability are maintained. Major consensus mechanisms, including Proof of Work (PoW), Proof of Stake (PoS), Practical Byzantine Fault Tolerance (PBFT), and Proof of Authority (PoA), are comparatively analyzed with respect to decentralization, throughput, finality, energy consumption, and deployment context. The paper also examines blockchain forking as a mechanism for protocol evolution and governance. By distinguishing protocol-level concerns from application-level adoption, this survey provides a technical foundation for evaluating blockchain systems and identifies open challenges in scalability, interoperability, governance, privacy, and sustainable consensus design.

Theodor-Nicolae Carp

The present interdisciplinary research article is also available on ResearchGate.net, at: https://www.researchgate.net/publication/397547485_Andromeda_Stellar_Dromellar_A_Clean_Water-Regenerating_AI-Driven_Digital_Currency_Symbolizing_Humanity's_Next_Archetype<b>Abstract:</b>The convergence of blockchain technology and artificial intelligence (AI) offers unprecedented opportunities to redefine digital value, representation and cultural meaning. We introduce Andromeda Stellar (nicknamed Dromellar), a clean water-regenerating, AI-driven digital currency designed not solely as a medium of exchange but as a collectible artifact and a conceptual, philosophical statement reflecting humanity’s next evolutionary archetype. Each Stellar coin integrates multifaceted symbolic and aesthetic elements: the lion emblem, representing courage and humility refined through life’s trials; gold coloration, symbolizing purity, transcendence, and the refinement of human potential; constellations, reflecting the reconnection of isolated human “stars” into a unified cosmic wholeness; the Morning Star, a prophetic, Messianic and First-Anointed figure embodying transformation through cycles of death and resurrection; and the Milky Way–Andromeda cosmic fire, signifying passion, positive change, healing, and restoration. The inscription Homo constellatus explicitly denotes the envisioned evolutionary archetype of humanity, uniting individual growth with collective aspiration. Framed through a hydrological imperative, Stellar reconfigures value as a flowing river of cosmic liquidity, where AI acts as the dynamic current – circulating, regenerating, and irrigating meaning across an evolving economic basin. This dual motif evokes the Amazon's tropical vitality for generative abundance and the Nile's unyielding traversal of the Sahara – the world's largest desert – for resilient endurance, symbolizing how Stellar sustains poverty-free global wealth as a reserve currency, akin to life-saving electric power amid utmost trials and tribulations. This metaphor bridges systems engineering (cybernetic feedback loops ensuring equilibrium) and digital humanities (performative materiality encoding archetypes in code), while confronting the ecological paradox of AI's resource consumption. To reconcile AI’s material footprint with its metaphor of flow, Stellar incorporates a closed-loop water-recycling architecture that achieves full reclamation of process water with no chemical effluent – via an eight-stage, chemical-free cascade of thermal recovery, mechanical filtration, adsorptive organics removal, membrane desalination via NF-ED hybrids, UV disinfection and AI-optimized remineralization, yielding potable-grade output (TDS &lt;50 ppm, pathogen-free). This operational covenant transforms the hydrological metaphor into measurable sustainability, aligning the system with EU Green Deal and UN SDG frameworks.In 2025, as AI data centers alone demand 193–297 billion gallons (731–1,125 million cubic meters) of water annually – equivalent to the household usage of 6–10 million Americans – with individual facilities guzzling up to 5 million gallons daily – Stellar's ethical hydrology embeds mitigations like tokenized water credits to balance renewal with restraint, mirroring the Nile's silt-rich floods that historically greened arid expanses for economic rebirth despite scarcity crises. Amid SDG 6's stalled progress – where only 35% of targets show moderate advancement and 2.2 billion people still lack safe water, per the UN's November 2025 Sustainable Development Goals Report – Stellar advances regenerative AI-blockchain via initiatives like Nexchain's green Web3 for energy-efficient fusion and UNDP's FLock Accelerator for decentralized sustainability in vulnerable regions, ensuring self-feeding cycles that propel SDG 13 (Climate Action) and SDG 17 (Partnerships). Feasibility is evidenced by 2025 pilots, such as Microsoft's zero-water datacenter designs using liquid cooling and non-evaporative systems, now scaling across U.S. superclusters with near-zero consumption. Stellar introduces a Gaian reciprocity model, wherein AI-driven computations feed a closed-loop water system that tokenizes excess as Aqua Relics, funding real-world water projects and embedding planetary hydration into the digital economy. By reclaiming up to 95% of process water per cycle, Stellar operationalizes SDG 6, 13, and 17, combining decentralized ledger verification, AI-generated artifacts, and tokenized sustainability incentives. Unlike conventional cryptocurrencies, Stellar integrates artistic, symbolic, and ecological dimensions, creating a socially, culturally, and environmentally responsible framework for digital value." Technically, Stellar employs the ERC721 token standard to ensure each coin is unique, verifiable, and programmatically extensible, with AI-generated visual assets hosted on a Node.js backend. Coins incorporate algorithmically generated SVG representations featuring the lion, cosmic motifs, and variable color gradients, resulting in unique, collectible artifacts. A React-based frontend enables wallet connectivity, interactive minting, and visualization of token-specific symbolic elements. Stellar thus functions simultaneously as a blockchain prototype, AI-driven artistic system, and philosophical instrument. Beyond technical implementation, Stellar exemplifies how digital currency can transcend transactional utility, embedding symbolism, cultural narrative, and cosmic storytelling into the architecture of ownership and value. It demonstrates a fusion of art, philosophy, and technology, fostering reflection on courage, refinement, human connectivity, guidance, and transformation. Through Nile-like resilience, it envisions an automated reserve that irrigates economic deserts, ensuring equitable prosperity and eradicating poverty even in global adversities, as floods once sustained Egypt's civilization against isolation and drought. Challenges for adoption remain – including regulatory compliance, security, and scalability – but Stellar presents a compelling model for next-generation digital currencies that are not only functional and tradable but also collectible, conceptually rich, and culturally meaningful. By linking AI-generated artifacts with blockchain verification and symbolic storytelling, Stellar offers a vision for how humanity may encode its aspirations, ethics, and cosmological understanding into the evolving digital economy.

Huang Dongyan, Huang Min

For communication scenarios demanding extremely high information security and facing significant risks of data leakage, a covert communication scheme based on Ethereum virtual machine bytecode was proposed. By strategically allocating the storage space of smart contract variables, the scheme embedded covert data into contract bytecode and utilized the inherent characteristics of bytecode to set positioning markers, enabling efficient extraction by the receiver. Additionally, three ciphertext parsing modes were designed to accommodate transmissions of different data scales, further enhancing the security of encoded data. Theoretical analysis and extensive experimental results demonstrate that the scheme can effectively hide up to 170 bit of information per transaction. The structural similarity of opcode frequency distributions between the embedded contract and the original contract reaches up to 99.78%. The Pearson correlation coefficient of the high-frequency 3-gram opcode patterns between the normal and embedded contracts is 0.999 7 (<italic>p </italic>= 6.42×10⁻¹⁴), indicating that the embedding process does not introduce statistically significant differences in the local instruction sequence distribution. These results fully validate the strong concealment capability, transmission efficiency, and security of the proposed scheme.

Mrs.A.Anitha Mrs.A.Anitha, Amina Tabassum, POTTABATHINI SISIRA, SANKINENI THARAKARAM · 5 authors

In IIoT situations, federated learning (FL) is a way to use industrial data that protects privacy. At the same time, adding blockchain to federated learning training makes it more trustworthy. But there are still some big problems with current blockchain-based FL frameworks: 1) The current consensus mechanisms don't do a good job of filtering out bad devices, which lets low-quality participants mess with global model training and make the model less robust; 2) Current privacy budget strategies are too simple, making it hard to find a balance between protecting privacy during statistical queries and gradient updates. Strong privacy protection lowers model accuracy, while weak protection doesn't protect against poisoning attacks. This paper proposes ShieldDFL, a blockchain-based federated learning framework with dual privacy protection and reputation-driven consensus, to solve these problems. This method uses a hybrid consensus mechanism based on LSTM-based reputation scoring to dynamically assess both short-term and long-term device contributions. This makes it possible to choose the best devices with accuracy. At the same time, it adds a new dual privacy budget mechanism that uses differential privacy for both statistical queries and gradient updates. This keeps privacy strong while keeping the model's performance high. The proposed method lowers the chances of bad devices getting into the consensus pool to 1.5%, lowers the success rates of SAR and BASR attacks to 5.8% and 2.1%, respectively, and keeps the model's accuracy high at 98.1% on MNIST and 87.6% on CIFAR-10. In general, the proposed framework does a good job of getting around the security and privacy problems that come with blockchain-based federated learning. It offers a fast and flexible way for decentralised and trustworthy collaboration in IIoT situations.

Євген Олександрович Живило, Юрій Володимирович Кучма

Formulation of the problem in general. The purpose of the article is to develop a multi-agent model of adaptive trust for decentralised confidential systems, capable of ensuring the integrity and reliability of computing processes in the presence of adaptive attacks on network nodes. Research methods. During the research, analysis and synthesis methods were used to study approaches to the construction of multi-agent systems and trust management mechanisms in decentralised environments. The method of system and simulation modelling was used to develop a multi-agent model of adaptive trust and to study its behaviour under attacks on the integrity of computing processes. Experimental and comparative methods enabled evaluation of the proposed approach's effectiveness and justification of its advantages over static trust models. Literature review. Literary analysis shows that modern models of trust in decentralised systems are based on the integration of dynamic adaptive mechanisms, AI algorithms, and cryptographic protocols, which allow for increased cyber resilience and data integrity. At the same time, questions remain open about the scalability of models, the optimisation of adaptation parameters, and the integration of national and European regulatory approaches into practical systems, which provide a scientific perspective for the development of multi-agent models of adaptive trust. Research results. The article formalises attacks on the integrity of computing processes and develops a multi-agent model of adaptive trust for decentralised confidential systems based on Bayesian updating and evolutionary adaptation of strategies. The results of the simulation experiments confirmed that the proposed model provides high resistance to attacks, rapid stabilisation of agent confidence levels and an effective balance between security, privacy and performance. Research novelty. The work improves approaches to trust formation in decentralised systems by integrating models of multi-agent interaction and stochastic game theory, in which trust is modelled as an evolutionary process under conditions of incomplete information. Well-known Bayesian models of trust have been expanded by combining Bayesian belief update mechanisms with reinforcement learning algorithms, ensuring dynamic adaptation of agent behaviour to variable and targeted attacks on the integrity of computational processes. The mechanism for correcting agents' strategies has been clarified, extending classic game models of trust to decentralised, confidential systems without centralised control, thereby increasing their resistance to adaptive threats. Theoretical and practical significance. The study expands theoretical approaches to the formation of adaptive trust in decentralised systems and integrates Bayesian updating with reinforcement learning algorithms. In practice, the model increases resistance to integrity attacks and ensures the confidentiality of data exchange, enabling the adaptive development of secure platforms for federated learning, Web3, and IoT. Conclusion and future work. The proposed model of adaptive trust in decentralised systems, integrating Bayesian updating, behavioural indicators, and reinforcement learning, ensures agent self-adaptation and increases resistance to attacks on data integrity under conditions of incomplete information. Simulation experiments confirmed the model's effectiveness in balancing security, privacy, and the transparency of interaction, opening the way for integration into Zero Trust Architecture and the development of intelligent, next-generation trust systems.

Ian Staley

Quantum computing’s accelerating trajectory threatens the cryptographic foundations of every major blockchain network. Recent research demonstrates that fewer than 500 000 physical qubits could break ECC-256 in approximately nine minutes, while expert surveys place a 28–49% probability of a cryptographically relevant quantum computer (CRQC) emerging within ten years. This paper presents a layered reference architecture for end-to-end quantum-resilient distributed ledger systems, making three contributions: (1) a structured threat analysis applying STRIDE across blockchain architectural layers and post-quantum cryptography (PQC) migration phases; (2) a seven-layer reference architecture with per-layer interface specifications and dependency graph; and (3) a multi-chain quantum readiness assessment covering twelve major networks with fintech-specific migration strategies for decentralised finance (DeFi), stablecoins, tokenised real-world assets (RWA), and decentralised identity (DID). A critical finding is that blockchain’s primary quantum risk is real-time signature forgery upon CRQC arrival, not retroactive harvest-now-decrypt-later (HNDL) attacks on signatures. Cross-chain bridges, data availability layers, and Lightning Network payment channels are identified as the most critically neglected quantum attack surfaces.