Blockchain consensus mechanisms form the backbone of decentralized systems by ensuring agreement among distributed nodes without a central authority. At the core of these mechanisms lie number-theoretic foundations, including cryptographic primitives such as modular arithmetic, hash functions, elliptic curve cryptography, and zero-knowledge proofs. These mathematical constructs enable secure transaction validation, identity verification, and resistance against adversarial attacks. This paper presents a systematic review of number-theoretic foundations underpinning blockchain consensus mechanisms, focusing on methods, architectural implementations, and emerging research directions. The study analyses widely adopted consensus algorithms such as Proof of Work (PoW), Proof of Stake (PoS), and Byzantine Fault Tolerant (BFT) protocols, highlighting their dependence on number theory for ensuring security, randomness, and fairness. A comprehensive review of 30 studies published between 2018 and 2023 is conducted to examine advancements in cryptographic techniques such as verifiable random functions (VRFs), homomorphic encryption, and zero-knowledge proofs. These techniques play a crucial role in improving scalability, privacy, and efficiency of blockchain systems. The findings reveal that while number-theoretic approaches provide strong security guarantees, challenges such as computational overhead, scalability, and energy consumption persist. The paper concludes by identifying future research directions, including post-quantum cryptography, lightweight cryptographic protocols, and AI-assisted consensus optimization.
Theoretical background: In general, authors claim that the business model for any human-beings organisation defines who and how creates values in a socio-economic context. Taking into account the organisational theories presented in literature, authors notice a variety of definitions and components of business models. In addition, values in the business models have different interpretations. By definition, decentralised autonomous organisation (DAO) is using the Blockchain 2.0 technology, which strongly supports its internal operational management, change of attitude towards organisation members’ identification, and controlling internal activities. Purpose of the article: Construction of the Decentralised Autonomous Organisation (DAO) business model for determining DAO strategic development is the main purpose of this study. The authors aim to provide their own proposal of business model, as well as the identification of DAO business model components. The authors expand the DAO business model canvas, and beyond variables included in Osterwalder’s model, and consider some other important DAO features by example of TalentDAO case study. Research methods: The authors have focused on surveys of the management science literature in some popular repositories. Beyond that, they have added a DAO case study. They have done descriptive analysis of publications on business models and DAO business models. The authors applied the case study approach, because they argue that each DAO is different and taking into account suggestions provided by practitioners, the exploratory case study method is the best method to reveal idiosyncrasy of business organisation as well as applicability of theoretical business models for practice of DAO management. Main findings: Through the literature surveys, authors concluded that selected theories in science of management are fundamental for DAO construction and applicable for development of business models. Although the reviewed models are various, they have many common features and allow constructing the authors’ model of DAO business, which is an extension of Osterwalder Business Model Canvas. The authors characterised DAO partners, customers, values, resources, and activities. The authors discussed constraints and risks of DAO activities as well as the applied methods of coordination and control. The authors claim that DAO supports decentralized decision-making and intra-organizational trust intensification. They argue that the case study on DAO business model is an exemplification, which can be useful for development of other similar DAOs.
One of the main Web3 applications is Non-Fungible Tokens, blockchain-based certificates to keep track of the ownership of unique digital or physical assets. Nowadays, there is no standard method to evaluate an NFT, and only for a trait-based collection can we rely on the rarity score, which estimates the scarcity of the traits of the NFT. However, rarity is unsuitable for describing the price of a token in a volatile market, and it is not a good price indicator because a token’s price is strictly related to external unpredictable events and the interest people have in specific assets. In this paper, we propose an evaluation model called The Popularity Model , that aims to evaluate NFTs based on marketability The Popularity Model is based on a set of indices which define a dynamic, socioeconomic indicator, with an antifraud system. We formalised and compared our popularity model and the rarity score to show their differences. Finally, we propose two applicable use cases in which the popularity index can be applied. The experiments show and confirm the utility and efficacy of the proposed evaluation model.
Amid the rapid evolution of digital currencies and the decentralized finance (DeFi) ecosystem, technology-driven, anonymous, and cross-border financial crimes pose systemic challenges to traditional regulatory frameworks. Grounded in three core theories of criminal psychology—Rational Choice Theory, Routine Activity Theory, and Techniques of Neutralization—and integrating the “technology–society co-construction” perspective from the sociology of technology, this study constructs a three-dimensional analytical framework encompassing “technological ecology, social cognition, and individual psychology.” It systematically elucidates the psychological formation logic and evolutionary pathways of financial crimes within the DeFi domain. The research reveals that the technical features of DeFi—anonymity, decentralization, and code autonomy—collectively create a “structural opportunity space” characterized by low accountability costs and weakened moral constraints. Subcultural communities further supply “morally neutralizing scripts” through narratives of crypto-libertarianism and the myth of “code as law.” Under these dual influences, individual psychology undergoes transformation, manifesting as complex motivations, distorted risk perceptions, and heightened moral disengagement, ultimately leading to a rationalization mechanism for criminal acts veiled behind “technological neutrality.”
The security and decentralization of Proof-of-Work (PoW) have been well-tested in existing blockchain systems. However, its tremendous energy waste has raised concerns about sustainability. Proof-of-Useful-Work (PoUW) aims to redirect the meaningless computation to meaningful tasks such as solving machine learning (ML) problems, giving rise to the branch of Proof-of-Learning (PoL). While previous studies have proposed various PoLs, they all, to some degree, suffer from security, decentralization, or efficiency issues. In this paper, we propose a PoL framework that trains ML models efficiently while maintaining blockchain security in a fully distributed manner. We name the framework SEDULity, which stands for a Secure, Efficient, Distributed, and Useful Learning-based blockchain system. Specifically, we encode the template block into the training process and design a useful function that is difficult to solve but relatively easy to verify, as a substitute for the PoW puzzle. We show that our framework is distributed, secure, and efficiently trains ML models. We further demonstrate that the proposed PoL framework can be extended to other types of useful work and design an incentive mechanism to incentivize task verification. We show theoretically that a rational miner is incentivized to train fully honestly with well-designed system parameters. Finally, we present simulation results to demonstrate the performance of our framework and validate our analysis.
Tushin Mallick, Maya Zeldin, Murat Cenk, Cristina Nita-Rotaru
As quantum computing advances toward practical deployment, it threatens a wide range of classical cryptographic mechanisms, including digital signatures, key exchange protocols, public-key encryption, and certain hash-based constructions that underpin modern network infrastructures. These primitives form the security backbone of most blockchain platforms, raising serious concerns about the long-term viability of blockchain systems in a post-quantum world. Although migrating to post-quantum cryptography may appear straightforward, the substantially larger key sizes and higher computational costs of post-quantum primitives can introduce significant challenges and, in some cases, render such transitions impractical for blockchain environments. In this paper, we examine the implications of adopting post-quantum cryptography in blockchain systems across four key dimensions. We begin by identifying the cryptographic primitives within blockchain architectures that are most vulnerable to quantum attacks, particularly those used in consensus mechanisms, identity management, and transaction validation. We then survey proposed post-quantum adaptations across existing blockchain designs, analyzing their feasibility within decentralized and resource-constrained settings. Building on this analysis, we evaluate how replacing classical primitives with post-quantum alternatives affects system performance, protocol dynamics, and the incentive and trust structures that sustain blockchain ecosystems. Our study demonstrates that integrating post-quantum signature schemes into blockchain systems is not a simple drop-in replacement; instead, it requires careful architectural redesign, as naive substitutions risk undermining both security guarantees and operational efficiency.
Dec 15, 2025·Blockchain Confluence (satellite conference of the 1st IEEE International Conference on Distributed Ledger Technologies), Nov 2025, Lisboa, Portugal
Eddy Kiomba Kambilo, Nicolas Herbaut, Irina Rychkova, Carine Souveyet
Blockchain technology is gaining momentum across many sectors. Whereas blockchain solutions have important positive effects on the business domain, they also introduce constraints and may cause delayed or unforeseen negative effects, undermining business strategies. The diversity of blockchain patterns and lack of standardized frameworks linking business goals to technical design decisions make pattern selection a complex task for system architects. To address this challenge, we propose Blockchain--Technology-Aware Enterprise Modeling (BC-TEAEM), a decision support framework that combines ontologies of blockchain patterns and domain-independent soft goals with a multi-criteria decision-making approach. The framework focuses on the interplay between a domain expert and a technical expert to ensure alignment and traceability. By iteratively capturing and refining preferences, BC-TEAEM supports systematic selection of blockchain patterns. We develop a prototype decision support tool implementing our method and validate it through a case study of a pharmaceutical company's supply chain traceability system, demonstrating the framework's applicability. %a supply chain traceability case study.
With the rise of cryptocurrencies, many new applications built on decentralized blockchains have emerged. Blockchains are full-stack distributed systems where multiple sub-systems interact. While many deployed blockchains and decentralized applications need better scalability and performance, security is also critical. Due to their complexity, assessing blockchain and DAPP security requires a more holistic view than for traditional distributed or centralized systems. In this thesis, we summarize our contributions to blockchain and decentralized application security. We propose a security reference architecture to support standardized vulnerability and threat analysis. We study consensus security in single-chain Proof-of-Work blockchains, including resistance to selfish mining, undercutting, and greedy transaction selection, as well as related issues in DAG-based systems. We contribute to wallet security with a new classification of authentication schemes and a two-factor method based on One-Time Passwords. We advance e-voting with a practical boardroom voting protocol, extend it to a scalable version for millions of participants while preserving security and privacy, and introduce a repetitive voting framework that enables vote changes between elections while avoiding peak-end effects. Finally, we improve secure logging using blockchains and trusted computing through a centralized ledger that guarantees non-equivocation, integrity, and censorship evidence, then build on it to propose an interoperability protocol for central bank digital currencies that ensures atomic transfers.
Structural changes and outliers often coexist, complicating statistical inference. This paper addresses the problem of testing for parameter changes in conditionally heteroscedastic time series models, particularly in the presence of outliers. To mitigate the impact of outliers, we introduce a two-step procedure comprising robust estimation and residual truncation. Based on this procedure, we propose a residual-based robust CUSUM test and its self-normalized counterpart. We derive the limiting null distributions of the proposed robust tests and establish their consistency. Simulation results demonstrate the strong robustness of the tests against outliers. To illustrate the practical application, we analyze Bitcoin data.
Abstract This paper investigates the optimization of data sampling and target labeling techniques to enhance algorithmic trading strategies in cryptocurrency markets, focusing on Bitcoin (BTC) and Ethereum (ETH). Traditional data sampling methods, such as time bars, often fail to capture the nuances of the continuously active and highly volatile cryptocurrency market and force traders to wait for arbitrary points in time. To address this, we propose an alternative approach using information-driven sampling methods, including the CUSUM filter, range bars, volume bars, and dollar bars, and evaluate their performance using tick-level data from January 2018 to June 2023. Additionally, we introduce the Triple Barrier method for target labeling, which offers a solution tailored for algorithmic trading as opposed to the widely used next-bar prediction. We empirically assess the effectiveness of these data sampling and labeling methods to craft profitable trading strategies. The results demonstrate that the innovative combination of CUSUM-filtered data with Triple Barrier labeling outperforms traditional time bars and next-bar prediction, achieving consistently positive trading performance even after accounting for transaction costs. Moreover, our system enables making trading decisions at any point in time on the basis of market conditions, providing an advantage over traditional methods that rely on fixed time intervals. Furthermore, the paper contributes to the ongoing debate on the applicability of Transformer models to time series classification in the context of algorithmic trading by evaluating various Transformer architectures—including the vanilla Transformer encoder, FEDformer, and Autoformer—alongside other deep learning architectures and classical machine learning models, revealing insights into their relative performance.
Smart contracts rely on blockchain oracles to access off-chain data, yet existing oracle designs often face challenges such as untrustworthy data sources, weak temporal guarantees, and limited verifiability. This work presents Ivy Oracle, a robust and time-trustworthy data feed framework that enhances the reliability and auditability of off-chain information for smart contracts. Ivy Oracle integrates trusted execution environments (TEEs) for secure data acquisition, an external time server for authenticated timestamps, and a PageRank-based trust model to evaluate source credibility. We implement and evaluate Ivy Oracle on the Ethereum Sepolia testnet, demonstrating that it achieves up to 63.6% lower on-chain gas consumption than Chainlink for signature verification while maintaining only a slight increase in communication overhead due to its dual-attestation mechanism. These results confirm that Ivy Oracle provides strong time trustworthiness and data reliability with minimal performance cost, making it suitable for latency-sensitive blockchain applications.
P. Chinnasamy, R. Shashidhar Reddy, Y. Lohith Kiran, D. Prathap Reddy · 5 authors
In the age of using technology to work together remotely, the inability to share files in a secure manner is a problem often faced since many online sharing options available do not have good protection on their sharing options and do not safeguard against unauthorized use of the files. This paper presents a secure file sharing portal that has end to end encryption and user-based access controls and that works inside a web browser. The system uses the Web Crypto API interface to provide local encryption on the user’s devices using the AES-GCM encryption algorithm, so that the user’s plaintext documents do not leave the device. A separate layer of protection exists in the system. It is not enough for a user to just receive the encrypted file. The user has to receive a decryption key that the sender has to share through a separate channel. Access is for members only which requires sender approval and we have features like secure QR code share, session monitoring, digital certificates for identity proof and policy enforcement for compliance. Also, we have a "Secure Space" module which is for ephemeral work groups that has in space chat, multi user invites, controlled key exchange and one click revocation which in turn puts power back in the users’ hands. We combined zero knowledge structure with audit able workflows to present a privacy first, scalable and easy to use solution. Also, we show how we used modern web tech to create a trusted setting for sensitive file share which at the same time does not sacrifice ease of use or performance.
With the expansion of international trade scale and the increasing demands for transportation capacity and operational reliability, this paper proposes a port-railway collaborative scheduling system driven by the integration of blockchain and AI. This system adopts the data sharing and anti-tampering mechanism of distributed ledgers, combines machine learning to improve the accuracy of scheduling decisions, thereby achieving self-tuning scheduling plans, intelligent prediction of transportation demands, and maximizing resource utilization. This intelligent scheduling system not only enhances efficiency and security, but also strengthens the synergy between ports and railways, reduces operational costs, and further improves scheduling quality and system reliability. Through practical case verification, it is proved that this system is effective in real scenarios.
Biru Rajak, Malik Bader Alazzam, B Karthikeyan, P N V Syamala Rao M · 6 authors
The scale of IoT and IIoT systems developing is growing rapidly since they deploy in critical infrastructure which also created major security issues such as data breaches, unauthorized access, and centralized model lack of trust issues. It is search of this study to formulate a block-chain-based IoT network that provides secure, trustful and corrupted-proof transmission of data. What is new in the approach is the combination of the use of machine learning-based real-time anomaly detection and the Ethereum-based smart contracts in the creation of a decentralized and intelligent security layer. In the offered framework, models XGBoost are trained using the Edge-IIoTset dataset with maximum accuracy of detection of 98%. Compared to traditional centralized and standalone ML-based solutions, the hybrid system is found to be much more efficient in both initial detection (precision), trust enforcement, and resilience. The findings ratify that the framework can be deployed in sensitive IoT/IIoT networks based not only on its ability to identify the source of cyber threats and curb them in real-time without necessitating any update, but also on its potential to increase the transparency and traceability of the network.
Decentralized Finance (DeFi) has become a key innovation within blockchain technology by enabling permissionless and programmable financial services without traditional intermediaries. This thesis examines how yield is generated in DeFi and provides a systematic comparison of the main implementation models that enable it. The study focuses on three core mechanisms—liquidity mining, interest-bearing token systems, and automated vault strategies—and explores how they operate in practice through case studies of Aave, Uniswap, and Yearn Finance. The research aims to classify these models, analyze their technical foundations, and evaluate their governance structures and associated risks. The analysis is based on a literature review and protocol documentation from leading DeFi platforms. Each model is assessed across several dimensions, including reward structure, capital efficiency, user accessibility, and exposure to risks such as smart contract exploits, impermanent loss, and market volatility. The case studies demonstrate how different design choices lead to variations in yield generation: Aave emphasizes lending-based interest mechanisms, Uniswap relies on trading fees, and Yearn Finance automates strategy allocation across protocols. The findings show that no single model is universally superior; instead, each involves trade-offs between sustainability, complexity, and yield potential. Governance also emerges as a central factor in long-term stability, as the governance mechanisms influence strategy design, risk management, and protocol evolution. Overall, the study highlights a shift in DeFi from rapid growth toward more sustainable and structured yield frameworks.
Pankaj Gugnani, Monis Khan, Spandan Barve, Debanjan Sadhya · 5 authors
Public blockchains like Ethereum generate vast amounts of transactional data, offering insight into significant economic and decentralized application activity. However, these data are often unstructured and semantically poor. Understanding the functionality of smart contracts is crucial for unlocking the potential of this data. This work presents a novel methodology to transform raw blockchain transaction logs into semantically rich representations. We first classify smart contracts by analyzing their function names and structures using N-gram profiling and embedding comparisons against known standards like ERC/EIP. This process assigns functional labels (e.g., DeFi, Exchange, Token) to the smart contracts. Subsequently, we model the framework as a heterogeneous graph of users and labeled contracts. We employ a modified Node2Vec algorithm with an enforced alternating node-type walk strategy to effectively capture the dynamics of user-contract interactions. This process yields low-dimensional vector embeddings for users and contracts, making blockchain data readily available for knowledge discovery. We demonstrate the utility of our approach through a smart contract recommendation system that suggests relevant contracts to users based on their interaction history and learned embeddings.
The digital economy is rapidly transforming the global landscape by integrating technology, entrepreneurship, and innovation across every sector. Startups have become the key drivers of this transformation, enabling new models of production, finance, and governance. By 2047, the digital economy is expected to evolve into a deeply interconnected system powered by artificial intelligence, blockchain, decentralized finance, and sustainable technologies. These advancements will reshape industries, empower small enterprises, and foster inclusive growth. This paper explores how startups will act as engines of innovation, leveraging digital tools to solve complex social and economic challenges. It highlights emerging trends such as AI-driven decision-making, edge computing, green technologies, and decentralized governance models that will redefine the global business environment. At the same time, the paper acknowledges the challenges of data privacy, cybersecurity, skill development, and environmental sustainability. Through policy analysis and strategic recommendations, the study emphasizes the importance of strong digital infrastructure, ethical data practices, and inclusive innovation ecosystems to ensure balanced growth. By 2047, success in the digital economy will depend not only on technological advancement but also on human creativity, collaboration, and sustainable practices.
This study contributes to the growing literature on the determinants of Bitcoin volatility by examining its relationship with financial stress. Building on prior research linking Bitcoin volatility to broader economic and financial uncertainty, we employ a combination of regression analysis, a GARCH-MIDAS framework, and a Vector Autoregression (VAR) model to evaluate both the static and dynamic effects of financial uncertainty on Bitcoin. Preliminary regression results indicate that financial stress measures significantly and negatively predict Bitcoin volatility. The GARCH-MIDAS model confirms these results, showing a strong negative impact of financial stress on the long-term component of volatility. VAR analysis further reveals that Bitcoin volatility decreases in response to shocks in financial stress indicators. These findings highlight Bitcoin’s sensitivity to systemic financial conditions and carry important implications for risk management among cryptocurrency traders, institutional investors, and financial regulators.