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.
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.
Blockchain technology is considered a transformative innovation, offering decentralized, secure, and transparent solutions to various industries, with cryptocurrencies being its most famous application. The volatility and non-linear behavior of cryptocurrency markets pose significant challenges for predicting their prices accurately. Predicting cryptocurrencies prices based on traditional statistical methods often fail to capture the market complex dynamics. Therefore, the recent developments in Artificial Intelligence, especially in deep learning and ensemble-based approaches have presented promising results. This study delivers a comprehensive literature review focusing on applying deep learning and ensemble deep learning algorithms in cryptocurrency time series price prediction. The main deep learning models such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) are examined with a variety of time intervals and cryptocurrency types. The findings present that deep learning models, especially when used in hybrid or ensemble configurations, have obtained promising results. This review highlights the efficacy and significant potential of ensemble deep learning and its capabilities in cryptocurrencies price trend forecasting offering valuable insights for investors and researchers.
We study non-interactive zero-knowledge proofs (NIZKs) for NP satisfying: 1) statistical soundness, 2) computational zero-knowledge and 3) certified-everlasting zero-knowledge (CE-ZK). The CE-ZK property allows a verifier of a quantum proof to revoke the proof in a way that can be checked (certified) by the prover. Conditioned on successful certification, the verifier's state can be efficiently simulated with only the statement, in a statistically indistinguishable way. Our contributions regarding these certified-everlasting NIZKs (CE-NIZKs) are as follows: - We identify a barrier to obtaining CE-NIZKs in the CRS model via generalizations of known interactive zero-knowledge proofs that satisfy CE-ZK. - We circumvent this by constructing CE-NIZK from black-box use of NIZK for NP satisfying certain properties, along with OWFs. As a result, we obtain CE-NIZKs for NP in the CRS model, based on polynomial hardness of the learning with errors (LWE) assumption. - In addition, we observe that the aforementioned barrier does not apply to the shared EPR model. We leverage this fact to construct a CE-NIZK for NP in this model based on any statistical binding hidden-bits generator, which can be based on LWE. The only quantum computation in this protocol involves single-qubit measurements of the shared EPR pairs.
This study asks whether Ethereum’s proof-of-stake (PoS) incentives not only make economic sense on paper but also feel attractive to real validators who may be loss-averse and sensitive to risk. We take a canonical Eth2 slot-level model of rewards, penalties, costs, and proposer-conditional maximal extractable value (MEV) and overlay a prospect-theoretic valuation that captures reference dependence, loss aversion, diminishing sensitivity, and probability weighting. This Prospect-Theoretic Incentive Mechanism (PT-IM) separates the “money edge” (expected accounting return) from the “felt edge” (behavioral value) by mapping monetary outcomes through a prospect value function and comparing the two across parameter ranges. The mechanism is parametric and modular, allowing different MEV, cost, and penalty profiles to plug in without altering the base PoS model. Using stylized numerical examples, we identify regions where cooperation that pays in expectation can remain unattractive under plausible loss-averse preferences, especially when penalties are salient or MEV is volatile. We discuss how these distortions may affect validator participation, economic security, and the tuning of rewards and penalties in Ethereum’s PoS. Integrating behavioral valuation into crypto-economic design thus provides a practical diagnostic for adjusting protocol parameters when economics and perception diverge.
Dan Alexandru Mitrea, Constantin Viorel Marian, Rareş Alexandru Manolescu
In many jurisdictions, property registration and transfers remain constrained by inefficient, paper-based processes that depend on multiple intermediaries and bureaucratic approvals. This paper proposes a decentralized, blockchain-based property platform designed to streamline these processes using Non-Fungible Tokens (NFTs) and artificial intelligence (AI) agents to modernize public-sector asset management. The work addresses the persistent inefficiencies of paper-based property registration and ownership transfer by embedding legal and administrative logic within smart contracts and automating compliance through an intelligent conversational interface. The system was implemented using Ethereum-based ERC-721 standards, React for the user interface, and Langfuse-powered AI integration for guided user interaction. The pilot implementation presents secure, transparent, and auditable property-transfer transactions executed entirely on-chain, while hybrid IPFS-based storage and decentralized identifiers preserve privacy and legal validity. Comparative analysis against existing national initiatives indicates that the proposed architecture delivers decentralization, citizen control, and interoperability without compromising regulatory requirements. The system reduces bureaucratic overhead, simplifies transaction workflows, and lowers user error risk, thereby strengthening accountability and public trust. Overall, the paper outlines a viable foundation for legally aligned, AI-assisted digital property registries and offers a policy-oriented roadmap for integrating blockchain-enabled systems into public-sector governance infrastructures.
Based on distributed ledger technology, a new type of arbitration courts has been emerging in the world for the last five years. Their task is to resolve disputes using blockchain and smart contracts. Did the creators of the idea of “distributed justice” really invent a new way to effectively and fairly resolve disputes in the 21st century? Blockchain arbitration involves resolving disputes using the theory of multi-person games, the concept of Schelling point, the idea of decentralized autonomous organizations (DAO), tokens and crowdsourcing. The article attempts to answer the question of whether arbitration decisions made on the basis of economic incentives can be considered to meet the criteria of Aristotelian rectificatory justice. The article is analytical in nature, addressing a topic that has only become relevant in the world a few years ago. The analysis uses theses from cryptoeconomics and game theory. The work initially outlines the problems. Due to the small number of experiences of digital arbitration in the world, the theses and hypotheses of the text, written from the perspective of theory and philosophy of law, require further in-depth analyses.
Blockchain networks have revolutionized decentralized applications but remain vulnerable to evolving security threats due to their reliance on static consensus mechanisms that cannot adapt to changing threat landscapes. This paper addresses this critical security gap by proposing Autonomous Defense-Adaptive Consensus Optimisation for Blockchain Networks (ADACON), an original framework for the dynamic adjustment of consensus mechanisms based on Bayesian threat detection. The research investigates how real-time adaptation between multiple consensus protocols can enhance blockchain resilience while maintaining performance. The approach integrates a Bayesian Threat Detector, Consensus Adapter, and Network State monitor in a modular architecture that continuously assesses network conditions and switches between five consensus mechanisms (PoW, PoS, PBFT, PoA, DPoS) as threats emerge. The framework was evaluated through comprehensive simulations involving 1,000 nodes, testing response to six distinct attack vectors, including Sybil, DoS, Byzantine, Eclipse, Majority, and Routing attacks. Results demonstrate that ADACON effectively identifies and responds to varied attacks with a latency of 29.7 ms and throughput of 833 TPS). Statistical validation across five independent simulation runs (seeds 5-9) confirmed framework reliability with consistent performance metrics (CV < 7.1% for latency, 5.4% for throughput). Delegated Proof of Stake emerged as the most frequently selected mechanism (23.2%) due to its balanced performance across multiple security dimensions. Significantly, the system exhibited greater adaptability and attack coverage than existing hybrid approaches. The previous high switching frequency was reduced by using hysteresis, i.e., by providing dwell time and an improved threshold that avoids unnecessary switching. The study concludes that dynamic consensus adaptation offers substantial security advantages for blockchain networks, particularly in high-security environments like financial systems and critical infrastructure. However, further research must focus on optimizing switching frequency and developing secure transition protocols to maximize effectiveness. ADACON represents an incremental extension tested toward more resilient blockchain systems that can autonomously respond to emerging threats while balancing security, performance, and resource utilization.
Zia Ullah, Zia Ullah, Sanam Shahla Rizvi, Ibrar Ali Shah · 5 authors
Vehicular Ad Hoc Networks (VANETs) are essential for the success of Intelligent Transportation Systems (ITS), providing real-time communication between vehicles and infrastructure. However, the highly dynamic and decentralized nature of VANETs introduces significant challenges in ensuring trust and security across the network, including security threats, communication overhead, and energy inefficiencies. This paper presents a novel blockchain-based trust management framework that addresses these issues by incorporating lightweight consensus mechanisms, optimized data propagation strategies, and energy-aware protocols. Our approach reduces communication overhead by selectively propagating trust updates, leading to a 35% decrease in overall network traffic compared to traditional broadcast-based systems. In terms of trust accuracy, our model achieves over 95% accuracy in detecting malicious nodes, significantly outperforming existing solutions. The proposed system demonstrates the identification and penalization of malicious behaviors such as Sybil attacks and false reporting with a 25% improvement in detection rate, while maintaining low latency (an average reduction of 30% compared to PoW-based systems) and efficient energy consumption, reducing energy use by up to 40%. The proposed model also incorporates a hybrid Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) consensus mechanism, which further enhances its scalability and fault tolerance. Simulation results show that our framework converges to accurate trust values faster than traditional methods, ensuring that reliable trust evaluations are made in real-time, even under high mobility conditions. The combination of these optimizations ensures that our framework is not only secure but also highly efficient, capable of supporting scalable and resilient VANET deployments. Furthermore, our decentralized approach ensures that trust decisions are made in real-time without the need for a centralized authority, making the system more adaptable to the high-mobility conditions of VANETs. This research offers a comprehensive solution for VANETs trust management, significantly improving communication efficiency, trust accuracy, and energy consumption while maintaining robust security and scalability. Our proposed blockchain-based trust management system provides a secure, energy-efficient, and scalable solution for VANETs, setting the stage for future developments in secure vehicular communication networks.
This study examines budget management in the Decentralized Autonomous Governments (GADs) at the parish level, emphasizing the importance of transparency in public resource administration as a mechanism to promote accountability and prevent corruption. The main objective was to apply budgetary indicators to the Tacamoros Parish GAD, located in Sozoranga, Loja Province, during the fiscal periods 2021–2022, in order to evaluate the efficiency and effectiveness of budget allocations. A quantitative and descriptive approach was adopted, employing data collection and analysis tools based on the guidelines of the Organic Code of Territorial Organization, Autonomy, and Decentralization (COOTAD) and the Organic Code of Planning and Public Finance (COPFP). The results reveal that the budget cycle achieved a confidence level of 82.93%, with an associated risk of 17.07%. Likewise, revenue execution compared to the programmed figures reached 49.13% in 2021 and 69.15% in 2022, while expenditure execution was 59.47% in 2021 and 50.33% in 2022. The study concludes that strengthening long-term strategic planning and promoting greater financial autonomy in parish-level GADs are essential to ensure more efficient budget management for the benefit of the community.
Digital forensic investigation in 2025 faces unprecedented challenges posed by the convergence of decentralized web technologies (Web3), adversarial generative AI systems, and darknet infrastructure. Traditional attribution and evidence preservation methodologies prove in-sufficient when adversaries exploit blockchain immutability, synthetic media generation, and privacy-enhancing technologies to obscure malicious intent. This paper in-traduces SHARD (Shadowed and Silicon Hybrid Attribution and Reconstruction Diagnostic), a multi-modal forensic framework designed to recover, correlate, and at-tribute malicious artifacts across distributed ledger systems, synthetic content generators, and anonymized net-works. Through systematic analysis of 47 real-world cybercriminal cases and forensic evaluation against 12 at-tack vectors, SHARD achieves 89.2% attribution accuracy while reducing investigative timelines by 64% com-pared to conventional methods. We present novel techniques for blockchain temporal analysis, deepfake prove-nance tracking, and Tor-exit node correlation. The frame-work integrates machine learning-based anomaly detection with cryptographic verification to distinguish legitimate decentralized activity from adversarial manipulation. Our contributions include: (1) a formal threat model encompassing Web3 forensics; (2) a hybrid architecture combining on-chain and off-chain analysis; (3) algorithmic innovations for synthetic media fingerprinting; and (4) extensive empirical validation against contemporary attack scenarios. This work addresses a critical gap in digital forensics as investigative techniques must evolve alongside the technological infrastructure that criminals exploit.
This thesis examines whether Decentralized Autonomous Organizations (DAOs) can resolve the fundamental trilemma of global public goods provision, specifically addressing the seemingly impossible simultaneous achievement of effective climate action, responsible AI governance, and equitable international coordination. Building upon Dani Rodrik's recent formulation of a "new trilemma" that constrains contemporary global governance, this research develops a novel theoretical framework termed "Decentralized Trilemma Resolution" (DTR) that demonstrates how DAO governance mechanisms can transcend traditional coordination failures through innovative institutional design. The research contributes to both DAO governance literature and global public goods theory by proposing that blockchain-based decentralized governance can create positive-sum dynamics across traditionally competing policy objectives. Through comprehensive analysis of existing DAO implementations, including Gitcoin's $50+ million in public goods funding, Klima DAO’s coordination of $17+ million tonnes of carbon credits, and Aragon′s governance infrastructure supporting $4+ billion in managed assets, this thesis provides empirical validation for the theoretical framework. The DTR framework introduces four core mechanisms that enable trilemma resolution: Multi-Stakeholder Token Governance (MSTG), Algorithmic Transparency and Accountability (ATA), Modular Governance Architecture (MGA), and Incentive Alignment Mechanisms (IAM). Mathematical formalization demonstrates how these mechanisms create superadditive utility functions where coordination across climate action, AI governance, and equitable development generates synergistic rather than competitive outcomes. Key findings indicate that DAOs demonstrate 6-20x faster decision-making, 3-5x higher stakeholder participation, and 2-5x lower administrative costs compared to traditional governance mechanisms. However, challenges remain in enforcement capabilities, regulatory recognition, and scaling to nation-state level coordination. The thesis concludes that hybrid models combining DAO governance with traditional institutional structures offer the most promising pathway for near-term trilemma resolution, while pure DAO governance may emerge as viable for global coordination as technical and regulatory infrastructure matures. This research provides the first systematic framework for understanding how decentralized governance can address the fundamental coordination challenges of the 21st century, offering both theoretical insights and practical implementation pathways for policymakers, technologists, and global governance practitioners.
The security of a patient’s clinical records and their confidentiality is a crucial responsibility for any medical orga- nization seeking to operate optimally and protect the privacy of its patients. The loss of a patient’s clinical record in a data breach can have disastrous consequences. According to the 2022 Data Breaches Investigations Report by Verizon, human error contributed to 82% of data breaches. To address this issue, this research paper proposes a system that integrates user- side inspection software and blockchain technology to prevent data breaches by leveraging blockchain’s transparency and immutability. Our proposed system can prevent data breaches by protecting sensitive files from unauthorized access within the organization as well as outside the organization while also ensuring the secure transmission of cryptographically-secured files. The system utilizes a Proof of Stake (PoS) consensus algorithm to enhance security, scalability, and efficiency so that it can even be used by organizations without considerable computer architecture.