Trust (or distrust) in artificial intelligence (AI) is a critical research topic, given AI's pervasive integration across societal domains. Despite its significance, scholarly attention to process-based learned trust in AI remains limited. To address this gap, this study designed a virtual non-fungible token (NFT) investment task, featuring seven rounds of risk decision-making scenarios, to simulate an investment/trust game to explore participants' multifaceted trust under the influence of different chatbots' social role. The findings suggested the chatbot's social role had a significant impact on participants' trust behaviors and perceptions over time. Trust in the two chatbot types diverged until the system-induced failures occurred. The friend-like chatbot elicited a higher level of behavioral trust than the servant-like counterpart. During those trust-damaging moments, the friend-like chatbot proved more effective in mitigating trust erosion and facilitating trust repair, as evidenced by relatively stable investment behaviors. The findings reinforce the notion that friendship with AI can function as a relational buffer, softening the impact of trust violations and facilitating smoother trust recovery.
The rapid advancement of the Internet of Things (IoT) has led to the creation of large-scale interconnected networks of smart devices capable of autonomously collecting, processing, and exchanging data in real time across diverse application domains. While this development offers significant benefits, it also introduces critical challenges related to data security, privacy protection, interoperability, and the increasingly complex governance of distributed IoT systems. Traditional centralized governance approaches often fail to address these issues effectively due to single points of failure, limited transparency, and insufficient trust mechanisms. The integration of blockchain technology into IoT ecosystems provides a promising alternative by leveraging decentralized architecture, immutable ledgers, transparency, and tamper-resistant features that enhance accountability and trust. This study aims to identify and design an appropriate governance model for blockchain-integrated IoT systems that balances security, operational efficiency, and decentralization. The research adopts a conceptual and qualitative approach through a systematic literature analysis and the synthesis of existing governance, blockchain, and IoT frameworks to develop a structured governance model. The proposed framework defines institutional roles, policy structures, decision-making processes, and control mechanisms among participating entities. The results demonstrate that a blockchain-based governance model enhances system security, operational efficiency, and inter-organizational trust by reducing reliance on centralized authorities and improving data integrity. In addition, the use of smart contracts enables automated policy enforcement, transparent coordination, and sustainable system operations, supporting scalable and resilient governance for future blockchain IoT ecosystems.
Many Ethereum smart contracts rely on block attributes such as block.timestamp or blockhash to generate random numbers for applications like lotteries and games. However, these values are predictable and miner-manipulable, creating the Bad Randomness vulnerability (SWC-120) that has led to real-world exploits. Current detection tools identify only simple patterns and fail to verify whether protective modifiers actually guard vulnerable code. A major obstacle to improving these tools is the lack of large, accurately labeled datasets. This paper presents a benchmark dataset of 1,752 Ethereum smart contracts with validated Bad Randomness vulnerabilities. We developed a five-phase methodology comprising keyword filtering, pattern matching with 58 regular expressions, risk classification, function-level validation, and context analysis. The function-level validation revealed that 49% of contracts initially classified as protected were actually exploitable because modifiers were applied to different functions than those containing vulnerabilities. We classify contracts into four risk levels based on exploitability: HIGH_RISK (no protection), MEDIUM_RISK (miner-exploitable only), LOW_RISK (owner-exploitable only), and SAFE (using Chainlink VRF or commit-reveal). Our dataset is 51 times larger than RNVulDet and the first to provide function-level validation and risk stratification. Evaluation of Slither and Mythril revealed significant detection gaps, as both tools identified none of the vulnerable contracts in our sample, indicating limitations in handling complex randomness patterns. The dataset and validation scripts are publicly available to support future research in smart contract security.
Christopher Blake, Chen Feng, Xuachao Wang, Qianyu Yu
Proof of work blockchain protocols using multiple hash types are considered. It is proven that the security region of such a protocol cannot be the AND of a 51\% attack on all the hash types. Nevertheless, a protocol called Merged Bitcoin is introduced, which is the Bitcoin protocol where links between blocks can be formed using multiple different hash types. Closed form bounds on its security region in the $Δ$-bounded delay network model are proven, and these bounds are compared to simulation results. This protocol is proven to maximize cost of attack in the linear cost-per-hash model. A difficulty adjustment method is introduced, and it is argued that this can partly remedy asymmetric advantages an adversary may gain in hashing power for some hash types, including from algorithmic advances, quantum attacks like Grover's algorithm, or hardware backdoor attacks.
A unit of semantic labor cannot function as money, because meaning is not fungible without being destroyed. THE MONEY-FUNCTION TEST: Any instrument enabling transferability + accumulability + general comparability + convertibility + settlement power functions as money—regardless of framing. THE HARDEST SENTENCE: If semantic labor becomes currency, semantic life becomes debt. THE POST-MONEY OPERATOR STACK (PMOS): 1. Context Ledgers (CL): Memory without fungibility 2. Reciprocity Windows (RW): Obligation without permanent debt 3. Non-Transferable Credentials (NTC): Recognition without accumulation 4. Commons Access Rights (CAR): Allocation without payment 5. Dispute and Repair Protocols (DRP): Settlement without objectivity theater THE MONEY LIMIT: Money works when value can be abstracted from context. Money fails when value is inseparable from context. Semantic labor crosses the money limit. THE ABOLITION: Money is abolished not by replacing it with better money, but by building coordination systems appropriate to the form of value being coordinated. PMOS is designed so that adding money-properties destroys the system's function—structural protection against financialization. SCHOLARLY LINEAGE: Marx → Mauss → Polanyi → Graeber → Ostrom → Semantic Economy This document prevents recuperation of the Semantic Economy critique into "semantic tokens" or other money-functioning schemes.
The financial technology (FinTech) revolution, driven by Distributed Ledger Technology (DLT), presents a watershed moment for global commerce and law. At its core, DLT, encompassing cryptocurrency, blockchain, and smart contracts, challenges the foundational principles of traditional finance and legal jurisprudence: intermediation, jurisdiction, and contract enforceability. This paper analyzes the critical legal dimensions emerging from this technological shift, moving beyond an initial period of regulatory uncertainty toward a new era of targeted legislation and landmark litigation. Specifically, it examines the fragmented global regulatory response to crypto-assets (e.g., the EU's MiCA and US legislative efforts), the legal complexity of classifying DLT assets, the disruptive potential and data privacy concerns of non-currency blockchain applications, and the profound jurisprudential conflict between the deterministic "code is law" ethos of smart contracts and the flexibility of common and civil law traditions. The paper concludes that DLT presents a significant legal opportunity to enhance transparency and efficiency, but only through the establishment of nuanced, principle-based regulatory frameworks that can reconcile decentralized technology with the imperative of financial stability, consumer protection, and equitable legal recourse.
The Internet of Medical Things (IoMT) enables real-time health monitoring and intelligent clinical decision-making by continuously collecting and processing sensitive physiological data from wearable, implantable, and edge-connected devices. However, this data aggregation paradigm introduces critical privacy and security challenges, including data leakage, aggregator misbehavior, and adversarial attacks, while existing frameworks often fail to simultaneously ensure confidentiality, verifiability, and efficiency. To address these limitations, we propose MedGuard, a novel end-to-end secure data aggregation framework for IoMT that synergistically integrates Fully Homomorphic Encryption (FHE) based on the CKKS scheme and Groth16 zero-knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs). MedGuard enables healthcare providers to perform complex analytical queries, such as statistical analysis, anomaly detection, and trend forecasting, directly on encrypted data without decryption, ensuring compliance with privacy regulations. By allowing edge nodes to generate cryptographic proofs of correct computation and enabling cloud-based verification, MedGuard eliminates reliance on trusted intermediaries and mitigates insider threats. Our comprehensive evaluation, conducted in a high-fidelity OMNeT++ 6.0.1 simulation environment with 1,000 IoMT devices, 100 edge nodes, and an Amazon EC2 c5.4xlarge cloud server, uses a hybrid dataset combining real-world and GMM-augmented synthetic data. Results show that MedGuard achieves an end-to-end latency of 64.8 ms, a 13.3% improvement over state-of-the-art baselines, communication efficiency of 1.465 GB/s, per-query energy consumption of 1.489 mJ, and sustained throughputs of 1,200 packets/s, 120 aggregates/s, and 1,200 queries/s. These performance gains, combined with a robust [Formula: see text] security level, demonstrate that MedGuard delivers scalable, verifiable, and privacy-preserving analytics for next-generation smart healthcare systems.
This paper examines the directional connectedness between the returns of Bitcoin and Ethereum and the supply of stablecoins across different market conditions. Using a Quantile Vector Autoregression (QVAR) model, we analyze daily log-returns of major cryptocurrencies and changes in stablecoin supply from January 2021 to November 2024, capturing dynamics at the 5th, 50th, and 95th quantiles. Our findings show that the Total Connectedness Index (TCI) nearly triples under extreme conditions, with Bitcoin and Ethereum transitioning from passive roles in normal periods to dominant transmitters of influence during downturns. Stablecoins behave heterogeneously across regimes, with roles varying significantly even within the same subclass. Tether exhibits state-dependent behavior, acting as a net receiver of shocks in most conditions but emerging as a transmitter during bull markets. We also assessed the impact of the Terra-LUNA collapse, revealing a regime shift in the transmission of shocks: connectedness rises under normal and negative conditions but declines in positive markets. These patterns suggest that, under certain conditions, major cryptocurrencies can influence stablecoin issuance in distinct ways, leading to asymmetric adjustments in supply across individual stablecoins and shaping liquidity dynamics throughout the ecosystem. While we do not attempt to model the underlying mechanisms behind these shifts, our results point to the importance of monitoring state-dependent relationships and recognizing the diverse behaviors of stablecoins. The findings motivate the development of regime-sensitive monitoring tools and support ongoing policy discussions around stablecoin design, issuance frameworks, and market transparency.
The promulgation of Regulation (EU) 2024/1689 (the EU AI Act) establishes the world's first comprehensive legal framework for AI governance. However, a critical gap remains between the Act’s legislative intent and the technical reality of probabilistic AI systems. This working paper introduces Ternary Moral Logic (TML), a cryptographic governance architecture designed to operationalize the Act’s requirements for High-Risk AI systems. Unlike binary architectures that obscure uncertainty, TML enforces a tri-state logic—Proceed (+1), Pause (0), Refuse (-1)—mapped directly to the Act's risk categories. We demonstrate how this "Sacred Pause" mechanism satisfies Article 9 (Risk Management) and Article 14 (Human Oversight) by mechanically preventing action under high ethical uncertainty. Furthermore, we detail the implementation of "Immutable Moral Trace Logs" utilizing Merkle-batched storage on Layer-2 blockchains (Polygon zkEVM) to satisfy Article 12 (Record Keeping) and Article 61 (Post-Market Monitoring). This paper provides a complete technical specification for the TML framework, including logic gate definitions, smart contract architectures for three-party escrow, and zero-knowledge proof circuits for GDPR-compliant auditing. Comparative analysis demonstrates that this architecture reduces compliance latency to ≤2ms for inference and <500ms for logging, proving that rigorous regulatory enforcement is compatible with high-performance AI deployment. Interactive Report: A live, interactive version of this architecture is available at https://github.com/FractonicMind/TernaryMoralLogic/blob/main/Research_Reports/The%20Executable%20Architecture%20for%20the%20EU%20AI%20Act.html
ABSTRACT Stablecoins attract academic interest because of their value‐pegging mechanisms and price stability. This likely results in distinct market efficiency. This study compares stablecoins (USDC, Tether, Dai) with Bitcoin and Ethereum and assesses long memory through the Hurst exponent while addressing distortions caused by heavy tails and extreme events. Through shuffled and rank‐order series with a sliding‐window approach, we provide the first reliable time‐varying analysis. The results show that stablecoins exhibit inefficiency and anti‐persistence, with Tether being relatively more efficient. Their tail properties are highly sensitive to extreme events. In contrast, Bitcoin and Ethereum maintain stable weak‐form efficiency even during the COVID‐19 pandemic. These differences are linked to stablecoins' US dollar pegging mechanisms and regulatory constraints. The findings of this study enable comparisons of market efficiency between stablecoins and unpegged cryptocurrencies and offer insights for regulation and investment decisions.
In the digitally connected era, travel planning is increasingly hindered by the fragmentation of platforms used for destination discovery, accommodation booking, and experience sharing, forcing travelers to switch between multiple applications and leading to inefficiency, inconsistent information, and reduced satisfaction. To overcome this challenge, TripTale is introduced as an integrated web-based platform that unifies travel discovery, booking, and social interaction within a single ecosystem. The system enables users to explore destinations by selecting their country, state, and district, providing curated lists of tourist attractions with detailed highlights, cultural significance, and local specialties. By leveraging location-based services and interactive mapping, TripTale delivers real-time recommendations for nearby hotels, cafes, and lodges, allowing users to complete reservations directly within the platform. In addition to planning and booking, TripTale incorporates social networking features that allow travelers to upload photos, write reviews, and share travel tips, fostering a collaborative community and improving information reliability through shared experiences. A distinctive feature of TripTale is the integration of blockchain technology to authenticate and preserve travel memories. Users can convert their journeys into Non-Fungible Tokens (NFTs), ensuring secure, tamper- proof, and verifiable ownership of their digital experiences, thereby transforming personal travel records into collectible digital assets. The platform is implemented using HTML5, Tailwind CSS, and JavaScript for the frontend, while FastAPI and Supabase manage backend services such as authentication, data storage, and real-time updates. Cloud deployment on Vercel and Render ensures high availability and scalability, while integration with Google Maps API enables dynamic navigation and location intelligence. By combining modern web technologies, blockchain innovation, and user-centered design, TripTale provides a comprehensive and future- ready solution that simplifies travel planning, enhances user engagement, and preserves valuable travel experiences in a secure digital environment.
Blockchain technology and cryptocurrencies have attracted significant attention in recent years, yet remain susceptible to cyber threats such as phishing attacks. Existing detection approaches often suffer from high computational costs and limited robustness, especially when facing varying data distributions and sparse structures. To address these issues, we propose Robust, Node behavior, Transaction structure, and Network (R-NTN), a detection framework for Ethereum phishing accounts that leverages multi-dimensional transaction features. R-NTN first constructs 2-hop ego graphs via random walks, then extracts features from three complementary dimensions: behavioral attributes, transaction-based structural features, and network embeddings. These features are integrated into a unified representation for downstream classification. Experiments show that R-NTN consistently outperforms baseline methods and maintains high accuracy across datasets of different scales and compositions, demonstrating strong robustness and generalizability.
Decentralized Finance (DeFi) has become a major component of digital asset markets, yet accurately valuing protocol performance remains difficult due to high volatility, nonlinear pricing dynamics, and persistent disclosure gaps that amplify valuation risk. This study develops an Optuna-tuned Super Learner stacked ensemble to improve risk-aware DeFi valuation, combining Extremely Randomized Trees (ETs), Support Vector Regression (SVR), and Categorical Boosting (CAT) as heterogeneous base learners, with a K-Nearest Neighbors (KNNs) meta-learner integrating their forecasts. Using an expanding-window panel time-series cross-validation design, the framework achieves significantly higher predictive accuracy than individual models, benchmark ensembles, and econometric baselines, obtaining RMSE = 0.085, MAE = 0.065, and R2 = 0.97—representing a 25–36% reduction in valuation error. Wilcoxon tests confirm that these gains are statistically significant (p < 0.01). SHAP-based interpretability analysis identifies Gross Merchandise Volume (GMV) as the primary valuation determinant, followed by Total Value Locked (TVL) and key protocol design features such as Decentralized Exchange (DEX) classification, while revenue variables and inflation contribute secondary effects. The findings demonstrate how explainable ensemble learning can strengthen valuation accuracy, reduce information-driven uncertainty, and support risk-informed decision-making for investors, analysts, developers, and policymakers operating within rapidly evolving blockchain-based digital asset environments.
Anti-money laundering (AML) remains a critical challenge in cryptocurrency ecosystems, where blockchain’s transparency paradoxically coexists with pseudonymity. Traditional methods often fall short in modeling the temporal and structural complexity of transaction networks. This paper introduces ChronoWave-GNN, a graph neural framework designed from the theoretical perspective of time-frequency representation learning. By combining wavelet-based frequency decomposition with temporal encoding, our model captures nonstationary and multi-scale patterns inherent in illicit financial activity. This dual-domain perspective enhances the expressive capacity of graph representations without relying on modular patching. We validate our approach on the Elliptic dataset, where ChronoWave-GNN achieves a test accuracy of 0.9802 and F1-score of 0.9799, surpassing prior state-of-the-art results. These findings suggest that unifying temporal dynamics and spectral compression offers a principled and effective pathway for robust AML in decentralized financial systems.
Il presente report è stato sviluppato nell’ambito del progetto di ricerca MetaJust con l’obiettivo di offrire raccomandazioni di policy su alcuni dei temi più rilevanti legati al Metaverso. La rapida diffusione di queste piattaforme, sia centralizzate sia decentralizzate, ha infatti evidenziato la necessità di strumenti normativi e linee guida capaci di garantire la tutela dei diritti degli utenti, la trasparenza delle interazioni e la responsabilità dei diversi attori coinvolti, senza ostacolare l’innovazione tecnologica. Il documento prende in esame innanzitutto le questioni legate all’identità digitale, distinguendo tra metaversi centralizzati e decentralizzati e proponendo strumenti e strategie per garantire sicurezza, interoperabilità e responsabilità degli utenti. Viene inoltre approfondita la disciplina delle Decentralized Autonomous Organizations (DAO), evidenziando le potenzialità e i rischi legati alla gestione autonoma delle risorse digitali e le implicazioni normative. Un altro focus riguarda la raccolta, la gestione e la protezione dei dati personali, con indicazioni per rafforzare la trasparenza, la minimizzazione dei dati e la responsabilità dei gestori delle piattaforme. Il report analizza anche le strategie di moderazione dei contenuti e dei comportamenti, con l’obiettivo di prevenire abusi, molestie e pratiche illecite, bilanciando la sicurezza con i diritti degli utenti. Infine, in linea con gli obiettivi del progetto viene discusso l’uso delle piattaforme immersive per lo svolgimento di procedimenti giudiziari, evidenziando opportunità e criticità nella costruzione di processi equi e accessibili. Il report intende fornire un quadro giuridico per accompagnare lo sviluppo del Metaverso in chiave responsabile e consapevole.
Shiyu Zhang, Zining Wang, Jin Zheng, John Cartlidge
Systemic risk refers to the overall vulnerability arising from the high degree of interconnectedness and interdependence within the financial system. In the rapidly developing decentralized finance (DeFi) ecosystem, numerous studies have analyzed systemic risk through specific channels such as liquidity pressures, leverage mechanisms, smart contract risks, and historical risk events. However, these studies are mostly event-driven or focused on isolated risk channels, paying limited attention to the structural dimension of systemic risk. Overall, this study provides a unified quantitative framework for ecosystem-level analysis and continuous monitoring of systemic risk in DeFi. From a network-based perspective, this paper proposes the DeFi Correlation Fragility Indicator (CFI), constructed from time-varying correlation networks at the protocol category level. The CFI captures ecosystem-wide structural fragility associated with correlation concentration and increasing synchronicity. Furthermore, we define a Risk Contribution Score (RCS) to quantify the marginal contribution of different protocol types to overall systemic risk. By combining the CFI and RCS, the framework enables both the tracking of time-varying systemic risk and identification of structurally important functional modules in risk accumulation and amplification.
This study reviews the advancements in AI-driven methods for predicting stock prices, tracing their evolution from traditional approaches to modern finance. The role of AI in the market extends beyond predictive systems to encompass the intersection of financial markets with emerging technologies, such as blockchain, and the potential influence of quantum computing on economic modeling. A decentralized finance system examines the application of Reinforcement Learning in financial market prediction, highlighting its potential for continuous learning from dynamic market conditions. The study discusses the development of hybrid prediction models, stock market machine learning systems, and AI-driven investment portfolio management. The potential of quantum computing enhances portfolio analysis, fraud detection, optimization, and asset valuation for complex market predictions, as well as the impact of blockchain technologies on transparency, security, and efficiency. Machine learning techniques can significantly automate data collection and purification. Financial decision-making and the application of time-series analysis techniques can be readily learned through deep reinforcement learning for stock price prediction. Deep Neural Networks and Strategic Asset Allocation can be managed by evaluating performance and portfolio using real-time market insights from AI models. Although there are numerous ethical, sentimental, regulatory, and data quality issues in market prediction, the future job market is heavily dependent on these criteria, particularly through effective risk management and fraud detection.
The rise in Blockchain-based digital assets has transformed the financial ecosystems, which has also created complex governance and taxation challenges. The pseudonymous and cross-border nature of crypto transactions undermines traditional tax enforcement, leaving regulators such as the South African Revenue Service (SARS) reliant on voluntary disclosures with limited verification mechanisms, while existing Blockchain forensic tools and regulatory technologies (RegTechs) have advanced in anti-money laundering and institutional compliance, their integration into issues related to taxpayer compliance and locally adapted solutions remains underdeveloped. Therefore, this study conducts a state-of-the-art review of Blockchain forensics, RegTech innovations, and crypto tax frameworks to identify gaps in the crypto tax compliance space. Then, this study builds on these insights and proposes a conceptual model that integrates digital forensics, cost basis automation aligned with SARS rules, wallet interaction mapping, and non-fungible tokens (NFTs) as verifiable audit anchors. The contributions of this study are threefold: theoretically, which reconceptualise the adoption of Blockchain forensics as a proactive compliance mechanism; practically, it conceptualises a locally adapted proof-of-concept for diverse transaction types, including DeFi and NFTs; and lastly, innovatively, which introduces NFTs to enhance auditability, trust, and transparency in digital tax compliance.
We present a lightweight, probabilistic mechanism for certifying aligned storage between participants in decentralized systems. Participants respond to randomized suffix queries by performing forward scans over their locally stored data and returning short response sequences. A verifier observes only overlap statistics between responses. We prove that the overlap probability is bounded above by the minimum storage density among participants, ensuring that high observed overlap implies all parties store a large fraction of the underlying dataset. This bound holds regardless of adversarial strategy: a single well-provisioned participant cannot "carry" an under-provisioned partner. The protocol's "+1" advancement rule introduces pointer desynchronization that causes naïve Binomial models to overestimate tail probabilities by 2–3×. We establish rigorous security bounds through systematic simulation of Poisson-walk dynamics. For example, observing 10 or more matches out of 12 recorded elements rules out minimum density below 0.6 at the 2.4% significance level. Independent repetition amplifies confidence exponentially. The mechanism requires no cryptographic commitments per element, no global verifier, and reveals only O(m) randomly-selected elements per interaction. We analyze several natural adversarial strategies—fabrication, selective answering, collusion, Sybil attacks—and show that none can increase overlap probability beyond what storage density allows. From a mechanism-design perspective, repeated suffix-walk interactions induce a game where aligned storage is the dominant strategy, enabling emergent consensus without central coordination. The protocol serves as a foundation for proof-of-aligned-storage in distributed systems and provides consensus weight based on demonstrated storage rather than computational power or stake. Throughout this paper, "proof" refers to statistical evidence under a well-validated probabilistic model, not a cryptographic zero-knowledge proof.
Over the past two years, Hong Kong hasn’t just talked about Web3 transformation — it has executed it. A sequenced rollout of real policies. A clear regulatory masterplan. A vision anchored in innovation and investor protection. Today, Hong Kong is emerging as one of the world’s most credible and forward-looking regulated digital asset hubs. In my latest article, I break down how the SFC’s A-S-P-I-Re Roadmap, new licensing frameworks, custody standards, staking regulations, and tokenisation initiatives are reshaping the entire virtual asset landscape across 2024–2025. This is not just regulatory evolution — it’s regulatory engineering. 🔍 Inside the article: • The real meaning of “same activity, same risk, same regulation” • How reforms are raising the bar for VATPs and market integrity • Why Hong Kong’s digital asset roadmap is now a global reference point • The rise of institutional-grade custody + cybersecurity requirements • The strategic push behind Project Ensemble and tokenised finance • How collaboration between the SFC, HKMA, and industry is driving safe innovation Hong Kong’s approach shows that a digital asset market can be innovative, resilient, and globally aligned — all at once.
The rapid development of Internet of Things (IoT) technology has led to growing concerns about data security and user privacy in the interactions within distributed systems. Decentralized Applications (DApps) in distributed systems consist of on-chain and off-chain functions, where on-chain functions are smart contracts running in the blockchain network, while off-chain functions operate outside the blockchain. Since smart contracts cannot access off-chain information, they cannot verify whether the off-chain functions, i.e. the software components, they interact with have been tampered or not. As a result, establishing mutual trust between the on-chain smart contracts and the off-chain functions remains a significant challenge. To address the challenge, this paper introduces TeeMAF, a generic framework for mutual attestation between on-chain and off-chain functions, leveraging Trusted Execution Environments (TEE), specifically Intel Software Guard Extensions (SGX), SCONE (a TEE container on top of Intel SGX), and remote attestation technologies. This ensures that the deployed off-chain functions of a DApp execute in a provably secure computing environment and achieve mutual attestation with the interacting on-chain functions. Through a security analysis of TeeMAF, the reliability of deployed DApps can be verified, ensuring their correct execution. Furthermore, based on this framework, this paper proposes a decentralized resource orchestration platform (a specific DApp) for deploying applications over untrusted environments. The system is implemented on Ethereum and benchmarked using Hyperledger Caliper. Performance evaluation focusing on throughput and latency demonstrates that, compared to platforms without a mutual attestation scheme, the performance overhead remains within an acceptable range.
We estimate risk premia in the cross-section of cryptocurrency returns using the Giglio-Xiu (2021) three-pass approach, allowing for omitted latent factors alongside observed stock-market and crypto-market factors. Using weekly data on a broad universe of large cryptocurrencies, we find that crypto expected returns load on both crypto-specific factors and selected equity-industry factors associated with technology and profitability, consistent with increased integration between crypto and traditional markets. In addition, we study non-tradable state variables capturing investor sentiment (Fear and Greed), speculative rotation (Altcoin Season Index), and security shocks (hacked value scaled by market capitalization), which are new to the literature. Relative to conventional Fama-MacBeth estimates, the latent-factor approach yields materially different premia for key factors, highlighting the importance of controlling for unobserved risks in crypto asset pricing.
Consensus protocols are crucial for a blockchain system as they are what allow agreement between the system's nodes in a potentially adversarial environment. For this reason, it is paramount to ensure their correct design and implementation to prevent such adversaries from carrying out malicious behaviour. Formal verification allows us to ensure the correctness of such protocols, but requires high levels of effort and expertise to carry out and thus is often omitted in the development process. In this paper, we present IsabeLLM, a tool that integrates the proof assistant Isabelle with a Large Language Model to assist and automate proofs. We demonstrate the effectiveness of IsabeLLM by using it to develop a novel model of Bitcoin's Proof of Work consensus protocol and verify its correctness. We use the DeepSeek R1 API for this demonstration and found that we were able to generate correct proofs for each of the non-trivial lemmas present in the verification.
This paper documents the installation, configuration, and operation of a full Bitcoin node in a Linux environment, from manual compilation of the source code to complete synchronization with the network. The technical phases of the process are described, the main files generated by Bitcoin Core are analyzed, and the effects of the parameters txindex, prune, dbcache, maxmempool, and maxconnections are empirically studied. System resources during the block download (IBD) mechanism are also documented, and the operational importance of each resource is explained. This paper provides a solid foundation for future research proposals on Bitcoin node performance or for the development of blockchain data query tools.