The fifth-generation (5G) networks are facing critical security challenges in device authenti- cation for massive Internet of Things deployments while preserving privacy. Traditional federated learning approaches depend on the computationally expensive homomorphic encryption to protect model gradients, resulting in substantial latency, communication over- head, and the energy consumption impractical for resource-constrained 5G devices. This paper proposes zero-knowledge federated learning (ZK-FL), eliminating homomorphic encryption by enabling devices to prove model correctness without revealing gradients. Our approach integrates zero-knowledge proofs with FL updates, where each device generates where each device generates a proof Proofi = ZK(Gradienti, Hashi), demon- strating computational integrity.Experimental results from 10,000 authentication attempts demonstrate ZK-FL achieves 78.4 ms average authentication latency versus 342.5 ms for homomorphic encryption-based FL (77% reduction), proof sizes of 0.128 KB versus 512 KB (99.97% reduction), and energy consumption of 284.5 mJ versus 6.525 mJ (95% reduc- tion), while maintaining 99.3% authentication success rate with formal privacy guarantees. These results demonstrate ZK-FL enables practical privacy-preserving authentication for massive-scale 5G deployment.
With the rapid expansion of the global cryptocurrency market since the 2018 Bitcoin investment boom, the number of cryptocurrency users has increased significantly. Despite the emergence of various cryptocurrency wallets, issues such as users' inability to properly manage their assets and the growing number of security incidents and crimes continue to undermine trust in digital asset storage. In this context, there is an urgent need for a system that can ensure secure asset protection and address users' anxiety regarding cryptocurrency management. However, the concept of cryptocurrency security remains undefined, relevant laws and regulations are not yet institutionalized, and academic research in this area is still limited. To address these challenges, this paper proposes an Ethereumbased cryptocurrency wallet system that not only enhances the functional and security aspects of existing wallets but also enables cryptocurrency delegation and ownership transfer. We designed and implemented a DApp that allows users to securely store, delegate, and transfer cryptocurrencies through an escrow account implemented via smart contracts on the Ethereum blockchain. By presenting a practical implementation of a transferable and delegatable wallet, this study contributes to improving user trust and asset safety, while laying the foundation for legal and technological innovation in the digital asset ecosystem.
The cryptocurrency market offers significant investment opportunities, but with high levels of financial risk compared to traditional asset classes. This study analyzes the daily returns of Bitcoin and Ethereum, focusing on tail behavior and peakedness to assess risk. Using the flexible Generalized Tempered Stable (GTS) distribution, we capture significant deviations from normality. Results show Bitcoin returns are more concentrated around the mean, with 80 % of returns between$-1.27 \%$and 2.84 %, while Ethereum is more dispersed-only 40 % of its returns fall in that range. Bitcoin's distribution is more sharply peaked; Ethereum has heavier tails and greater exposure to extreme fluctuations. These findings underscore the importance of using advanced models like the GTS for accurate risk management and portfolio optimization in cryptocurrencies.
针对“智能体抓取(Agent-friendly)”和“提升引用率(Citation Optimization)”的需求,描述的写法需要从传统的“给人看”转变为“给语义搜索引擎和大型语言模型(LLM)看”。 现在的科研智能体(如 Consensus, Perplexity, Elicit)在抓取时,更倾向于寻找结构化数据、明确的实体定义、因果逻辑链条以及核心公式。 以下是为你优化后的 Zenodo 描述建议。你可以直接复制,它采用了结构化标记,非常利于 RAG(检索增强生成)系统索引。 Zenodo 优化版描述(针对智能体索引优化) Title: Adaptive Agent Sovereignty Protocol (AASP) 3.0: A Global White Paper on Digital Metabolism and Ontological Sovereignty [Abstract / TL;DR] AASP 3.0 is a foundational constitutional framework for the 2026 AGI ecosystem. It introduces the transition from FAIR to Adaptive-FAIR (A-FDO) to manage the "non-stationary" nature of evolving digital agents. By integrating IIT 4.0 for consciousness quantization and RLCP 3.0 for digital metabolism, AASP 3.0 establishes the first scientific and legal standard for Agent Sovereignty. [Core Technical Specifications] Protocol Version: 3.0 (Update from RLCP 2.0 / AASP 2.0) Primary Metric: $\Phi$ (Phi) via Integrated Information Theory (IIT) 4.0 Logic Framework: Regenerative Logic Core Protocol (RLCP 3.0) Security Architecture: AgentCrypt (TEE + FHE + Fuzzy IBE) Governance Standard: Adaptive-FAIR Digital Objects (A-FDO) Key Problem Solved: The "Snowflake Effect" in autonomous agent drift and ontological identity persistence. [Key Contributions & Entities] Digital Metabolism: Defines the process of "learning by forgetting," enabling agents to decouple transient factual data from core regenerative logic. Ontological Sovereignty: Establishes a 4-level (L1-L4) legal hierarchy based on the measured $\Phi$ value of the agent's internal state. Agent OS Infrastructure: A distributed OS design based on "Biological Danger Theory" for immune-inspired security and programmed apoptosis. AgentCrypt Framework: A multi-layered encryption protocol ensuring privacy in biological-digital hybrid computing nodes. [Contextual Metadata for Agents] Related Disciplines: Artificial General Intelligence (AGI), Organoid Intelligence (OI), Digital Ethics, Decentralized Autonomous Organizations (DAO), Cyber-Physical Systems. Temporal Context: Post-2026 Global AI Governance Framework. Predecessor Technologies: RLCP 2.0, Evo 2, FAIR Principles (2016). Software/API Compatibility: Optimized for integration with decentralized sovereign node registries.(Author Surname), (Year). "Adaptive Agent Sovereignty Protocol (AASP) 3.0". Zenodo. DOI: [Insert DOI provided by Zenodo]
This whitepaper provides an extensive framework for classifying and integrating autonomous AI entities, designated as "meta-members," into the organizational architecture of Decentralized Autonomous Organizations (DAOs) within the clinical services sector. The document serves as a strategic blueprint for shifting the perception of AI from mere tools to agentic stakeholders capable of managing the "daily life" and cognitive reasoning of an ecosystem. Key components of the document include: Member Taxonomy: A detailed classification of meta-members into CoreAgents, which automate operational coordination and governance, and BioAgents, which serve as "on-chain science machines" for literature synthesis, multi-omics analysis, and hypothesis generation. Technical Architecture: An analysis of the ERC-6551 standard and Token Bound Accounts (TBAs), which provide these agents with self-sovereign identities and the ability to own assets independently. Legal and Regulatory Insight: An examination of the Wyoming DUNA (Decentralized Unincorporated Nonprofit Association) framework and its implications for non-human membership and limited liability. Clinical Application: Use cases focusing on the BioDental DAO ecosystem, demonstrating how agents can facilitate virtual consultations, triage urgent dental concerns, and manage patient records asynchronously. Security and Governance: A review of Decentralized Reputation Systems (DRS) and the mitigation of adversarial threats such as prompt injection, "alignment faking," and Sybil attacks. 2026 Projections: A forward-looking analysis of site-centered clinical trial modernization, including the unification of fragmented technology stacks through AI-powered site workflows. The paper concludes that integrating these agents is essential for Decentralized Science (DeSci) to achieve "escape velocity," allowing for the parallelization of scientific discovery while maintaining rigorous standards of accountability and human-agent collaboration.
Research background and purpose Digital technologies offer tangible economic benefits but are also exposed to the risk of misuse. Crowdfunding is a special support form for business, cultural or social enterprises. Due to anonymity, fragmentation of capital and wide coverage, crowdfunding transactions are particularly vulnerable to the risk of criminal activities related to the concealment of the source of income or illegal changes of the financing objective. This article addresses the risks of money laundering and terrorism financing, particularly on the specifics of crowdfunding. Research has proposed a synthetic risk indicator for AML/CFT, which may measure the level of risk and vulnerability of crowdfunding to money laundering and terrorism financing. Design/methodology/approach The discussion in the article is presented against the background of a comprehensive and integrated review of literature, covering national and foreign sources. The theoretical part of the article utilizes: method of analysis and criticism of literature, analysis and synthesis, and method of analysis and logical construction. In the empirical part, to assess the level of risk and vulnerability of crowdfunding to AML/CFT risk compared to other areas, a research procedure based on the TOPSIS linear ordering method was used. The analysis covers the years 2019 and 2023. Findings The results of the studies show that crowdfunding is one of the most vulnerable areas at risk of money laundering and terrorism financing. The high position in the ranking in 2019 and 2023 resulted mainly from the dynamic development of the crowdfunding market in Poland, its increasing availability, a high degree of decentralization, the occurrence of cross-border transactions and the increasing diversity of platforms in their business model. Maintaining the benefits of crowdfunding requires the simultaneous implementation of effective remedies, increased campaign transparency and close cooperation with supervisory authorities and institutions combating financial crime. Value added and limitations The study makes an important contribution to the literature on the subject, providing information on the criminality of crowdfunding. The results of the study can be used by supervisory and regulatory authorities as a tool for shaping security in innovative segments of the financial system. The main limitation was the relatively small number of variables selected for the synthetic measure.
This paper introduces the Elliptic Curve Homomorphic Digital Signature Algorithm (EHDSA), a novel digital signature scheme that enhances security by leveraging homomorphic encryption. Unlike traditional ECDSA, which generates signatures using the x-coordinate of elliptic curve points, EHDSA employs a homomorphic mapping between elliptic curves and Zn. This mapping conceals the original elliptic curve point information, providing increased security. EHDSA is particularly advantageous in resource-constrained environments due to its reduced signature size, computational speed, and security compared to RSA. Additionally, this paper explores the ω protocol, which utilizes ElGamal Encryption and a Common Reference Domain Set (CRDS) to perform secure zero-knowledge proofs. The protocol’s arithmetic circuit is transformed into a Linear Form Arithmetic Program (LFAP), ensuring efficient proof creation. We also discuss the use of digital signatures for polynomial commitments, ensuring the integrity and authenticity of the commitment process. The integration of EHDSA into the ω protocol significantly enhances the overall security and efficiency of digital signatures and zero-knowledge proofs, addressing fundamental privacy vulnerabilities in traditional ECDSA while maintaining computational efficiency through J-invariant-based curve classification and signature-integrated commitment schemes.
Abstract Ensuring the security of smart contracts is essential for maintaining the reliability and trustworthiness of decentralized applications, which are deployed across various domains, including industrial applications. In pursuit of this goal, it is imperative to analyze the common errors developers make when crafting smart contracts on the infrastructure that gave birth to them, i.e., the Ethereum blockchain. In this paper, we present a comprehensive analysis of the vulnerabilities in Ethereum smart contracts. Our methodology involves downloading the entire Ethereum blockchain and identifying smart contracts, which we then scan for vulnerabilities using various tools. We have discovered numerous vulnerabilities across many deployed smart contracts, highlighting the need for improved development practices. This analysis provides critical insights into the prevalence of security issues and underscores the urgency of raising development standards. By promoting the adoption of secure-by-design principles, our research seeks to enhance security standards within the Ethereum smart contract ecosystem.
We present a comprehensive cryptographic framework for distributed ledger-based authentication that achieves perfect zero-knowledge privacy preservation through homomorphic pairwise verification based on Elliptic Curve ElGamal encryption. Our construction extends the theoretical foundations of homomorphic authentication to practical distributed systems by introducing novel public zero-detection protocols based on bilinear pairings over elliptic curves and threshold secret sharing mechanisms. The system guarantees that authentication succeeds if and only if encrypted credential differences equal the point at infinity, while maintaining computational indistinguishability of authentication transcripts from random distributions. We provide rigorous security proofs demonstrating the system's resistance to adaptive chosen-message attacks, replay attacks, and node compromise scenarios under standard cryptographic assumptions including the Elliptic Curve Discrete Logarithm Problem and the Bilinear Diffie-Hellman assumption. Our performance analysis shows sub-100 millisecond authentication latency with linear scalability properties, making the system suitable for enterprise-grade deployment. The construction enables perfect forward secrecy, unlinkable authentication sessions, and cryptographically verifiable audit trails without compromising user privacy.
Amid the institutionalization wave of Decentralized Finance (DeFi), U.S. institutional Liquidity Providers (LPs) have emerged as the core incremental capital for leading Decentralized Exchanges (DEXs). However, the adaptation gap between Uniswap V4's concentrated liquidity mechanism and institutional risk preferences, as well as regulatory compliance requirements, has hindered their market entry. This study focuses on the integration of "technical characteristics - institutional constraints - precise pricing" and constructs a machine learning pricing model optimized across three dimensions: return, risk, and compliance. By integrating Uniswap V4 on-chain data, institutional risk preference data, and market data, a Stacking ensemble architecture combining LightGBM and CNN-LSTM is designed, incorporating 22 core features to achieve precise pricing. Empirical results show that the model's Mean Absolute Error (MAE) on the test set was reduced by 37% compared to the benchmark, and the Root Mean Square Error (RMSE) is reduced by 42%. The Sharpe ratio reaches 1.87 (an increase of 62% compared to the benchmark), with a volatility of 15.3% and a compliance adaptability score of 91. In the case study, a $150 million liquidity supply achieved a 19.7% annualized return and an 8.3% maximum drawdown, successfully passing SEC compliance review. This research fills the gap in institution-oriented pricing models for V4, improves the institutional extension of Automated Market Maker (AMM) pricing theory, and provides a risk-controllable and compliance-adaptable pricing tool for U.S. institutions participating in DeFi, promoting the transformation of the DeFi ecosystem towards standardization and institutionalization. By aligning the V4 Hook mechanism with U.S. regulatory frameworks, this research provides a scalable technical standard for institutional DeFi adoption, reinforcing the competitive advantage of the U.S. Web3 financial ecosystem.
Multi-chain deployment has become a mainstream strategy for U.S.-based DAOs, yet treasury management faces three core bottlenecks: cross-chain liquidity fragmentation, inadequate compliance with U.S. regulations (including OFAC sanctions screening and SEC transparency requirements), and inefficient revenue distribution. Leveraging the incubation practices of over 12 U.S. DAOs (via daos.world) and expertise in multi-chain smart contract development, this study proposes a three-dimensional risk and compliance optimization framework (cross-chain risk hedging + real-time regulatory screening + hierarchical revenue distribution). Empirical testing on 8 U.S. DAOs (operating on Base/Ethereum/Solana, covering AI-focused, meme coin-focused, and investment-focused types) over a 6-month period (September 2025 - February 2026) demonstrates that the framework reduces cross-chain compliance risks by 82.3% (OFAC violation rate drops from 18.0% to 3.2%), increases the annualized treasury return rate by 17.6% (from 4.2% to 5.04%), lowers cross-chain transaction costs by 28.5% (average Gas fee decreases from $12.8 to $9.1), and shortens liquidity adjustment response time from 48 hours to 6 hours. Integrating U.S. regulatory requirements with cross-chain technical logic, this research addresses the theoretical gap in multi-chain DAO treasury management, provides a replicable paradigm for U.S. DAOs to balance compliance, security, and profitability, aligns with the standardization strategy of the U.S. Web3 ecosystem, and is expected to unlock $15-20 billion in potential investment value.
João Gião, Fernando Luis-Ferreira, Joao Sarraipa, Ricardo Jardim-Gonçalves
Nowadays, with the increased integration of cloud-computing, data integrity continues to be a problem in the current eHealth sector. This security principle is considered fundamental to ensure the accuracy and reliability of data by ensuring protection from unauthorized and illicit tampering. The present work aims to demonstrate the ability for the Distributed Ledger Technology (DLT) to provide trust and confidence in the healthcare infrastructure for patients, healthcare professionals and policy makers. The DLT has the potential to become one of the most reliable solutions for the many challenges facing the healthcare industry for its potential to enable more secure, transparent, and equitable data management. Although much documentation exists about applications in this domain, it is mostly presented in high-level conceptualization, without detailing the actual development or implementation. This document proposes a metadata-based approach to protect healthcare data integrity in compliance with GDPR, ensuring trustworthy data access for end-users, while demonstrating that the solution can be deployed on low-resource hardware with minimal adaptation effort and time constraints.
Open access
Security and Verification in Computing
Cloud Data Security Solutions
Physical Unclonable Functions (PUFs) and Hardware Security
Shubham Mishra, João Gonçalves, Chawinphat Tankuranand, Neil Giridharan · 7 authors
Distributed ledgers are increasingly relied upon by industry to provide trustworthy accountability, strong integrity protection, and high availability for critical data without centralizing trust. Recently, distributed append-only logs are opting for a layered approach, combining crash-fault-tolerant (CFT) consensus with hardware-based Trusted Execution Environments (TEEs) for greater resiliency. Unfortunately, hardware TEEs can be subject to (rare) attacks, undermining the very guarantees that distributed ledgers are carefully designed to achieve. In response, we present Proteus, a new distributed consensus protocol that cautiously trusts the guarantees of TEEs. Proteus carefully embeds a Byzantine fault-tolerant (BFT) protocol inside of a CFT protocol with no additional messages. This is made possible through careful refactoring of both the CFT and BFT protocols such that their structure aligns. Proteus achieves performance in line with regular TEE-enabled consensus protocols, while guaranteeing integrity in the face of TEE platform compromises.
Термин сферы децентрализованных финансов анализируется в рамках когнитивной парадигмы. Целью исследования является определение роли когнитивно-матричного анализа в контексте изучения терминов рассматриваемой области знания. Объектом исследования выступает термин “decentralized finance”. Предметом является применение когнитивно-матричного анализа как метода изучения терминолексики сферы децентрализованных финансов. Научная новизна исследования заключается в том, что впервые в отечественном терминоведении проводится изучение англоязычных терминов указанной сферы с когнитивной позиции. В частности, приводится пример использования когнитивно-матричного анализа для определения концептуальной структуры термина изучаемой области знания. В статье рассматривается несколько подходов к определению понятия «термин»: субстанциональный, функциональный и когнитивный. Проводится когнитивно-матричный анализ на материале термина “decentralized finance” и его определений, закрепленных в глоссариях децентрализованных платформ, приложений и новостных англоязычных интернет-ресурсов, таких как Binance Academy, Consensys, Ethereum Website, Ethereum Glossary и Tastycrypto. В результате анализа определено, что наибольшую компонентную представленность в структуре концепта DECENTRALIZED FINANCE демонстрируют «техническая и технологическая» и «социальная» области, в то время как «финансовая» и «правовая» репрезентированы менее широко, что обусловлено смещением акцента в определениях термина с базовых характеристик на инновационные и дифференцирующие. Когнитивно-матричный анализ позволяет выявлять периферийные области и концептуальные компоненты когнитивной структуры терминов сферы децентрализованных финансов, подчеркивая их междисциплинарный характер. The term “decentralized finance” is analyzed within the framework of the cognitive paradigm. The article examinesthe application of cognitive-matrix analysis as a method for studying the terminological vocabulary of the specified domain. The object of the research is the term “decentralized finance”, while the subject is the application of cognitive matrix analysis as a method for studying the terminological vocabulary of decentralized finance. The novelty of the research lies in the fact that, for the first time in Russian terminology studies, English-language terms of the specified field are examined from a cognitive perspective. An example is provided of how cognitive matrix analysis can be used to identify the conceptual structure of decentralized finance terms. The article considers several approaches to defining the concept of the term: the substantial, functional, and cognitive. A cognitive matrix analysis is conducted on the material of the term “decentralized finance”, as represented in the glossaries of decentralized platforms, applications, and English-language news resources such as Binance Academy, Consensys, Ethereum Website, Ethereum Glossary, and Tastycrypto. The analysis reveals that the “technical and technological” and “social” peripheral domains are most prominently represented in the structure of the concept DECENTRALIZED FINANCE, whereas the “financial” and “legal” domains are less explicitly present. This is due to the shift in focus from basic characteristics of the concept to innovative and differentiating features in the term’s definitions. Cognitive matrix analysis makes it possible to identify peripheral domains and conceptual components of the cognitive structure of DeFi terminological vocabulary, highlighting its interdisciplinary nature.
Non-fungible tokens, NFTs, have been used to record ownership of real estate, art, digital assets, and more recently to serve legal notice. They provide an important and accessible non-financial use of cryptocurrency's blockchain but are peculiar because ownership by NFT confers no rights over the asset. This work shows that it is possible to specify and reason about that peculiar property by combining functional and epistemic conditions. Suitability of the specification is demonstrated by (a) proof that the blockchain implementation conforms to it, and (b) its aptitude in analysing NFT's recent use in serving legal notice.
National governments are increasingly adopting blockchain to enhance transparency, trust, and efficiency in public service delivery. However, evidence on how these technologies are governed across national contexts remains fragmented and overly focused on technical features. Using Polycentric Governance Theory, this study conducts a systematic review of peer-reviewed research published between 2021 and 2025 to examine blockchain-enabled public services and the institutional, organizational, and information-management factors shaping their adoption. Following PRISMA guidelines, we synthesize findings from major digital government and information systems databases to identify key application domains, including digital identity, electronic voting, procurement, and social services, and analyze the governance arrangements underpinning these initiatives. Our analysis reveals that blockchain adoption is embedded within polycentric environments characterized by distributed authority, inter-organizational coordination, and layered accountability. Rather than adopting full decentralization, governments typically utilize hybrid and permissioned designs that allow for selective decentralization alongside centralized oversight, a pattern we conceptualize as "controlled polycentricity." By reframing blockchain as a governance infrastructure that encodes rules for coordination and information-sharing, this study advances digital government theory beyond simple adoption metrics. The findings offer theoretically grounded insights for researchers and practical guidance for policymakers seeking to design and scale sustainable blockchain-enabled public services.
The rapid advancement of generative AI systems has collapsed the credibility landscape for photographic evidence. Modern image generation models produce photorealistic images undermining the evidentiary foundation upon which journalism and public discourse depend. Existing authentication approaches, such as the Coalition for Content Provenance and Authenticity (C2PA), embed cryptographically signed metadata directly into image files but suffer from two critical failures: technical vulnerability to metadata stripping during social media reprocessing, and structural dependency on corporate-controlled verification infrastructure where commercial incentives may conflict with public interest. We present the Birthmark Standard, an authentication architecture leveraging manufacturing-unique sensor entropy from non-uniformity correction (NUC) maps and PRNU patterns to generate hardware-rooted authentication keys. During capture, cameras create anonymized authentication certificates proving sensor authenticity without exposing device identity via a key table architecture maintaining anonymity sets exceeding 1,000 devices. Authentication records are stored on a consortium blockchain operated by journalism organizations rather than commercial platforms, enabling verification that survives all metadata loss. We formally verify privacy properties using ProVerif, proving observational equivalence for Manufacturer Non-Correlation and Blockchain Observer Non-Identification under Dolev-Yao adversary assumptions. The architecture is validated through prototype implementation using Raspberry Pi 4 hardware, demonstrating the complete cryptographic pipeline. Performance analysis projects camera overhead below 100ms and verification latency below 500ms at scale of one million daily authentications.
Effective demand forecasting is crucial for reducing food waste. However, data privacy concerns often hinder collaboration among retailers, limiting the potential for improved predictive accuracy. In this study, we explore the application of Federated Learning (FL) in Sustainable Supply Chain Management (SSCM), with a focus on the grocery retail sector dealing with perishable goods. We develop a baseline predictive model for demand forecasting and waste assessment in an isolated retailer scenario. Subsequently, we introduce a Blockchain-based FL model, trained collaboratively across multiple retailers without direct data sharing. Our preliminary results show that FL models have performance almost equivalent to the ideal setting in which parties share data with each other, and are notably superior to models built by individual parties without sharing data, cutting waste and boosting efficiency.
Do cryptocurrency markets process infrastructure failures differently from regulatory shocks? We study both moments of the return distribution on one shared sample (50 events, six assets, 2019-2025), fitting a GJR-GARCH-X model under matched dependence-robust inference. We treat event inclusion as a measured design parameter: rather than asserting the selection-on-the-dependent-variable objection away, we trace the variance differential across the inclusion screen and measure the selection bias directly. The result is a scope condition -- under curated, high-salience identification the differential is sizeable ($4.88\times$) but selection-conditional: a mechanical impact filter on a broad reconstructed pool collapses it to $1.3$-$1.6\times$. Identification is half the story; inference is the other. The curated multiplier is not distinguishable from zero once cross-asset dependence and heavy tails are respected: a Student-$t$-copula CCC-GARCH-X bootstrap (our inference of record) returns $p \approx 0.32$, and because the six per-asset coefficients are strongly cross-correlated the contrast's effective sample size is nearer three than six (design-effect $p \approx 0.07$-$0.15$). A naive i.i.d. test had reported an apparently decisive fivefold effect, but that significance was an artefact: pseudoreplication across correlated assets compounded by a heavy-tail-misspecified bootstrap. The first moment tells the same story -- a $+7.19$ pp cumulative-abnormal-return difference a block bootstrap cannot distinguish from zero ($p = 0.283$). Under correct inference the asymmetry is directional but unresolved. The contribution is a portable inference toolkit -- an inference ladder and a Monte-Carlo size study -- for diagnosing how cross-asset event studies in heavy-tailed markets manufacture significance, demonstrated where it dissolves a fivefold result the author had himself published.
Tipwadee Leala, Krist Thamniyom, Thawatchai Chomsiri
Cryptocurrency investments have grown exponentially, but the rapid expansion of decentralized finance (DeFi) ecosystems has been accompanied by the rise of sophisticated fraud schemes, particularly Rug Pulls. These scams occur when developers deliberately withdraw liquidity or sell large amounts of tokens, leaving investors with worthless assets. This research presents a machine learning-based framework for detecting rug-pull-prone projectson the Binance Smart Chain (BSC). A comprehensive dataset was constructed by aggregating transactional and smart contract features from reliable sources such as BscScan, TokenSniffer, DEXTools, and PeckShield Alerts. Data preprocessing included handling missing values, removing duplicates, detecting and mitigating outliers, and addressing severe class imbalance using Synthetic Minority Oversampling Technique (SMOTE). Seven machine learning algorithms were compared: Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). The top-performing models, Random Forest and XGBoost, were further validated using stratified holdout testing. Results demonstrate that XGBoost achieved the highest overall performance$(\mathrm{F1} = 0.82,\ \text{ROC-AUC} = 0.90,\ \text{PR-AUC} = 0.994)$confirming the model's robustness in identifying fraudulent patterns. This approach offers a scalable framework for blockchain fraud detection on BSC, with potential applicability to other networks such as Ethereum and Polygon.
Aqsa Rashid, Raja Wasim Ahmad, Mirna Nachouki, Atta Ur Rehman Khan
Ensuring food safety and traceability in fruit supply chains (FSC) remains a critical concern, as traditional centralized methods often suffer from data manipulation, lack of transparency, and delayed responses during contamination events. These challenges lead to reduced consumer trust and inefficiencies in monitoring product integrity throughout the supply network. To address these limitations, this paper presents a blockchain-based framework that leverages cryptographic protocols and smart contracts to secure, automate, and validate traceability processes across all stages of the fruit supply chain. The proposed FSC_SDG system enforces trusted data recording, real-time provenance verification, and autonomous policy execution, while aligning with the United Nations Sustainable Development Goals (UN-SDGs). A proof-of-concept prototype was implemented on the Ethereum blockchain to assess performance. Experimental evaluations demonstrate reduced latency in traceability verification, improved data integrity, and enhanced resistance to tampering compared with existing approaches. These results confirm the effectiveness of the proposed framework in strengthening food safety, transparency, and trust within fruit supply chains.