У статті досліджується Інтернет 3.0 як децентралізована, енергетично незалежна, штучно-інтелектуальна інфраструктура, що формується на перетині Web3, блокчейн-економіки, ядерної енергетики нового покоління та пост-сингулярних технологій. Аналізуються економічні, організаційні та правові аспекти переходу України до Інтернету 3.0 в умовах післявоєнної реконструкції та наближення до часу інтегрованої сингулярності. Особливу увагу приділено ризику «інтелектуального захоплення» – ситуації, коли технологічна сингулярність може привласнювати права людини на ідеї та інтелектуальний внесок. Автор обґрунтовує необхідність створення системи юридичної та фізичної фіксації і капіталізації прав людини на ідеї, висновки, розробки замість їх захоплення технологіями. Запропоновано концепцію «Інтернету свідомості» як децентралізованої мережі персоніфікованих цифрових двійників, де внесок кожної людини токенізується та захищається через блокчейн. Обґрунтовано, що поєднання енергетично та комунікаційно незалежних дата-центрів на базі малих модульних реакторів, національного блокчейну та Інтернету 3.0 надає Україні можливість стати частиною глобальної інфраструктури накопичення та примноження персоніфікованого інтелектуального капіталу.
Zhuolun Xiang, Andrei Tonkikh, Alexander Spiegelman
Despite broad adoption of BFT consensus in blockchains, censorship resistance remains weak: existing designs offer limited inclusion guarantees and allow leaders to exclude transactions. We address this with a new abstraction and protocol stack. We define \emph{Prefix Consensus}, where parties input vectors and output two consistent vectors $(v^{\sf low},v^{\sf high})$ that extend the maximum common prefix of honest inputs and satisfy $v_i^{\sf low}\preceq v_j^{\sf high}$ for all honest parties $i,j$. We show that Prefix Consensus is solvable asynchronously and establish tight round-complexity bounds. We then define \emph{Strong Prefix Consensus}, which additionally requires agreement on the high output, and give a leaderless partially synchronous protocol. Using its accountable variant, we build a leaderless, multi-proposer, censorship-resistant BFT SMR protocol with amortized four-round commit latency under synchronized starts, while guaranteeing that after GST at most $f$ slots can be censored. Finally, we connect Prefix Consensus to graded consensus, obtaining a matching lower bound and a 3-round protocol, and derive leaderless Binary Consensus with improved worst-case complexity.
Visual social media platforms have become primary venues for political discourse, yet we know little about how moralization operates differently across platforms and topics. Analyzing 2,027,595 TikToks and 1,126,972 Instagram posts during the 2024 US presidential election, we demonstrate that issues are not necessarily inherently moralized, but a product of audience demographics, platform architecture, and partisan framing. Using temporal supply-demand analysis and moral foundations scoring (eMFD), we examine the dynamics of key electoral issues. Three key findings emerge. First, moralization patterns diverge dramatically by platform: TikTok's algorithm enabled viral spread of moralized abortion and immigration content despite lower supply, while Instagram amplified economic discourse that aligned supply and demand. Second, traditionally "pragmatic" economic issues became moralized-cryptocurrency discourse invoked loyalty and authority foundations more strongly than any other topic, framing regulation as government overreach. Third, platforms responded to different events: TikTok surged after Harris's nomination across all topics (96% reduction in supply volatility), while Instagram spiked around cryptocurrency policy developments. Semantic network analysis reveals TikTok's circular topology enables cross-cutting exposure while Instagram's fragmented structure isolates Harris from economic discourse. These findings demonstrate that understanding political moralization requires examining platform-specific ecosystems where architecture, demographics, and content strategy interact to determine which issues get moralized and how moral content spreads.
The rapid advancement of artificial intelligence has made the generation of synthetic images widely accessible, increasing concerns related to misinformation, digital forgery, and content authenticity on large-scale online platforms. This paper proposes a blockchain-backed framework for verifying AI-generated images through a registry-based provenance mechanism. Each AI-generated image is assigned a digital fingerprint that preserves similarity using perceptual hashing and is registered at creation time by participating generation platforms. The hashes are stored on a hybrid on-chain/off-chain public blockchain using a Merkle Patricia Trie for tamper-resistant storage (on-chain) and a Burkhard-Keller tree (off-chain) to enable efficient similarity search over large image registries. Verification is performed when images are re-uploaded to digital platforms such as social media services, enabling identification of previously registered AI-generated images even after benign transformations or partial modifications. The proposed system does not aim to universally detect all synthetic images, but instead focuses on verifying the provenance of AI-generated content that has been registered at creation time. By design, this approach complements existing watermarking and learning-based detection methods, providing a platform-agnostic, tamper-proof mechanism for scalable content provenance and authenticity verification at the point of large-scale online distribution.
The digitization of healthcare has generated massive volumes of Electronic Health Records (EHRs), offering unprecedented opportunities for training Artificial Intelligence (AI) models. However, stringent privacy regulations such as GDPR and HIPAA have created data silos that prevent centralized training. Federated Learning (FL) has emerged as a promising solution that enables collaborative model training without sharing raw patient data. Despite its potential, FL remains vulnerable to poisoning and Sybil attacks, in which malicious participants corrupt the global model or infiltrate the network using fake identities. While recent approaches integrate Blockchain technology for auditability, they predominantly rely on probabilistic reputation systems rather than robust cryptographic identity verification. This paper proposes a Trustworthy Blockchain-based Federated Learning (TBFL) framework integrating Self-Sovereign Identity (SSI) standards. By leveraging Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs), our architecture ensures only authenticated healthcare entities contribute to the global model. Through comprehensive evaluation using the MIMIC-IV dataset, we demonstrate that anchoring trust in cryptographic identity verification rather than behavioral patterns significantly mitigates security risks while maintaining clinical utility. Our results show the framework successfully neutralizes 100% of Sybil attacks, achieves robust predictive performance (AUC = 0.954, Recall = 0.890), and introduces negligible computational overhead (<0.12%). The approach provides a secure, scalable, and economically viable ecosystem for inter-institutional health data collaboration, with total operational costs of approximately $18 for 100 training rounds across multiple institutions.
Consent is an ethical cornerstone of clinical research and healthcare in general. Although the ethical principles of consent - providing information, ensuring comprehension, and ensuring voluntariness - are well-defined, the technological infrastructure remains outdated. Clinicians are responsible for obtaining informed consent from research subjects or patients, and for managing it before, during, and after clinical trials or care, which is a burden for them. The voluntary nature of participating in clinical research or undergoing medical treatment implies the need for a participant-centric consent management system. However, this is not reflected in most established systems. Not only do most healthcare information systems not follow a user-centric model, but they also create data silos, which significantly reduce the mobility of patient data between different healthcare institutions and impact personalized medicine. Furthermore, consent management tools are outdated. We propose ClinConNet (Clinical Consent Network), a platform that connects researchers and participants based on clinical research projects. ClinConNet is powered by a dynamic consent model based on blockchain and take advantage of dynamic consent interfaces, as well as blockchain and Self-Sovereign Identity systems. ClinConNet is user-centric and provides important privacy features for patients, such as unlinkability, confidentiality, and ownership of identity data. It is also compatible with the right to be forgotten, as defined in many personal data protection regulations, such as the GDPR. We provide a detailed privacy and security analysis in an adversarial model, as well as a Proof of Concept implementation with detailed performance measures that demonstrate the feasibility of our blockchain-based consent management system with a median end-to-end consent establishment time of under 200ms and a throughput of 250TPS.
Andrey Chursin, Lefteris Kokoris-Kogias, Alex Orlov, Alberto Sonnino · 5 authors
Log-Structured Merge-Trees (LSM-trees) dominate persistent key-value storage but suffer from high write amplification from 10x to 30x under random workloads due to repeated compaction. This overhead becomes prohibitive for large values with uniformly distributed keys, a workload common in content-addressable storage, deduplication systems, and blockchain validators. We present Tidehunter, a storage engine that eliminates value compaction by treating the Write-Ahead Log (WAL) as permanent storage rather than a temporary recovery buffer. Values are never overwritten; and small, lazily-flushed index tables map keys to WAL positions. Tidehunter introduces (a) lock-free writes that saturate NVMe drives through atomic allocation and parallel copying, (b) an optimistic index structure that exploits uniform key distributions for single-roundtrip lookups, and (c) epoch-based pruning that reclaims space without blocking writes. On a 1 TB dataset with 1 KB values, Tidehunter achieves 830K writes per second, that is 8.4x higher than RocksDB and 2.9x higher than BlobDB, while improving point queries by 1.7x and existence checks by 15.6x. We validate real-world impact by integrating Tidehunter into Sui, a high-throughput blockchain, where it maintains stable throughput and latency under loads that cause RocksDB-backed validators to collapse. Tidehunter is production-ready and is being deployed in production within Sui.
Abstract Product life cycle management (PLM) in large supply chains still suffers from limited transparency, manual record-keeping, and weak traceability of provenance and expiry, which often results in counterfeit products, delayed recalls, and unsafe items reaching consumers. After the advancements in blockchain technologies, immutable, decentralised and auto-generated smart contracts provide a secure, safe, and organised solution for consent to the agreement between two or more parties and help digital assets and transactions to occur efficiently This work proposes a Blockchain–Internet of Things (B-IoT) based smart-contract framework that automates three key phases of the product life cycle: purchase order creation, invoice generation at delivery, and expiry-driven discard management. The proposed system will auto-trigger the smart contract through a program. It will generate the smart contract for a product when the purchase order is placed, generate an invoice, and handle the expired and discarded products. IoT devices will record important parameters such as product ID scanning, recording storage temperature, GPS trackers during transportation, etc. These parameters will help to auto-trigger the smart contract. To tune threshold parameters (e.g., temperature bounds, delay limits) and minimize cost–latency trade-offs, we build a lightweight regression model whose hyper-parameters are optimized using nature-inspired Mayfly (MFA) and Honey Badger (HBA) algorithms. The model predicts gas usage and latency per phase, achieving an RMSE of 0.15 and R 2 ≈ 0.9 on simulated transaction logs, while the final configuration yields an average execution efficiency of 92.3% and accuracy of 94.9% in correctly auto-triggering contract phases. The prototype is implemented using Ganache Truffle Suite, Remix, and MyEtherWallet, and evaluated in terms of gas consumption, functional correctness, and automation benefits over traditional manual contracts. Results demonstrate that the proposed B-IoT smart-contract framework provides transparent, tamper-resistant, and fine-grained product life management suitable for industrial deployments.
INTRODUCTION: Each node keeps the identical block data in the decentralized, tamper-proof distributed ledger known as the blockchain. OBJECTIVES: A blockchain network's working time lengthens, the amount of data that nodes must preserve and synchronize increases noticeably. METHODS: This brings up significant storage performance difficulties. We have started a study from a blockchain data storage standpoint to address this storage performance issue. We propose a distributed hash table (DHT)-based blockchain data archiving approach by analyzing the redundancy state caused by every node in the current blockchain network containing the same data. The block data is introduced in three ways: archived data building, lookup, and interaction with the underlying chain. RESULTS: This is done to ensure that blockchain data is not lost and can be accessed. This reduces storage redundancy and satisfies the practical requirements of storage and access in the blockchain's initial application. CONCLUSION: Experiments on energy transaction data show that the technique suggested in this article has a considerably lower storage occupancy increase rate than fabric storage.
Imoleayo Abraham Awodele, Molusiwa S. Ramabodu, Nathaniel Ayinde Olatunde, Iruka C. Anugwo
Africa is richly endowed with renewable energy resources, including solar, wind, and hydropower, yet the continent faces a significant energy access deficit, with over 600 million people lacking reliable electricity. Traditional fossil fuel-based energy models have proven inadequate for meeting the region's growing energy demands while posing environmental and economic challenges. This study explores the need to transcend these conventional energy paradigms by accelerating the adoption of sustainable, inclusive renewable energy systems tailored to Africa's unique context. Adopting a qualitative research approach, the study employed document analysis of policy reports, scholarly literature, and energy market trends to examine the continent's renewable energy transition. Thematic analysis identified key barriers such as limited access to financing, fragmented regulatory frameworks, and insufficient technical capacity. However, the findings also highlight transformative opportunities, including decentralized energy systems for off-grid rural communities, digital innovations, and international climate finance. The study recommends empowering community-driven energy models, adopting innovative financing mechanisms such as microcredit and crowdfunding and fostering cross-sectoral collaboration. These measures will not only expand energy access but also position Africa as a leader in global climate action, environmental sustainability, and inclusive energy innovation. Keywords: Renewable Energy Transition; Decentralized Energy Systems; Sustainable Development; Africa Energy Policy.
R. N. V. Jagan Mohan, Pravallika Sree Rayanoothala, R. Praneetha Sree
Agriculture faces multifaceted challenges including climate variability, soil degradation, and supply chain inefficiencies, particularly for smallholder farmers practicing multicropping. This study systematically integrates blockchain technology for secure, transparent transactions with reinforcement learning (RL)-optimized Neutrosophic multi-regression for precise crop loss prediction in multicropping systems. Using real-world data from six crops (rice, banana, turmeric, elephant foot yam, coconut, cocoa), Neutrosophic multi-regression estimated losses with RL hyperparameter tuning, achieving superior prediction accuracy. A blockchain framework was developed for farmer validation, transaction security, and smart contract execution using Ethereum/Ganache. Results demonstrate 25%–35% reduction in predicted crop losses and enhanced supply chain traceability. This Smart Agriculture 5.0 framework advances Agriculture 4.0 through human-AI symbiosis and uncertainty modeling, addressing single-point failures, data privacy, and trust deficits for scalable sustainable farming Through this multidimensional approach, the study endeavors to not only enhance the productivity and sustainability of agricultural practices but also to foster resilience in the face of evolving challenges.
Yaroslava Yakovenko, Zavodovska D., Reichling Peter
Over the past decade, cryptocurrencies have evolved from a niche technological innovation into a global financial phenomenon. Bitcoin, Ethereum, and other digital assets have attracted massive attention from investors, policymakers, and the general public. The central debate surrounding cryptocurrencies centres on whether they represent a financial bubble destined to burst or the foundation of a new, decentralized financial future.
Oscar Revelo Sánchez, Alexander Barón Salazar, Manuel Bolaños González
This systematic review examines recent advances in blockchain-based electronic voting systems, motivated by the need for more transparent, secure, and verifiable electoral processes. The rapid growth of research between 2022 and 2025 highlights blockchain as a promising foundation for addressing long-standing challenges of integrity, anonymity, and trust in digital elections, particularly in academic contexts where pilot deployments are more feasible. The review followed PRISMA 2020 guidelines and applied the evidence-based methodology proposed by Kitchenham & Charters. Searches were conducted in six major databases, yielding 861 records; after removing duplicates and applying eligibility criteria, 338 studies were retained. Data were extracted using a structured template and synthesised qualitatively due to the conceptual and methodological heterogeneity of the evidence. The included studies reveal significant progress in blockchain architectures, smart contracts, and advanced cryptographic mechanisms—such as blind signatures, zero-knowledge proofs, and homomorphic encryption. Multiple authentication and verification strategies were identified; however, real-world validations remain limited and largely confined to small-scale academic pilots. Overall, blockchain-based voting systems demonstrate conceptual advantages over traditional and conventional electronic models, especially regarding transparency and auditability. Nevertheless, the field requires stronger empirical evaluation, greater scalability, and clearer regulatory alignment to support broader institutional adoption.
This study explores the diversification potential of Bitcoin in a French investment portfolio comprising oil, currency, and gold across three distinct market regimes: a pre-crisis stable period, the COVID-19 pandemic, and the Russia–Ukraine conflict. The purpose is to assess whether Bitcoin can enhance portfolio efficiency and provide hedging opportunities under varying market conditions. The analysis is conducted using daily data for Bitcoin, gold, oil, currency, and the CAC40 index from January 1, 2019, to April 22, 2022. Portfolio performance is evaluated through the Mean–Variance (MV) framework and Stochastic Dominance (SD) analysis, allowing for a robust comparison of risk–return trade-offs and investor preferences. The MV results show that including Bitcoin consistently improves the portfolio’s risk–return profile, evidenced by an upward shift in the efficient frontier across all sub-periods. However, the SD analysis yields more nuanced insights. Before and during the COVID-19 crisis, the portfolio excluding Bitcoin dominates the Bitcoin-inclusive portfolio under second- and third-order stochastic dominance criteria, suggesting that risk-averse investors would prefer the traditional asset mix. In contrast, during the Russia–Ukraine war, no clear stochastic dominance is detected between Bitcoin-inclusive and Bitcoin-exclusive portfolios. These findings emphasize that Bitcoin’s diversification role is highly context- and framework-dependent.
Muhammad Usama, Arshad Aziz, Nada Alasbali, Nazik Alturki · 6 authors
The growing deployment of the Internet of Things (IoT), especially in critical infrastructure, has increased the need for identity systems that are scalable and robust against attacks. However, existing centralized systems have fundamental weaknesses, especially where adversaries use artificial intelligence (AI)-based techniques, such as generative spoofing, model poisoning, and deepfakes to create fake identities. In this paper, we present a novel blockchain-based IoT security system that combines decentralized identity verification, zero-knowledge proofs, Byzantine-resistant federated learning, and formal verification of smart contracts. The proposed architecture eliminates single points of trust, allows device registration while preserving privacy, and provides defense against AI-driven attacks through formally modeled state transitions. Experimental results show that this method shows significant improvements over previous frameworks, including a 48% reduction in false acceptance rate during GAN-based spoofing and speedup the ZKP verification. This work provides a blockchain-enabled identity management system for IoT to encounter AI-based threats and maintain a balance between performance and security with the help of adversarial simulation, symbolic execution, and threshold cryptography.
• We examine the profitability of a cryptocurrency momentum strategy using 9 “survivor coins”. • The survivor cryptocurrency momentum portfolio (SCMP) does not generate significant payoffs. • SCMP does not leverage a plain momentum strategy based on a broader set of coins. • Significant payoffs documented for momentum strategies are an artefact of coins that are only temporarily accessible for trading. Motivated by the significant illiquidity observed in the cryptocurrency market—exemplified by phenomena such as "defaulted coins"—this study is the first to investigate a cryptocurrency-specific analog of currency momentum, as implemented among G10 currencies. We analyze nine free-floating cryptocurrencies that remained within the top 100 altcoins by market capitalization during the sample period, spanning January 2017 to August 2024. Using weekly data, we evaluate two cryptocurrency momentum strategies: one focused solely on survivor coins and another utilizing the largest 30 coins for a given year (referred to as "plain cryptocurrency momentum"). Our main findings are as follows: (a) Cryptocurrency momentum is not evident when applied to survivor coins; (b) plain cryptocurrency momentum is profitable only after the dataset is trimmed; (c) the profitability of trimmed plain cryptocurrency momentum does not result from leveraging survivor coin-based cryptocurrency momentum; (d) even after trimming, the profitability of plain cryptocurrency momentum is highly sample-dependent.
Do online narratives leave a measurable imprint on prices in markets for digital or cultural goods? This paper evaluates how community attention and sentiment relate to valuation in major Ethereum NFT collections after accounting for time effects, market-wide conditions, and persistent visual heterogeneity. Transaction data for large generative collections are merged with Reddit-based discourse measures available for 25 collections, covering 87{,}696 secondary-market sales from January 2021 through March 2025. Visual differences are absorbed by a transparent, within-collection standardized index built from explicit image traits and aggregated via PCA. Discourse is summarized at the collection-by-bin level using discussion intensity and lexicon-based tone measures, with smoothing to reduce noise when text volume is sparse. A mixed-effects specification with a Mundlak within--between decomposition separates persistent cross-collection differences from within-collection fluctuations. Valuations align most strongly with sustained collection-level attention and sentiment environments; within collections, short-horizon negativity is consistently associated with higher prices, and attention is most informative when measured as cumulative engagement over multiple prior windows.
Open access
3 source records
econ.GN
Consumer Behavior in Brand Consumption and Identification
The current scientific system faces systemic challenges. Decentralized Science (DeSci) has emerged as a technological extension of the Open Science (OS) movement, aiming to improve transparency, accessibility, and equity in research through blockchain and Web3 technologies. While DeSci has gained traction in Western countries, little is known about its adoption in non-Western contexts. Here, we surveyed 37 researchers and technologists active in Japan’s emerging decentralized‑science (DeSci) during spring 2024 to assess how far the movement has progressed and what impedes its progress. Roughly 60% of respondents had already worked on blockchain projects and more than 80% owned crypto assets, yet almost 90% had discovered DeSci only in the past two years. Respondents largely embraced DeSci’s five core ideals: shared governance, transparent funding, open access, shared ownership, and equitable incentives. Meanwhile, four obstacles to growth were highlighted: low public awareness, difficulty sustaining engagement, limited talent diversity, and regulatory uncertainty. Taken together, the findings suggest that Japan’s DeSci community should also invest not only in further technical changes, but also in training, in broadening its talent base, and in setting clear guidelines. This study provides a comprehensive overview of the DeSci landscape in Japan and offers recommendations for its future development.
Essais sur le crédit, la découverte des taux et les facteurs déterminants du prix des jetons en finance décentralisée Cette thèse explore les fondements économiques et comportementaux de la finance décentralisée (DeFi), un champ en pleine expansion où les fonctions de prêt, d'emprunt et de fixation des taux d'intérêt sont assurées par des contrats intelligents plutôt que par des institutions financières. À travers trois essais complémentaires, ce travail analyse la conception des protocoles de crédit décentralisés, la formation des taux d'intérêt dans des marchés automatisés et les déterminants fondamentaux et comportementaux de la valorisation des tokens DeFi.Le premier essai examine l'architecture du protocole Atlendis, qui permet des prêts non ou partiellement collatéralisés grâce à l'articulation entre souscription off-chain et exécution on-chain. Le deuxième propose un modèle théorique de découverte de taux basé sur une approche de jeu multi-unités, identifiant les conditions d'efficience et les frictions propres aux marchés décentralisés. Le troisième évalue empiriquement les facteurs économiques et comportementaux influençant les rendements des tokens, révélant le rôle central du sentiment des investisseurs et de la liquidité on-chain dans la dynamique des prix. En combinant ingénierie financière, modélisation théorique et analyse empirique, cette recherche met en lumière les mécanismes par lesquels la DeFi redéfinit l'intermédiation, la formation des prix et la gouvernance financière dans un environnement transparent et programmable.
Digital identity verification is central to trust management on the evolving decentralized web. Traditional web-based identity models, which are heavily centralized and dependent on trusted intermediaries, pose significant challenges related to user privacy, data security, and regulatory compliance, especially in sensitive contexts such as Know Your Customer (KYC) processes. This paper introduces a novel privacy-preserving KYC verification framework leveraging Zero-Knowledge Proofs (ZKPs), Self-Sovereign Identity (SSI), Decentralized Identifiers (DIDs), and smart contracts, explicitly designed as a decentralized trust infrastructure for web-based interoperable payments. Our approach enables users to verify their identities across multiple platforms without revealing sensitive personal information, thereby significantly reducing long-term reliance on centralized authorities and enhancing user control and privacy. Furthermore, our system achieves cross-chain interoperability, ensuring that identity verification credentials can be securely and efficiently recognized across diverse Web3 ecosystems. We present a detailed prototype implementation of our DID framework, highlighting its ability to meet regulatory requirements while ensuring seamless interoperability across platforms. Comprehensive performance evaluations, including metrics on proof generation time, gas consumption, and transaction costs, demonstrate that the framework achieves low-latency verification and efficient execution, making it suitable for high-throughput, web-scale deployment.
The rapid growth of Cardiovascular Disease (CVD) data from heterogeneous sources, including diagnostic imaging systems, electrocardiography devices, wearable sensors, and public research repositories, has created major challenges in ensuring data confidentiality, integrity, controlled access, and scalable management. Conventional centralized data storage architectures are prone to security breaches, and limited audit transparency. To address these limitations, this paper proposes a secure and scalable Token-Based PPos Heart chain (TPPoSHChain) framework for the management of multi-source CVD datasets by integrating blockchain technology, decentralized identity, authenticated encryption, and token-based access governance. The framework employs a hybrid on-chain/off-chain architecture, where the Algorand blockchain with a Pure Proof of Stake (PPoS) consensus mechanism provides immutable audit logging and access control enforcement, while encrypted datasets are stored off-chain in the InterPlanetary File System (IPFS) to enhance scalability. ChaCha20-Poly1305 authenticated encryption is used to protect datasets prior to storage and transmission, ensuring both confidentiality and integrity. Decentralized Identifiers (DIDs) establish a self-sovereign identity layer for data contributors, and actors, eliminating reliance on centralized identity providers. A blockchain-supported token-based access control mechanism enables fine-grained authorization, usage traceability, and secure cross-institutional data sharing. Experimental evaluation demonstrates that the proposed TPPoSHChain framework significantly improves transaction throughput, reduces latency, lowers storage overhead, and achieves efficient encryption performance than other existing models, making it well suited for secure and scalable CVD datasets.
Fabio Turazza, Marcello Pietri, Marco Picone, Marco Mamei
Privacy-Preserving Federated Learning (PPFL) is a Decentralized machine learning paradigm that enables multiple participants to collaboratively train a global model without sharing their data with the integration of cryptographic and privacy-based techniques to enhance the security of the global system. This privacy-oriented approach makes PPFL a highly suitable solution for training shared models in sectors where data privacy is a critical concern. In traditional FL, local models are trained on edge devices, and only model updates are shared with a central server, which aggregates them to improve the global model. However, despite the presence of the aforementioned privacy techniques, in the classical Federated structure, the issue of the server as a single-point-of-failure remains, leading to limitations both in terms of security and scalability. This paper introduces FedBGS, a fully Decentralized Blockchain-based framework that leverages Segmented Gossip Learning through Federated Analytics. The proposed system aims to optimize blockchain usage while providing comprehensive protection against all types of attacks, ensuring both privacy, security and non-IID data handling in Federated environments.
Using the Crypto Fear & Greed Index and Bitcoin daily data, sentiment extremity predicts excess uncertainty beyond realized volatility. Extreme fear and extreme greed regimes exhibit significantly higher spreads than neutral periods -- the "extremity premium." Extended validation on the full Fear & Greed history (2018--2026, N = 2,896) confirms the finding: within-volatility-quintile comparisons show a premium ($p < 0.001$, pooled volatility-demeaned Cohen's $d = 0.21$ -- a post-hoc, exploratory test, as the pre-specified within-quintile endpoint does not survive multiple-testing correction; raw pooled extreme-vs-neutral $d = 0.40$), Granger causality runs from uncertainty to spreads (primary-sample $F = 12.79$; the extended-sample $F = 211$ is partly mechanical, sharing a high-low input with the spread measure), and placebo tests reject the null ($p < 0.0001$). The effect replicates on Ethereum and across 6 of 7 market cycles. However, the premium is sensitive to functional form: regression controls absorb regime effects, while nonparametric stratification preserves them. We interpret this as evidence that sentiment extremity captures volatility-regime interactions not fully represented by parametric controls -- consistent with, but not conclusively separable from, the F&G Index's embedded volatility component. An agent-based model is included as an illustrative device that reproduces the pattern qualitatively; because its spread-uncertainty link is coded rather than emergent, it does no inferential work (the reported moment-matching test validates a separate simplified model, not the full agent specification), and the inferential weight rests entirely on the empirical analysis. The results suggest that intensity, not direction, drives uncertainty-linked liquidity withdrawal in cryptocurrency markets, though identifying "pure" sentiment effects from volatility remains open.
Cryptocurrency markets exceed USD 3 trillion in capitalisation, yet practitioners lack an interpretable, channel-decomposed composite for characterising crypto-native systemic stress. We introduce the Aggregated Systemic Risk Index (ASRI), built from four weighted sub-indices -- Stablecoin Concentration Risk (30%), DeFi Liquidity Risk (25%), Contagion Risk (25%, implemented as a TradFi-stress proxy), and Regulatory Opacity Risk (20%) -- with a Diebold--Yilmaz connectedness series computed on the sub-indices as network benchmark. We evaluate ASRI retrospectively against four crises (Terra/Luna, Celsius/3AC, FTX, SVB) and give a methodological account of how autocorrelation- and block-structure-robust inference reshapes apparent crisis-detection strength. The event-study signal is inconclusive: heavily serially correlated (AR(1) $\approx 0.8$--$0.9$), with placebo dates clearing the nominal threshold as often as crises. Fixed-threshold detection flags three of four events with $\approx$19-day average lead ($\approx$5 days under a responsive specification); walk-forward thresholds flag 4/4 but at high false-positive cost -- evidence against look-ahead bias, not a clean prediction record. ASRI's day-level discrimination (AUROC 0.866) beats only the circular D--Y comparator (0.670); it is statistically indistinguishable from its strongest sub-index (0.851), PC1 (0.858), and a standalone VIX series (0.875, $p=0.58$). We read aggregation's value as interpretive -- channel attribution, lead time, and regime structure in one auditable composite -- not as discriminative gain. With four crisis events the binding power limit, ASRI is a transparent, reproducible, retrospective monitoring framework targeting crypto-native vulnerabilities that SRISK and CoVaR are not built to capture, not a validated early-warning system. Out of sample it classifies the 2025 Bybit hack as non-systemic.