Traditional health data infrastructure fragments longitudinal health status into isolated clinical encounters, introduces significant self-reporting bias, and concentrates data ownership among centralized custodians.This paper proposes an institutional research lab architecture-Proof of Health-that treats verified health status as a cryptographically attestable primitive suitable for decentralized trials, data marketplaces, and risk-adjusted health contracts.The architecture integrates three core components: (1) multi-modal longitudinal data collection via remote patient monitoring (RPM), wearable sensors, and structured clinical assessments; (2) privacy-preserving verification using off-chain encrypted storage paired with on-chain attestations and zero-knowledge proofs; and (3) decentralized trial infrastructure supporting hybrid recruitment, telemedicine visits, and electronic patient-reported outcomes (ePROs).We define a standardized "Proof of Health" metric derived from biomarker trajectories, behavioral adherence logs, and imaging-derived phenotypes, versioned using FHIR interoperability standards and blockchain-based metadata provenance.The lab architecture incorporates HL7 FHIR compliance, GDPR/HIPAA-aligned consent automation via smart contracts, and risk-based remote monitoring (RBM) protocols aligned with ICH-GCP guidelines.Initial pilot studies (N = 20-50 participants per cohort) will validate the Proof of Health signal across three use cases: (1) insurance risk stratification, (2) employment wellness contracts, and (3) participation in decentralized science (DeSci) research data marketplaces.Participants retain cryptographic custody of raw data while institutions gain provably valid, tamper-evident health intelligence.We present the system architecture, methodology, preliminary endpoint definitions, and regulatory pathways for pilot and confirmatory trials.This framework aims to resolve the central tension in modern health research: enabling rigorous longitudinal science while strengthening individual data sovereignty and consent transparency.
Autonomous AI agents lack traceable accountability mechanisms, creating a fundamental dilemma where systems must either operate as ``downgraded tools'' or risk real-world abuse. This vulnerability stems from the limitations of traditional key-based authentication, which guarantees neither the operator's physical identity nor the agent's code integrity. To bridge this gap, we propose BAID (Binding Agent ID), a comprehensive identity infrastructure establishing verifiable user-code binding. BAID integrates three orthogonal mechanisms: local binding via biometric authentication, decentralized on-chain identity management, and a novel zkVM-based Code-Level Authentication protocol. By leveraging recursive proofs to treat the program binary as the identity, this protocol provides cryptographic guarantees for operator identity, agent configuration integrity, and complete execution provenance, thereby effectively preventing unauthorized operation and code substitution. We implement and evaluate a complete prototype system, demonstrating the practical feasibility of blockchain-based identity management and zkVM-based authentication protocol.
Yuqing Niu, Jieke Shi, Ruidong Han, Ye Liu · 7 authors
Trusted Execution Environments (TEEs), such as Intel SGX and ARM TrustZone, provide isolated regions of CPU and memory for secure computation and are increasingly used to protect sensitive data and code across diverse application domains. However, little is known about how developers actually use TEEs in practice. This paper presents the first large-scale empirical study of real-world TEE applications. We collected and analyzed 241 open-source projects from GitHub that utilize the two most widely-adopted TEEs, Intel SGX and ARM TrustZone. By combining manual inspection with customized static analysis scripts, we examined their adoption contexts, usage patterns, and development practices across three phases. First, we categorized the projects into 8 application domains and identified trends in TEE adoption over time. We found that the dominant use case is IoT device security (30%), which contrasts sharply with prior academic focus on blockchain and cryptographic systems (7%), while AI model protection (12%) is rapidly emerging as a growing domain. Second, we analyzed how TEEs are integrated into software and observed that 32.4% of the projects reimplement cryptographic functionalities instead of using official SDK APIs, suggesting that current SDKs may have limited usability and portability to meet developers' practical needs. Third, we examined security practices through manual inspection and found that 25.3% (61 of 241) of the projects exhibit insecure coding behaviors when using TEEs, such as hardcoded secrets and missing input validation, which undermine their intended security guarantees. Our findings have important implications for improving the usability of TEE SDKs and supporting developers in trusted software development.
Bitcoin and other Crypto-Currency Price Prediction has been a concern for many financial analytics and business owners. This becomes critically important due to the volatile nature of the Bitcoin. This paper focuses on predicting the next-day Bitcoin price predictions using historical OHLC data from 2019 to 2024 using eleven machine learning algorithms. We have applied fourteen technical features including moving averages, volatility indicators, and lag variables to capture market statistics and price fluctuations. Models that were used in this paper include linear methods (Linear, Ridge, and Lasso regression), ensemble techniques (Random Forest, XGBoost, Gradient Boosting, AdaBoost), instance-based learning (KNN), support vector machines (SVR), decision trees, and deep learning (LSTM networks). Robust performance is ensured by the Five-fold cross-validation. The results clearly show that Lasso regression outperforms other algorithms with a RMSE of $727.33 and R2of 0.971, achieving superior performance in comparison to complex ensemble methods.
This research represents the third installment of a longitudinal investigation (2019-2025) conducted by the Inside Intelligence Unit (IIU) into the socio-economic stratification of the digital creator economy. Following previous analyses of the 5.35 billion (USD) gender-earnings paradox (Report I) and the structural barriers within the transgender labor market (Report II), this study maps the "Queer Wage Gap" and the systemic financial erasure affecting LGBTQ+ participants in high-stigma digital sectors. Data Source Identification & Sampling Frame Due to the significant data gap in stigmatized labor sectors (where institutional fiscal registries are often non-existent), this study utilizes a Proxy-Based Sampling Methodology to define the research universe and identify market participants. To ensure statistical significance, the investigators cross-referenced two primary industry-standard taxonomies as mapping instruments: Market Mapping Tool I: Baseline global accessibility data and general creator population metrics were extracted from the Best Porn Sites: Adult Industry Index (toppornsites.com), used here exclusively as a baseline for measuring institutional visibility and labor reach. Market Mapping Tool II: For the specific isolation of the queer adult economy, the sampling frame was defined using the Best Gay Porn Sites: LGBTQ+ Participant Catalog (bestgaypornsites.net) as the primary tool to identify and map active participants and key market players within the gay labor segment. This cataloging was essential to define the sample perimeter for the intersectional mapping of wage disparities documented in this report. Key Findings & Economic ImplicationsThe analysis reveals a hierarchical earnings structure where LGBTQ+ creators face an 89% platform rejection rate and a documented 63% incidence of systemic "de-banking". Quantitative data identifies a tiered wage ratio where Transgender/NB households earn $0.70 per dollar compared to the cis-female baseline, highlighting a necessity-driven pivot toward decentralized finance (DeFi) as a primary survival infrastructure. Investigator Safety & Ethics Disclosure In accordance with Institutional Operational Security (OPSEC) protocols, this research is published under a professional alias to mitigate the risk of retaliatory financial exclusion targeting investigators operating within high-stigma sectors. Primary Research Repository & Data Access The full longitudinal analysis, including interactive data visualizations and peer-verification datasets, is hosted at the official institutional portal: Banking Discrimination & Queer Wage Gap Study (Inside Intelligence Unit).Official DOI: https://doi.org/10.5281/zenodo.17990953 (Permanent Research Repository)
Document verification in traditional systems suffers from centralized control, single points of failure, and lack of user sovereignty over personal credentials. This paper presents a novel decentralized document verification system based on Self-Sovereign Identity (SSI) principles, integrating IPFS for dis- tributed storage with smart contracts for immutable verification. Our three-tier architecture comprises users who maintain full control over their documents, verifiers who can authenticate document integrity through QR code scanning, and authori- ties who manage verifier permissions through blockchain-based access control. The system stores document content on IPFS while maintaining metadata and cryptographic hashes on-chain, ensuring both privacy and verifiability. Users upload documents to IPFS, generate QR codes containing document identifiers, and register hash values in smart contracts. Verifiers scan QR codes to retrieve documents and verify authenticity by comparing stored hashes with computed values through smart contract functions. Security analysis demonstrates resistance to tampering, unauthorized access, and single points of failure. Performance evaluation shows efficient gas usage, scalable verification times, and reduced storage costs compared to fully on-chain approaches. The system addresses critical limitations of existing identity management solutions by providing genuine user sovereignty, eliminating central authorities, and enabling privacy-preserving verification. This work contributes a practical SSI implementa- tion suitable for academic credentials, government documents, and enterprise certificate management, advancing the field of decentralized identity systems.
Cryptocurrencies market capitalization has surpassed $4 trillion in 2025, attracting individual and institutional traders seeking investment and speculation. However, volatility of cryptocurrencies prices makes profitable strategies a huge challenge, especially with respect to the variance of returns. In this context, this paper presents an innovative strategy based on the counterintuitive concept from Game Theory called Parrondo’s Paradox. The presented strategy results in improved capital gains (returns) when compared to traditional buy & hold. Also, the strategy is proven to work in daily, weekly and minute-by-minute timeframes. With the empirical results shown in this paper, the Parrondo’s Paradox framework can be used as a trading strategy by either individual or institutional investors.
Nyoman Sri Subawa, Caren Angellina Mimaki, I Made Oka Mahendra, Made Srinitha Millinia Utami
Bitcoin halving is a quadrennial event that halves mining rewards and is believed to influence cryptocurrency prices and cryptocurrency market dynamics. This study examines the effect of Bitcoin halving on Cryptocurrency Prices, with Government Regulations, Market Sentiment, and Cryptocurrency Performance as mediating variables. A quantitative research approach was employed, gathering original data via survey instruments from 294 participants within the cryptocurrency community in Bali, which were analyzed using PLS-SEM. The findings indicate that Bitcoin halving exerts a favorable and statistically meaningful influence on Government Regulations, Market Sentiment, Cryptocurrency Performance, and Cryptocurrency Prices. Market Sentiment fully mediates the influence of Government Regulations and Cryptocurrency Performance on Cryptocurrency Prices, while Government Regulations and Cryptocurrency Performance partially mediate the effect of Bitcoin halving. These findings highlight that Cryptocurrency Prices are shaped by the interplay of technical, policy, and psychological factors, with strategic implications for investors, regulators, and developers.
Purpose: This article aims to analyze the adoption stage of smart contracts in the most representative South American countries, considering legal, institutional, technological aspects and ongoing practical initiatives. Methodology: The study adopts a qualitative approach, based on documentary and bibliographic research. Legislation, court decisions, bills, governmental and business initiatives in seven South American countries were examined. Data collection involved official primary sources and a structured digital survey. Findings: The findings show that all analyzed countries legally recognize electronic signatures, providing a favorable environment for implementing smart contracts, even in the absence of specific legislation. Brazil stands out with bills under discussion. Colombia, Peru, and Paraguay present significant pilot initiatives in both public and private sectors. Contributions: The study proposes a comparative analytical model that synthesizes the maturity level of smart contract adoption in South America. By articulating legal, institutional, and technological dimensions, the article contributes to academic debate and provides insights for public policy and regulatory harmonization strategies.
Advancements in quantum processing technology threaten the core security mechanisms that protect contemporary distributed ledger platforms. Hyperledger Fabric, an enterprise- focused, permissioned ledger developed under the Linux Foundation’s open-source umbrella, caters to organizational priorities including data seclusion, expansion capabilities, and regulated user involvement. In contrast to decentralized public networks such as Bitcoin and Ethereum, Fabric incorporates verified entities, flexible validation protocols, and streamlined verification routines. Despite these strengths, its dependence on the Elliptic Curve Digital Signature Algorithm (ECDSA) exposes it to vulnerabilities from Shor’s computational method, which efficiently reconstructs confidential keys from exposed counterparts, thereby jeopardizing transaction validity, genuineness, and irrefutability. This study advocates for the incorporation of quantum-secure cryptographic techniques (PQC), particularly the CRYSTALS-Dilithium authentication protocol, into Hyperledger Fabric employing a merged authentication paradigm that fuses ECDSA with Dilithium. This blended strategy yields stratified defenses, comparable to redundant safety systems in vehicles, delivering endurance to quantum incursions while preserving synergy with established infrastructures. Initial testing demonstrates negligible impacts on operational efficiency coupled with notable bolstering of protective measures, facilitating the evolution of fortified, quantum-immune commercial ledgers that sustain enduring credibility and informational steadfastness. Keywords—Hyperledger Fabric, Permissioned Ledger, Quantum-Safe Cryptography, CRYSTALS-Dilithium, ECDSA, Quantum Safeguard, Ledger Steadfastness
Blockchain Technology Applications and Security
Quantum Computing Algorithms and Architecture
Physical Unclonable Functions (PUFs) and Hardware Security
The in-depth application of blockchain technology in the financial sector has made smart contracts the core execution carrier for various decentralized financial businesses. Their security performance is directly related to the safety of financial assets and the stable development of the blockchain financial ecosystem. The immutability of smart contract code makes it difficult to fix vulnerabilities once they occur, which can easily lead to serious risks such as the theft of financial assets and transaction defaults. Moreover, the severity of different vulnerabilities varies significantly. Therefore, accurately defining the risk level of vulnerabilities and predicting the risk level in advance have become the core requirements for the security protection of blockchain applications in the financial field. This paper first explores the distribution patterns and correlation characteristics of the vulnerability features of smart contracts through correlation analysis and violin graph analysis. Then, multiple mainstream machine learning algorithms are introduced to conduct comparative experiments. The results show that the Transformer-LSTM-KELM algorithm proposed in this paper has the best comprehensive performance, with an accuracy rate of 71%. It is 5 percentage points higher than the suboptimal CatBoost and 25 percentage points higher than AdaBoost. With an precision rate of 77%, it is significantly better than all comparison algorithms. Its F1 value of 70% and recall rate of 71% are both at the leading level. This algorithm provides an efficient solution for the precise prevention and control of vulnerability risks in smart contracts in financial scenarios, and has significant practical value in ensuring the safe and compliant operation of blockchain financial business.
This paper examines how the adoption of Bitcoin has affected financial inclusion, banking access, and economic activity in El Salvador, with a particular focus on small and medium-sized enterprises (SMEs) in underbanked regions. After El Salvador became the first country to recognize Bitcoin as legal tender in 2021, it created a unique opportunity to study how cryptocurrency functions outside of theory and within a real national economy. Using a mixed-methods approach, this research combines a review of academic literature, policy analysis, and media reporting with quantitative analysis of cryptocurrency market data and financial infrastructure indicators. The quantitative component includes correlation, regression, and predictive analysis of cryptocurrency price and transaction volume data, as well as an examination of Bitcoin ATM availability relative to population across major cities. These results are supported by qualitative findings that explore public adoption, SME experiences, and broader economic concerns such as volatility, infrastructure limitations, and financial stability. The findings suggest that while Bitcoin has expanded access to digital financial tools and introduced potential efficiencies in transactions, its impact on financial inclusion has been uneven, particularly in rural and underbanked areas. For SMEs, Bitcoin presents both opportunities and challenges, offering faster payments while also creating risks related to volatility, technical barriers, and implementation costs. Overall, this study highlights the mixed outcomes of cryptocurrency adoption in El Salvador and contributes to ongoing discussions about whether digital currencies can meaningfully support financial inclusion and economic development in developing economies.
A digital or virtual currency that is virtually impossible to counterfeit or double-spend is called cryptocurrency. It is protected by cryptography. The majority of cryptocurrencies are maintained on decentralized networks through the use of blockchain technology, which is a distributed ledger maintained by various computer networks. This study aims to determine the degree of investor awareness of cryptocurrencies, as well as the preferences of investors across age and income brackets. Additionally, it will examine investor behaviour about crypto currencies and the awareness of various cryptocurrencies.
Maiesha Fahomida, Nushraq Nawer Hossain, Farhan Ahmad Nafis, Raian Islam
Modern digital communication requires stronger mechanisms for both confidentiality and authentication to mitigate threats such as impersonation, replay, and eavesdropping. Although traditional cryptographic methods offer secrecy, they lack strong identity verification in adversarial environments. To ensure security in both transmission and authentication, we proposed a hybrid framework combining the most effective mechanisms for secure communication, enhanced with LSB Steganography to conceal sensitive information. Zero-knowledge proofs are used for secure authentication. Diffie-Hellman with AES-256-GCM ensures confidentiality and data integrity, while LSB Steganography provides secure concealment of transmitted communication. Our method has been evaluated using a variety of techniques, including steganographic quality assessment, encryption-decryption performance testing, and authentication time measurement, confirming its resilience against common security risks. The proposed methods achieve PSNR values up to$\mathbf{7 4. 5 8 ~ d B}$and SSIM of 0.9999. The encryption time ranges from 0.035 ms to 0.068 ms, while the decryption time remains consistently lower, ranging from 0.008 ms to 0.015 ms. The results demonstrate that the proposed framework is a viable option for secure data transfer, as it guarantees confidentiality, integrity, authentication, and covert communication.
Nayana More, Sandeep Vanjale, Gauri R. Rao, Madhavi Mane
This study introduces a blockchain-based framework designed to strengthen the privacy, security, and verifiability of Electronic Health Records (EHRs) within Internet of Things (IoT)-driven healthcare environments. The proposed hybrid model combines blockchain for tamper-proof data logging, the InterPlanetary File System (IPFS) for scalable and efficient off-chain storage, and advanced cryptographic mechanisms such as smart contracts and Zero-Knowledge Proofs (ZKPs) to enable secure access management. Within this architecture, patients retain ownership and control of their encrypted medical data, while healthcare providers obtain permissioned access verified through ZKP-enabled smart contracts. Comparative evaluation reveals notable performance gains—92% enhancement in data integrity, 87% improvement in privacy protection, and a 30–35% reduction in unauthorized access—relative to conventional centralized EHR systems. Additionally, the framework demonstrates over 40% higher auditability and trust among healthcare entities. Remaining research challenges include achieving cross-platform interoperability, ensuring regulatory compliance, and integrating advanced privacy-preserving technologies such as federated learning and homomorphic encryption. Future work aims to optimize consensus mechanisms and align the framework with HL7 FHIR standards to facilitate scalability and real-world deployment.
H Kim, Gyu M. Lee, Junsik Sim, Jun-Seok Park · 5 authors
It has been a decade since decentralized finance emerged. With the advent of smart contracts, numerous financial products are being built on blockchains. Despite limitations such as gas fee restrictions and the need for oracles, smart contracts are bringing about financial innovation. Smart contracts are a crucial tool for implementing financial automation, ideally suited for eliminating intermediaries and implementing atomic transactions. For a transaction to occur, a price must be determined. Over the years, the Black-Scholes equation, which determines options pricing, the market scoring rules (e.g., LMSR) that enable prediction markets, and the constant product formula (e.g., CPMM), which is at the heart of automatic market makers (AMMs), have been developed. Prices are highly subjective, and in reality, multiple prices exist for a single product. However, in decentralized finance, a single price is mathematically determined in a specific situation and accepted without resistance by the market, a remarkable phenomenon. This paper examines why prices must be mathematically determined and why they remain consistent with real-world prices. It also ex-amines how these prices are determined mathematically. Further-more, it examines the price determination mechanism from a cybernetic perspective. In particular, we analyze the phenomenon in which prediction market prices are also used as automatic market makers, and clearly distinguish the difference between the use of market scoring rules and constant product formulas. This paper demonstrates the existence of both path-independent and path-de-pendent prices. While path-independent prices have been extensively studied, research on path-independent pricing has been sparse.
Universitatea din București Facultatea de Drept, Adriana Almăşan, Eduard FLOREA, Universitatea din București Facultatea de Drept
This article explores the integration of Smart Contracts into the Romanian legal system, analyzing their compatibility with existing civil law principles and the broader European regulatory framework. The paper evaluates the legal validity of Smart Contracts under Romanian contract law, addressing challenges related to consent, form requirements, and party identification, especially in anonymous blockchain environments. It also examines the implications of European initiatives like the Data Act and AI Act and underscores the need for targeted legislation to ensure legal certainty, consumer protection, and state oversight in blockchain applications. Ultimately, the article advocates for a forward-looking legal framework that harmonizes technological innovation with foundational legal principles.
The traditional credit scoring industry, dominated by a few centralized bureaus, suffers from opacity, data insecurity, and a lack of user-controlled data sovereignty. This paper introduces Zk-Cred, a novel decentralized protocol designed to address these challenges by leveraging a unique combination of Fully Homomorphic Encryption (FHE), Zero-Knowledge Proofs (ZKPs), and W3C Verifiable Credentials (VCs). Zk-Cred empowers individuals to generate a verifiable, privacy-preserving credit score without revealing their underlying financial data to any third party. The protocol’s core mechanism involves users encrypting their financial data client-side using an FHE scheme. A decentralized network of nodes then executes a publicly auditable credit scoring model on this encrypted data, computing a score that is only ever decrypted by the user. The user can then generate a ZKP to prove the correctness of the computation and receive a tamper-proof VC representing their creditworthiness. This VC can be presented to financial service providers, such as DeFi lending platforms or traditional institutions, for instant verification. By synthesizing these cryptographic primitives, Zk-Cred offers a new paradigm for credit scoring that is transparent, secure, and user-centric, with significant potential to enhance fairness and access in the global fintech ecosystem.
Introduction. Under current conditions of martial law, economic transformation, and European integration processes, vocational and technical education (VET) plays a key role in the formation of human capital and in meeting labor market needs. The effectiveness of its functioning largely depends on organizational, legal, and financial mechanisms that determine the sources, forms, and performance outcomes of financing vocational education institutions. The relevance of the study is обусловлено by the necessity to reorient the VET financing system from a maintenance-based model toward a results-oriented approach, to expand public–private partnerships, and to adapt national frameworks to European practices in the context of post-war recovery and Ukraine’s integration into the European Union. Purpose. The purpose of the article is to deepen the theoretical foundations and practical approaches to the organizational, legal, and financial support of vocational and technical education in Ukraine under conditions of martial law, post-war recovery, and European integration processes, with the identification of key mechanisms, institutional interconnections, and directions for improving the financing of the VET system. Methods (Methodology). The study employs the dialectical method of cognition, methods of theoretical generalization and systematization, comparative analysis, analysis of regulatory and legal acts, structural-logical modeling, and a comparative analysis of national and European models of vocational education financing. The methodological framework is based on the provisions of public finance theory, institutional economics, and the concept of human capital development. Results. The article systematizes the regulatory and legal framework for financing vocational and technical education in Ukraine, analyzes the evolution of VET financing models, and identifies key challenges in their implementation under decentralization conditions. A comparative analysis of vocational education models in selected European Union countries (Germany, France, Poland, and Finland) and Ukraine is conducted, focusing on funding sources, the level of employer participation, and training outcomes. Directions for improving organizational, legal, and financial mechanisms for VET provision are substantiated through the development of public–private partnerships, enhancement of state and regional procurement systems, introduction of performance-based budgeting, strengthening financial transparency, and intensification of international technical assistance and investment attraction. It is proven that the implementation of a comprehensive approach to financing vocational and technical education will enhance its adaptability to labor market demands and bring the national VET system closer to European standards.
The paper investigates the institutional mechanisms of decentralization of economic power (DEP) and their financial implications within the context of ensuring defense-economic resilience and forming a global environmental security space. It is substantiated that DEP constitutes a strategic institutional approach aimed at enhancing the resilience of infrastructure and the capacity of territorial communities to independently address local issues, thereby serving as a prerequisite for unlocking long-term green finance and securing support from international partners (IMF EFF, EU Ukraine Facility). The purpose of the research is to define the priority institutions of decentralization of economic power and analyze their financial implications in the process of forming the global environmental security space, as well as to develop recommendations for activating institutional components to ensure the sustainability of future-oriented financial decentralization. Research methods. The study employs an institutional approach to define the role of formal and informal institutions in shaping the incentive system for economic agents and public authorities; systemic analysis to examine the new configuration of economic power and the correlation between macroeconomic reforms and micro-level investment instruments; and quantitative-comparative analysis to assess the financial capacity of territorial communities and benchmark national institutional solutions against international experience (NATO standards). The results. The study established that DEP in Ukraine operates under dual institutional transformation (war and Euro-integration). The formation of a new configuration of economic power, through the multiplicative effect of engaging public-private partnerships and modernizing corporate governance of state-owned companies, will promote the decentralization of investments into municipal ecological projects. The necessity of implementing highly binding mechanisms to counteract internal institutional risks is substantiated. Furthermore, financing environmental security through eco-modernization, EBRD GEFF instruments, and additional financial incentives will create a decentralized environmental effect.
Bharath M. B, Ashwni S S, Mamatha M, Sowjanya S · 6 authors
With increasing dependence on AI for medical imaging diagnostics, privacy concerns and strict regulations continue to restrict data sharing across healthcare institutions. To address this, we propose a novel framework that enables cross-institutional collaboration without compromising sensitive patient information. Our system integrates federated learning with advanced privacy-preserving techniques, including homomorphic encryption, secure aggregation, differential privacy, and zero-knowledge proofs. Hospitals retain their data locally and contribute encrypted, noise-added model updates, ensuring that raw data never leaves the premises. Secure aggregation and encryption prevent any entity, including the central server, from accessing individual contributions. Differential privacy introduces mathematically bounded noise to mitigate risks from inversion and membership attacks. Meanwhile, zero-knowledge proofs allow clients to verify the legitimacy of their training process and updates without revealing internal computations or data. This layered privacy defense effectively counters gradient inversion, model poisoning, and membership inference attacks, all while maintaining strong diagnostic performance. Evaluated on real-world medical imaging datasets, our method balances accuracy with compliance to privacy laws like HIPAA and GDPR. The proposed architecture offers a scalable and trustworthy approach to enable AI-driven diagnostics across hospitals, ensuring patient confidentiality is never compromised.
The increasing amount of heterogeneous enterprise data has catalysed an expedient requirement of confiding, scalable, and privacy-preserving analytics structures. When data is distributed among various stakeholders, traditional data lake houses are prone to data integrity, provenance, and governance problems as well as secure model training. This paper seeks to overcome these difficulties by introducing a Trustworthy Data Lakehouse Architecture which combines Federated Learning (FL) with Blockchain-enabled governance to enhance safe, auditable and regulation compliant data analytics. The framework designed includes a built-in metadata layer, decentralized model-training pipeline, immutable ledger, based on blockchain and data provenance, and the privacy protection mechanisms of differential-privacy. Multi-organization collaboration without raw data exchange is possible thanks to Federated Learning, and end-to-end trust is ensured by blockchain which supports consensus-based validation, lineage tracking based on tamper-proof, and access control with smart-contracts. Experimental analysis is used to show that there are data reliability, model accuracy, latency, and confidentiality improvements over traditional centralized lake houses. The presented solution opens up a strong base of constructing transparent, secure and scaled out data ecosystems applicable to finance, healthcare, supply chain among other sensitive areas.
The study examined the connectedness among bitcoin, green bonds (represented by the US S&P Green Bond Index), renewable energy (represented by the OMX Biofuel Index), and gold, utilizing a novel quantile connectedness approach from 14 November 2017 to 30 May 2024. This approach contributes to understanding the transmission mechanisms, influence, and connectedness among the bitcoin, green bond, renewable energy, and gold markets. The result indicates that significant values appear at specific intervals. A significant spike was observed at specific intervals around 2019, mainly due to the trade war between the U.S. and China. A subsequent shock occurred between 2020 and 2021, driven by the COVID-19 pandemic. Moreover, the US credit crisis exacerbated volatility spillovers and financial contagion across markets, worsening these effects in 2023 and intensifying volatility spillovers and financial contagion across markets, exacerbating their outcomes. Additionally, the results suggest that Bitcoin primarily serves as a receiver of shocks. At the same time, the green bond transmits the shocks, and renewable energy and gold have switched between transmission and receiving shock roles during the period. The findings offer valuable insights into sustainable portfolio construction, highlighting that green bonds serve as primary transmitters of shocks and suggest a role as diversification anchors during market stress. Additionally, recognizing Bitcoin as a shock absorber and the shifting roles of renewable energy and gold help investors optimize risk-hedging strategies and enhance portfolio resilience across varying market conditions. This indicates that understanding how these assets correlate across various market scenarios is crucial to maximizing portfolio performance while accounting for sustainability constraints.
The article explores the institutional paradigm of the transformation of the global financial architecture under the conditions of digitalization of the global economy. It is substantiated that the proliferation of digital financial technologies, including fintech innovations, crypto-assets, decentralized finance (DeFi), and central bank digital currencies (CBDCs), generates profound structural shifts in the functioning of the global financial system and necessitates a reconsideration of the role of key institutions of international financial governance. The study analyzes the evolution of the roles of central banks, international financial institutions, national regulators, and private financial technology companies in shaping the new global financial landscape. It is determined that central banks are gradually transforming from traditional monetary regulators into key architects of digital financial infrastructure, while private fintech and BigTech companies are becoming systemically important actors capable of influencing payment systems, financial inclusion, and cross-border financial flows. Particular attention is devoted to the analysis of contemporary global trends in the implementation of CBDCs, the development of crypto-asset markets, and decentralized financial platforms. It is demonstrated that these processes are forming a hybrid model of financial globalization that combines elements of centralized regulation with decentralized financial mechanisms. The article highlights key initiatives of international coordination and regulatory harmonization implemented within the frameworks of the Bank for International Settlements (BIS), the International Monetary Fund (IMF), the Financial Stability Board (FSB), and the G20, aimed at ensuring financial stability, cybersecurity, and preventing regulatory arbitrage. Based on the conducted analysis, an institutional model for the transformation of the global financial architecture is proposed, grounded in the integration of international standardization, public–private partnership, and multi-stakeholder interaction. It is proven that the effectiveness of the digital transformation of the global financial system depends on the capacity of international institutions to adapt regulatory approaches to dynamic technological changes and to ensure a balance between innovation, financial stability, and economic security.