This article explores the characteristics of data resources from the perspective of production factors, analyzes the demand for trustworthy circulation technology, designs a fusion architecture and related solutions, including multi-party data intersection calculation, distributed machine learning, etc. It also compares performance differences, conducts formal verification, points out the value and limitations of architecture innovation, and looks forward to future opportunities.
Xiangyun Tang, Minyang Li, Tao Zhang, Yijing Lin · 7 authors
In low-altitude networks, various aerial platforms such as unmanned aerial vehicles and airships cooperate to provide services including real-time monitoring, emergency response, and data collection. These platforms often operate with limited computing resources, restricted energy supply, and unstable wireless connectivity, making centralized data processing inefficient and prone to privacy risks. Federated Learning (FL) provides a promising solution by enabling multiple platforms to collaboratively train a shared model without exchanging raw data. However, the presence of Byzantine clients and a potentially malicious server poses serious threats to the robustness and trustworthiness of FL in such environments. Existing Byzantine-robust FL methods typically assume a semi-honest server and rely on auxiliary information such as clean datasets or known attacker ratios, which limits their applicability in dynamic and non-IID scenarios. In this paper, we propose zkFL, a Byzantine-robust FL framework that embeds zero-knowledge proofs to ensure verifiable aggregation under a malicious server. ZkFL allows clients to verify the correctness of server-side aggregation and dynamically adjusts client weights based on inference-guided detection, without relying on external datasets. Each round includes a zk-SNARK proof to guarantee aggregation integrity while preserving gradient privacy. Experiments demonstrate that zkFL exhibits strong robustness and verifiability in both IID and non-IID settings, outperforming prior methods, even in the presence of a malicious server.
Electric vehicles (EVs) are increasingly central to sustainable mobility, yet their integration into smart city infrastructures remains limited by centralized data systems that restrict scalability, threaten user privacy, and heighten exposure to cyber risks. Most prior work has concentrated on technical performance, with little attention to governance requirements such as accountability, regulatory compliance, and citizen trust. To address this gap, this study develops a decentralized EV data management framework based on IOTA’s Tangle, a distributed ledger designed for secure, scalable, and fee-less interactions across vehicles, charging stations, and urban digital platforms. Using a comparative design, conventional blockchain systems are evaluated against IOTA through simulation results. Findings show that the IOTA-based model sustains faster validation, eliminates fees, and supports higher throughput, while also aligning with governance principles reflected in policies such as the GDPR. The study demonstrates how distributed ledgers can advance both technical feasibility and trust in EV ecosystems.
У період суспільних криз та нестабільності стратегічний фандрайзинг стає важливим практичним інструментом забезпечення фінансової стабільності та соціально-економічного розвитку. Метою роботи є аналіз стратегічних підходів до інноваційного потенціалу фандрайзингу, які можуть бути успішно використані для ефективного залучення фінансування. Методологія. У процесі дослідження було застосовано загальні наукові методи: аналіз і синтез, порівняння, структурно-логічний метод, систематизація, узагальнення та абстракція. Наукова новизна. У дослідженні розглянуто проблематику стратегічного фандрайзингу у контексті інструментарію фінансової підтримки в умовах обмеженого ресурсного забезпечення. Розглянуто різні підходи до фандрайзингу, в тому числі – цифрового, традиційного, інноваційного та партнерського. Проаналізовано особливості конкретних стратегій та інструментів, таких, як краудфандинг, онлайн-платформи, технології non-fungible tokens (NFT). Встановлено, що комплексний підхід до фандрайзингу, який асимілює сучасні та традиційні методи, дозволяє забезпечити максимальну ефективність у досягненні фінансової стабільності. Висновки. У дослідженні обґрунтовано, що краудфандинг, онлайн-платформи та NFT позиціонуються надійними інструментами фандрайзингу, зокрема, на етапі формування стратегії діяльності компанії. Запропоновано ключові підходи до врегулювання ринку краудфандингових інвестицій. Доведено, що розглянуті види альтернативного фінансування та підтримки вбачаються дієвим засобом трансформації підходів до фінансування інноваційних проєктів. Проведена у статті аналітика дозволяє ідентифікувати переваги моделей фандрайзингу, а також виділити дотичні ризики та виклики, що дозволить розробляти та впроваджувати ефективні стратегії їх нівелювання.
The explosive popularity of blockchain-based gaming has brought non-fungible tokens (NFTs) into the fray, as tradable collectibles that connote not only monetary worth but an experience as well. In contrast to traditional in-game items, gaming NFTs exist in two domains of use, on-chain marketplaces and off-chain ecosystems, providing new twists to their valuation. This paper analyzes the approach, issues, and possible developments of the NFT gaming collectibles valuation by consolidating the knowledge in economics, data science, and digital games research. We then present a taxonomy of value drivers, gameplay utility, rarity, provenance, community effects, and tokenomics, to develop a conceptual background. We then compare the relative advantages and (*) disadvantages of popular valuation methods, including hedonic pricing models, repeat-sales indices, machine learning, and liquidity-adjusted approaches. We also identify structural issues of this nascent market, including wash trading, fragmentation of liquidity, evolving product utility, mutability of metadata, and regulatory uncertainty. Critical synthesis helps us see how valuation practices are susceptible to abuse on both sides: how we can abuse the market, but also how we can design the technical aspects of markets to cheat. Lastly, we discuss research directions in the form of standardized benchmarks, dynamic valuation frameworks, interoperability-adjusted models, and integrity checks. The paper will contribute to both the academic and practice worlds as it will provide a reproducible course of action by scholars, game development companies, investors, and marketplaces interested in designing open and sustainable valuation methods for the gaming NFTs.
Few studies examine whether the gap-filling effect, whereby the maturity structure of local government debt shapes corporate debt maturity, also holds in emerging, bank-dominated economies. Using data on Chinese local government financing vehicle (LGFV) bonds from 2009 to 2023, this study examines how the maturity structure of local government debt influences that of corporate debt. The results reveal a pronounced gap-filling effect, whereby longer local government debt maturities systematically crowd out long-term credit resources, raise corporate financing costs, and prompt firms to shorten their debt maturities. This effect is more salient during economic downturns and when local government debt levels are elevated. Heterogeneity analysis shows substantial variation in the gap-filling effect across regions, industries, and firm characteristics. In addition, firms with shorter debt maturities are found to face higher financial risks. This study contributes by providing policy evidence for fiscally decentralized emerging markets: local government maturity choices transmit to firms’ financing structure, implying that local debt management is instrumental for financial stability.
This study presents a comprehensive ten-year (2015–2024) evaluation of renewable energy development in Cameroon, emphasizing its intersection with Sustainable Development Goals (SDGs) and broader cross-sectoral development outcomes. Combining time-series analysis of national capacity data, policy content evaluation, and SDG-aligned simulation modeling, the paper assesses both technical and institutional trajectories of the energy transition. Key findings reveal a substantial increase in off-grid installations in underserved regions and a notable rise in grid-connected solar capacity—from 0 MW in 2015 to 63 MW by 2024—driven largely by post-2017 policy decentralization. Hydropower remains the dominant source, but the solar sector exhibited accelerated growth, contributing to enhanced rural electrification and public health infrastructure, with 27 % of rural health institutions now electrified. The renewable energy sector generated an estimated 3500 new jobs over the decade. An SDG alignment index applied across five targets indicates moderate but uneven progress, particularly for Goals 7 (affordable and clean energy), 3 (good health and well-being), and 13 (climate action). Scenario-based simulations underscore that policies promoting decentralized innovation and integrated energy planning significantly enhance rural energy access and socio-economic resilience. However, persistent financing barriers and institutional fragmentation constrain broader impact. The study offers a replicable analytical framework for data-driven, SDG-oriented assessment of energy transitions in Sub-Saharan Africa, contributing actionable insights for sustainable energy policy design in low-resource contexts. • Renewable energy in Cameroon grew steadily between 2015 and 2024. • Off-grid solar access expanded, boosting rural electrification progress. • Policy reforms accelerated decentralized energy access and regulation. • RE growth improved health, jobs, and equity across sectors. • A roadmap aligns RE planning with SDG targets for Cameroon.
René Dávila, Everardo Bárcenas, Rocío Aldeco-Pérez
Formal verification entails testing software to ensure it operates as specified. Smart contracts are self-executing contracts with the terms of the agreement directly written into lines of code. They run on blockchain platforms and automatically enforce and execute the terms of an agreement when meeting predefined conditions. However, Smart Contracts, as software models, often contain notable errors in their operation or specifications. This observation prompts us to conduct a focused study examining related works published across various sources. These publications detail specifications, verification tools, and relevant experiments. Subsequently, this survey proposes an alternative formal verification based on description logic.
With the rapid adoption of diffusion models for visual content generation, proving authorship and protecting copyright have become critical. This challenge is particularly important when model owners keep their models private and may be unwilling or unable to handle authorship issues, making third-party verification essential. A natural solution is to embed watermarks for later verification. However, existing methods require access to model weights and rely on computationally heavy procedures, rendering them impractical and non-scalable. To address these challenges, we propose NoisePrints, a lightweight watermarking scheme that utilizes the random seed used to initialize the diffusion process as a proof of authorship without modifying the generation process. Our key observation is that the initial noise derived from a seed is highly correlated with the generated visual content. By incorporating a hash function into the noise sampling process, we further ensure that recovering a valid seed from the content is infeasible. We also show that sampling an alternative seed that passes verification is infeasible, and demonstrate the robustness of our method under various manipulations. Finally, we show how to use cryptographic zero-knowledge proofs to prove ownership without revealing the seed. By keeping the seed secret, we increase the difficulty of watermark removal. In our experiments, we validate NoisePrints on multiple state-of-the-art diffusion models for images and videos, demonstrating efficient verification using only the seed and output, without requiring access to model weights.
The integration of blockchain technology into healthcare presents a paradigm shift for secure data management, enabling decentralized and tamper-proof storage and sharing of sensitive Electronic Health Records (EHRs). However, existing blockchain-based healthcare systems, while providing robust access control, commonly overlook the high latency in user-side re-computation of hashes for integrity verification of large multimedia data, impairing their practicality, especially in time-sensitive clinical scenarios. In this paper, we propose FAITH, an innovative scheme for \underline{F}ast \underline{A}uthenticated and \underline{I}nteroperable mul\underline{T}imedia \underline{H}ealthcare data storage and sharing over hybrid-storage blockchains. Rather than user-side hash re-computations, FAITH lets an off-chain storage provider generate verifiable proofs using recursive Zero-Knowledge Proofs (ZKPs), while the user only needs to perform lightweight verification. For flexible access authorization, we leverage Proxy Re-Encryption (PRE) and enable the provider to conduct ciphertext re-encryption, in which the re-encryption correctness can be verified via ZKPs against the malicious provider. All metadata and proofs are recorded on-chain for public verification. We provide a comprehensive analysis of FAITH's security regarding data privacy and integrity. We implemented a prototype of FAITH, and extensive experiments demonstrated its practicality for time-critical healthcare applications, dramatically reducing user-side verification latency by up to $98\%$, bringing it from $4$ s down to around $70$ ms for a $5$ GB encrypted file.
Non-fungible tokens (NFTs) have garnered attention because of their potential to disrupt traditional business models in various industries. This study provides insights into the drivers of individuals' intentions to purchase NFTs by investigating the relationship between perceived value (scarcity, uniqueness, verifiability, and royalty), as well as facilitating conditions, social influence, individual differences, and personality traits, and the intention to purchase NFTs. Decision-makers, creators, and investors can benefit from understanding these drivers. The proposed model integrates constructs from multiple adoption frameworks and related NFT literature to analyze the individual determinants of NFT purchase intentions. This study utilized a survey to collect data from participants and employed the partial least squares structural equation modeling (PLS/SEM) technique to validate the proposed model empirically. The findings indicate that perceived value, facilitating conditions, social influence, individual differences, and personality traits significantly shape individuals’ intentions to purchase NFTs. Perceived scarcity, verifiability, and royalty were found to be positively associated with perceived value, whereas perceived uniqueness did not demonstrate a statistically significant relationship. Furthermore, the study suggests that individual differences and personality traits do not moderate the relationship between perceived value and NFT purchase intention. However, individual differences and personality traits are directly associated with NFT purchase intention.
Venkata Sivakumar Musam, Nagendra Kumar Musham, C Siva, S. Karimulla Basha · 6 authors
The field of healthcare monitoring has been revolutionized by a combination of cloud computing and IoT-enabled sensor networks that enable real-time data collection, storage and processing. However, because healthcare data is sensitive, strong security and privacy-preserving measures are required to stop unwanted access and preserve data integrity in multi-authority settings. To propose a collaborative and privacy-preserving sensor cloud architecture for safe, scalable, and fine-grained access control for healthcare monitoring systems by utilizing Zero-Knowledge Proofs (ZKP) and Multi-Authority Attribute-Based Encryption (MA-ABE). The proposed method combines ZKP for authentication and MA-ABE for attribute-based encryption to protect sensitive data. It uses gateways to securely aggregate and transmit data to the cloud, and elliptic curve cryptography to optimize performance. The framework decreased computational overhead by 40% while achieving notable gains in encryption (120 ms) and decryption (150 ms). Outperforming current approaches in healthcare data security and access management, classification accuracy reached 96.5% with improved privacy preservation (96%) and scalability (94%). The suggested system addresses privacy, scalability, and computational efficiency while incorporating cutting-edge cryptographic approaches to provide secure healthcare monitoring. Large-scale, real-time healthcare applications can benefit greatly from its strong solution.
Octara Pribadi, Johanes Terang Kita Perangin Angin, Erick Erick
The development of information technology has driven the need for stronger security systems and guaranteed privacy on web-based platforms. Traditional verification methods such as passwords and two-factor authentication are increasingly seen as insufficient in facing the growing complexity of cyber threats. Zero-Knowledge Proof (ZKP) emerges as an alternative solution that enables identity verification without revealing sensitive information. This research aims to implement ZKP technology in web-based systems to enhance user security and privacy. The method involves the implementation of a simple XOR-based ZKP algorithm to prove identity without disclosing personal data, as well as a three-step verification mechanism between the verifier and the prover. Implementation results show that the system can prevent attacks such as man-in-the-middle and replay attacks, while maintaining data confidentiality during the authentication process. This study also identifies challenges in system efficiency and verification process integrity and offers technical solutions to support broader ZKP integration in digital platforms. Thus, the use of ZKP in web-based systems holds great potential to improve user trust and security in the digital era.
Following the stablecoin legislation (GENIUS Act) enacted under the second Trump administration in 2025, blockchain has become core digital economy infrastructure. However, privacy risks from decentralization and transparency constrain adoption in regulated industries, requiring solutions that harmonize blockchain architecture with regulatory compliance. Existing research relies on reactive auditing or post-execution rule checking, which wastes computational resources or provides only basic encryption or access controls without comprehensive privacy compliance. The proposed Artificial Intelligence-enhanced Regulatory Proof-of-Compliance (AIRPoC) framework addresses this gap through a two-phase consensus mechanism that integrates AI legal agents with semantic web technologies for autonomous regulatory compliance enforcement. Unlike existing research, AIRPoC implements a dual-layer architecture where AI-powered regulatory validation precedes consensus execution, ensuring that only compliant transactions proceed to blockchain finalization. The system employs AI legal agents that automatically construct and update regulatory databases via multi-oracle networks, using SPARQL-based inference engines for real-time General Data Protection Regulation (GDPR) compliance validation. A simulation-based experimental evaluation conducted across 24 tests with 116,200 transactions in a controlled environment demonstrates 88.9% compliance accuracy, with 9502 transactions per second (TPS) versus 11,192 TPS for basic Proof-of-Stake (PoS) (4.5% overhead). This research represents a paradigm shift to dynamic, transaction-based regulatory models that preserve blockchain efficiency.
Urban transportation is under strain from growing congestion, environmental impact and inefficiencies in centralised ride-sharing systems. This work presents a decentralized peer - to - peer carpooling platform that can use blockchain technology and smart contracts to securely control the entire ride life cycle, without intermediaries. The system uses Ethereum smart contracts for transparent and tamper proof handling of ride request, acceptance, completion and payments thus eliminating the risks associated with the centralized controllers. A layer geospatial computation powered by OpenRouteService computes precise distances and fares, and interactive maps increase user trust in price. The prototype, implemented using Streamlit, Web3.py and Solidity, includes role-based dashboards for drivers and passengers, wallet-based authentication to prevent identity spoofing and optimized contract design to reduce gas costs. Presenting evidence of values for outputs through functional testing on a local Ethereum network, there are correct state transitions, approve stable transaction costs, stable and accurate of mappings are within +/-0.1 km of Google Maps. This platform provides a scalable and privacy respecting method for shared mobility with future expansions towards decentralised storage, advanced reputation systems and zk -proof based location privacy.
This article explores the application of financial technologies in the auditing of decentralized finance (DeFi) and how these technologies can improve the audit process. The research aims to identify the main challenges faced in DeFi auditing and to offer recommendations for strengthening audit methodologies using financial technologies (FinTech). The article discusses decentralized finance and its innovative management principles based on blockchain technology, which fundamentally transforms traditional financial systems. The study identifies key risks associated with DeFi, such as vulnerabilities in smart contracts and the lack of regulatory frameworks. The theoretical foundation includes a detailed analysis of the differences between traditional and decentralized finance, highlighting DeFi’s advantages – lower operational costs, greater accessibility, and transparency – while addressing regulatory and security challenges. The research methodology is based on scientific literature analysis and the classification of FinTech tools according to established criteria to evaluate the effectiveness of audit methods and their potential for improvement. The main findings show that FinTech tools, such as artificial intelligence (e.g., decision tree models) and blockchain technology, enhance the reliability and speed of DeFi audits while highlighting the need for a more robust regulatory system. This article is relevant due to the rapid growth of the DeFi sector and its importance in the digital transformation of the financial industry. Moreover, this research aims to classify the most widely used financial technology tools according to their functions and analyze their impact on traditional and decentralized finance auditing processes, offering insights for improving audit methodologies.
Open collaboration business models (OCBMs) and software have existed since the 1950s but have been plagued by work ethic and financial bottleneck due to inadequate monetization schemes. OCBMs provide a range of advantages, primarily in accelerating problem solving & innovation, reducing costs, and enhancing security and transparency. Companies could establish sustainable revenue streams while benefitting from broad tech or platform adoption. However, fair revshare and credit attribution have been persistent problems with OCBMs. Businesses such as Diaspora, Soul Force, Sun Microsystems' Open Solaris, and others have experienced unsustainable OCBM initiatives, especially in the open-source software area. Web3 ethos-based business models and blockchain technologies provide an opportunity to correct and enforce many of the monetization strains associated with open collaboration. By decentralizing operational and governance control, free markets nested within companies that run themselves can be realized. The basic philosophy for a meritocratic monetization system with fair credibility-revshare automation is discussed.
Betas from spot regressions are central to asset pricing and risk management, as measures of systematic risk. This paper develops a new estimation and inference framework for spot regressions by leveraging high-frequency candlesticks, extending conventional (open-to-close) returns with intra-period high/low prices. Specifically, I construct candlestick-based estimators of regression parameters, including spot beta, by minimizing a quadratic risk under a fixed-k asymptotic framework. I then develop a feasible hypothesis testing procedure for spot betas with correct asymptotic size. Simulation results show that the proposed estimator reduces estimation risk relative to return-based estimators, especially in small samples, and the test achieves notably higher power. I apply the framework to assess the market neutrality of Bitcoin using 1-minute data on IBIT and SPY, finding deviations from neutrality, particularly in high-volatility periods.