This research explores the distributed database security storage and access control scheme based on IPFS and blockchain for the privacy issues such as sensitive data leakage and account security under the rapid development of Internet technology. The research background focuses on the contradictory status quo of data value enhancement and black-market data trading in the fields of intelligent medical care and unmanned driving, etc. Although the existing database security technology has made progress in encryption algorithms, dynamic protection, etc., it is still faced with the challenges of performance bottleneck and fine-grained access control of centralized architecture. The research aims to integrate the advantages of IPFS distributed storage and the tamper-proof characteristics of blockchain to construct a new type of secure storage system. Through theoretical analysis of IPFS peer-to-peer file system architecture, blockchain six-layer model (data layer, network layer, consensus layer, etc.) and AES/SM4 encryption algorithms, a system solution integrating blockchain smart contract and IPFS storage is designed: SM4 encrypts the original data and then stores it in IPFS, and achieves traceability through the blockchain record hash, and introduces the proxy re-encryption based on the identity technology to Realize dynamic access control. Experiments comparing the performance of MongoDB and IPFS show that in 5000 transactions, the delay of IPFS mode 12 nodes is reduced by 1.71 times compared with 6 nodes, which is significantly better than that of MongoDB's by 1.22 times; in the throughput test, IPFS increases linearly with the increase of nodes, while MongoDB decreases after the peak value. The study confirms that the combination of IPFS and blockchain can effectively reduce transaction latency by 31%, improve throughput by 30%, and safeguard the security of the whole data lifecycle through cryptographic technology. The results provide a decentralized security framework for distributed databases, with both theoretical innovation and engineering application value, which is of great practical significance for highly sensitive data fields such as healthcare and finance.
Watermarking protocols represent a possible solution to the problem of digital copyright protection of content distributed on the Internet. Their implementations, however, continue to be a complex problem due to the difficulties researchers encounter in proposing secure, easy-to-use and, at the same time, “trusted third parties” (TTPs)-free solutions. In this regard, implementations based on blockchain and smart contracts are among the most advanced and promising, even if they are affected by problems regarding the performance and privacy of the information exchanged and processed by smart contracts and managed by blockchains. This paper presents a watermarking protocol implemented by smart contracts and blockchain. The protocol uses a “layer-2” blockchain execution model and performs the computation in “trusted execution environments” (TEEs). Therefore, its implementation can guarantee efficient and confidential execution without compromising ease of use or resorting to TTPs. The protocol and its implementation can, thus, be considered a valid answer to the “trilemma” that afflicts the use of blockchains, managing to guarantee decentralization, security, and scalability.
Open access
Advanced Steganography and Watermarking Techniques
This research investigates the effect of social media sentiment on the cryptocurrency market, particularly focusing on Bitcoin and Ethereum. Using TensorFlow as a machine learning tool, we developed a sentiment index from 66,582 Reddit posts about Bitcoin and 23,231 about Ethereum, collected in 2022. The sentiment scores, ranging from -1 (negative) to 1 (positive), were categorized into positive, neutral, and negative classes and analyzed alongside daily return and volatility metrics for both cryptocurrencies using a Vector Autoregression (VAR) model. Our study identifies significant impacts of social media sentiment on cryptocurrency markets. Specifically, Bitcoin’s returns show a heightened sensitivity to negative sentiment, whereas Ethereum’s returns remain unaffected by any sentiment type. However, the volatility of both cryptocurrencies is affected by neutral sentiment. These findings highlight distinct behavioral patterns across cryptocurrencies and uncover a bidirectional relationship between market dynamics and social media sentiment. This study offers novel insights into how public perception influences digital asset markets, thereby contributing to the behavioral finance literature and providing practical implications for investors and policymakers.
Tokenization of real-world assets (RWA) is reshaping capital-markets infrastructure by embedding traditionally illiquid instruments—ranging from private-equity stakes to commercial real estate—within programmable digital tokens on distributed-ledger networks. This paper investigates three interlocking dimensions of this transformation. First, it dissects the legal and operational challenges that arise as asset rights migrate from paper certificates to cryptographically secured ledgers, highlighting jurisdictional uncertainty, fragmented custody rules, and the need for harmonized disclosure standards. Second, it evaluates emerging smart-contract governance models—including multi-signature escrow, on-chain compliance oracles, and upgradeable proxy contracts—and assesses their effectiveness in enforcing regulatory constraints, mitigating counter-party risk, and sustaining asset-life-cycle events such as corporate actions or rental-income distributions. Third, it analyzes the democratizing potential of tokenization, demonstrating how fractional ownership and 24/7 secondary liquidity can lower minimum investment thresholds, widen geographic reach, and broaden participation beyond accredited investors, while also outlining the attendant risks of market fragmentation and algorithmic discrimination. Using a mixed-methods approach that couples comparative legal analysis with event-study evidence from pilot tokenized-asset offerings, the paper offers a governance framework that balances innovation incentives with systemic-risk safeguards. The findings contribute to policy debates on digital-asset regulation, inform institutional-design choices for custodians and exchanges, and chart a research agenda for measuring tokenization’s long-run impact on market efficiency, financial inclusion, and asset-pricing dynamics.
Cryptocurrency is a digital innovation in the financial sector that has revolutionized the global transaction system through blockchain technology. One of the main challenges in the crypto domain today is determining the price of cryptocurrencies, which are highly volatile. Ethereum, one of the largest cryptocurrencies, exhibits complex volatility patterns that require a robust predictive system. This study aims to compare the performance of the standard XGBoost algorithm with XGBoost optimized using Bayesian Optimization in predicting daily Ethereum prices based on time series data from 2016 to June 2025. The dataset includes price-related features such as open, high, low, volume, and percentage price change. The modeling process consists of several stages including feature engineering, time series-based data splitting, and model training. Model performance was evaluated using three primary metrics: MAE, RMSE, and R² Score. The evaluation results show that the standard XGBoost model achieved an MAE of 80.8926 (3.12%), RMSE of 114.1457 (4.40%), and an R² Score of 0.9723. Meanwhile, the optimized model using Bayesian Optimization achieved an MAE of 70.7241 (2.73%), RMSE of 102.5334 (3.96%), and an R² Score of 0.9777. These results indicate that Bayesian Optimization helps improve the model's prediction accuracy. This study concludes that the XGBoost model with a Bayesian optimization approach yields superior and more effective performance in forecasting Ethereum prices based on time series data.
Maksym Lazirko, Deniz Appelbaum, Miklos A. Vasarhelyi
Cryptocurrency exchanges face increasing pressure to demonstrate reserve adequacy following platform failures, yet current Proof of Reserves (PoR) systems suffer from incomplete verification approaches that examine either on-chain or off-chain assets separately. This study introduces the Double-Helix Framework, a verification methodology that integrates on-chain blockchain analysis with off-chain consensus algorithms to provide complete assessment of exchange financial positions. The framework employs parallel verification strands that simultaneously validate blockchain-recorded transactions and off-chain financial information, creating a unified assessment mechanism that addresses the verification gaps in existing PoR systems. The framework's integration of traditional auditing principles with distributed ledger verification creates new possibilities for regulatory compliance and investor protection in digital asset management. This framework has implications for accounting practice, suggesting that comprehensive cryptocurrency audits require verification approaches that extend to on-chain, off-chain, and intersecting transactions that have varying degrees of separation between ledgers.
The rapid progress in artificial intelligence technologies in recent years has been largely driven by advances in reinforcement learning (RL). RL methods have proven to be highly effective in solving many practical problems. Distributed ledger technologies are finding wide application in the internet of things (IoTs), providing new approaches to solving problems of traditional IoT systems. Consensus is a fundamental component of distributed ledger technologies, responsible for ensuring data consistency between nodes, its security and accuracy. This paper is devoted to the study of the optimal choice of blockchain consensus protocol for IoT networks based on a combination of multi-criteria decision making (MCDM) and RL methods. The paper discusses the potential of merging MCDM and RL methods for selecting blockchain consensus protocols in IoT networks. It suggests a combined framework for effective protocol selection and management.
Open access
Blockchain Technology Applications and Security
Advanced Research in Systems and Signal Processing
In the modern era, the use of blockchain technology has been growing rapidly, where Ethereum smart contracts play an important role in securing decentralized application systems. However, these smart contracts are also susceptible to a large number of vulnerabilities, which pose significant threats to intelligent systems and IoT applications, leading to data breaches and financial losses. Traditional detection techniques, such as manual analysis and static automated tools, suffer from high false positives and undetected security vulnerabilities. To address these problems, this paper proposes an Artificial Intelligence (AI)-based security framework that integrates Generative Adversarial Network (GAN)-based feature selection and deep learning techniques to classify and detect malware attacks on smart contract execution in the blockchain decentralized network. After an exhaustive pre-processing phase yielding a dataset of 40,000 malware and benign samples, the proposed model is evaluated and compared with related studies on the basis of a number of performance metrics including training accuracy, training loss, and classification metrics (accuracy, precision, recall, and F1-score). Our combined approach achieved a remarkable accuracy of 97.6%, demonstrating its effectiveness in detecting malware and protecting blockchain systems.
This study aims to examine the legal gaps and regulatory challenges in enforcing criminal liability against perpetrators of crimes within Indonesia’s crypto ecosystem, particularly in the context of Decentralized Finance (DeFi), smart contracts, and decentralized digital asset trading platforms. The research employs a normative juridical approach using statutory and conceptual methods. The findings indicate that current criminal law instruments, such as Article 378 of the Criminal Code, Article 28 paragraph (1) of the Electronic Information and Transactions (ITE) Law, Law No. 8 of 2010 on the Prevention and Eradication of Money Laundering (TPPU), and Law No. 10 of 1998 on Banking, are inadequate to address the unique and complex characteristics of crypto-related crimes. These crimes are anonymous, cross-jurisdictional, and difficult to trace due to the absence of centralized authority. As a result, the existing legal framework fails to provide sufficient victim protection and leads to weak law enforcement effectiveness. This legal vacuum also hampers the state's ability to respond to the growing digital threats and creates legal uncertainty in the expanding crypto space. Therefore, this study recommends the formulation of specific criminal regulations that comprehensively define digital assets, legal subjects within decentralized systems, and new criminal offenses relevant to crypto-related conduct. It also calls for the establishment of specialized institutions dedicated to investigating and prosecuting such crimes. These proposed regulations are expected to strengthen the national criminal justice system, making it more adaptive, fair, and effective in addressing the challenges posed by digital transformation.
Ignacio Ariel Del Monte, Juan de Lucio, Miguel Angel Sicilia Urban
This systematic review examines risk of Impermanent Loss (IL) in Automated Market Makers (AMMs) within the Decentralized Finance (DeFi) ecosystem, employing the PRISMA-S methodology. Our comprehensive search across the Web of Science and Scopus databases identified 38 relevant studies published between January 2020 and September 2024. The review reveals a predominant focus on Constant Product Market Makers (CPMMs), which comprise 55.7% of all mentions, underscoring their central role in DeFi markets. There are 9 underlying causes affecting IL risk and the most important ones are price volatility, asset imbalance, and risk/return management. According to our categorization, the most commonly used Mitigation Strategies are Investment Strategies, Decentralized Tools and Technologies, and Design and Management of Liquidity Pool, Hedging Strategies and Context Strategies. IL risk research is calculated equally theoretically and empirically (9 references for each) and there are 6 research papers that calculate in a mixed way. Only 13 research papers employ market data in their reviews and 7.9% of all papers measure IL risk quantitatively. We seek to focus our studies on a more detailed treatment of the risk of IL that will result in improvements to liquidity providers in DeFi.
Traditional centralized scholarship evaluation processes typically require students to submit detailed academic records and qualification information, which exposes them to risks of data leakage and misuse, making it difficult to simultaneously ensure privacy protection and transparent auditability. To address these challenges, this paper proposes a scholarship evaluation system based on Decentralized Identity (DID) and Zero-Knowledge Proofs (ZKP). The system aggregates multidimensional ZKPs off-chain, and smart contracts verify compliance with evaluation criteria without revealing raw scores or computational details. Experimental results demonstrate that the proposed solution not only automates the evaluation efficiently but also maximally preserves student privacy and data integrity, offering a practical and trustworthy technical paradigm for higher education scholarship programs.
The objective of this study is to define a compliance-first, conceptually generalisable architecture for a multi-agent artificial intelligence platform integrated with distributed ledger technology, designed to be domain-, deployment-, and vendor-agnostic. It addresses a persistent shortcoming in current AI deployments, where compliance is often treated as a secondary concern, applied retroactively through prompt engineering rather than embedded within the foundational design. The proposed model encodes regulatory, governance, and ESG requirements into an objective-under-constraints framework, ensuring that all specialised agents operate within legally admissible and verifiably auditable parameters prior to any domain-specific implementation. A DAG-based verification layer is incorporated to enable scalable, low-latency, and cost-efficient operation while preserving evidentiary integrity. The analysis evaluates the feasibility of this conceptual model to support sustainable, rapid-deployment vertical applications without inducing vendor lock-in, preserving operational neutrality, and ensuring environmental accountability. The findings suggest that integrating compliance, ESG metrics, and agent specialisation at the architectural level provides a transferable foundation for cross-domain AI-DLT infrastructures.
Recent research has introduced a decentralized autonomous organization (DAO) managing a house – No1s1 (no-ones-one). While the concept has been technically implemented, it remains open how to govern and maintain the house in a decentralized manner. In this study, we investigate how a DAO can govern and maintain physical assets. For this, we use a Design Science Research methodology coupled with a requirements-driven approach and demonstrate through an artifact how such a governance and maintenance system can be implemented. The proposed system features two tokens rewarding active interaction with the physical house leading to a local community. We evaluate the system with a scenario-based study with users showing how the community can govern and maintain the house. Thus, the paper contributes to a better understanding on how a DAO with physical assets can be governed and interact with these assets, and to the better understanding of blockchain governance in general.
Gennaro Avitabile, Vincenzo Botta, Daniele Friolo, Ivan Visconti
Balancing immutability and compliance with regulations stands as a significant challenge in the realm of blockchain technology applications. Due to the increase of data-protection requirements (e.g., the GDPR in the EU), it is essential to address the problem of deleting data from a blockchain without compromising the security and transparency of the blockchain itself. Several works proposed techniques to address the data redaction problem. In their seminal work, Ateniese et al. [EuroS&P 2017] were the first to propose a redactable blockchain. Their approach focuses on permissioned blockchains and they showed how to change the content of a transaction without breaking the chaining among blocks by using special cryptographic hash functions (i.e., chameleon hash functions) and secure multi-party computation. We observe that the redaction technique of Ateniese et al. does not take into account the possibility that the blockchain supports smart contracts and that a redaction of a transaction might leave inconsistencies in the logic of the contracts, making some remaining non-redacted transactions invalid, and, more in general, the state of a smart contract inconsistent with the content of transactions. We find this choice rather limiting since decentralized and publicly verifiable computation guaranteed by smart-contract-enabled blockchains is necessary for modern (i.e., Web3) applications. To overcome the above limitations of the applicability of the redaction techniques of Ateniese et al., we propose a redaction technique with wider applicability that leverages succinct non-interactive arguments of knowledge (SNARKs) to realize what we call a proof-of-consistency .
Kiana Kiashemshaki, Elvis Nnaemeka Chukwuani, Mohammad Jalili Torkamani, Negin Mahmoudi
Blockchain technology offers a promising foundation for modernizing E-Voting systems by enhancing transparency, decentralization, and security. Yet, real-world adoption remains limited due to persistent challenges such as scalability constraints, high computational demands, and complex privacy requirements. This paper presents a comparative framework for analyzing blockchain-based E-Voting architectures, consensus mechanisms, and cryptographic protocols. We examine the limitations of prevalent models like Proof of Work, Proof of Stake, and Delegated Proof of Stake, and propose optimization strategies that include hybrid consensus, lightweight cryptography, and decentralized identity management. Additionally, we explore the novel role of Large Language Models (LLMs) in smart contract generation, anomaly detection, and user interaction. Our findings offer a foundation for designing secure, scalable, and intelligent blockchain-based E-Voting systems suitable for national-scale deployment. This work lays the groundwork for building an end-to-end blockchain E-Voting prototype enhanced by LLM-guided smart contract generation and validation, supported by a systematic framework and simulation-based analysis.
The greatest advantage that Web3 applications offer over Web 2.0 is the evolution of the data access layer. Opaque, centralized services that compelled trust from users are replaced by trustless, decentralized systems of smart contracts. However, the public nature of blockchain-based databases, on which smart contracts transact, has typically presented a challenge for applications that depend on data privacy or that rely on participants having incomplete information. This has changed with the introduction of confidential smart contract networks that encrypt the memory state of active contracts as well as their databases stored on-chain. With confidentiality, contracts can more readily implement novel interaction mechanisms that were previously infeasible. Meanwhile, in both Web 2.0 and Web3 applications, the user interface continues to play a crucial role in translating user intent into actionable requests. In many cases, developers have shifted intelligence and autonomy onto the client, leveraging web technologies for computation, graphics, and networking. Web3’s reliance on such frontends has revealed a pain point though, namely that decentralized applications are not accessible to end users without a persistent host serving the web application. Here we introduce the Non-Fungible Program (NFP) model for developing self-contained frontend applications that are distributed via blockchain, powered by web technology, backed by encrypted databases, and controlled by confidential smart contracts. Access to frontend code, as well as backend services, is controlled and guaranteed by smart contracts according to the NFT ownership model, eliminating the need for a separate host. By extension, NFP applications bring interactivity to token owners and enable new functionalities—such as authorization mechanisms for oracles, supplementary web services, and overlay networks—in a secure manner. In addition to releasing an open-source software development kit for building NFPs, we demonstrate the utility of NFPs with an interactive Bayesian game implemented on Secret Network.
Blockchain technology has become an essential tool for enhancing reliability and security across several industries, including the healthcare sector. In this work, we propose and implement an Ethereum-based blockchain framework to decentralize electronic health records (EHRs) at Tumakuru Siddaganga Hospital. The system establishes an append-only chain of transaction blocks that guarantees the confidentiality, auditability, and integrity of patient health records. By design, only authorized healthcare professionals can access patient data, and even then, only with the patient’s explicit consent—ensuring a privacy-preserving access model. Our approach demonstrated a 40% reduction in data access delays and eliminated unauthorized access attempts through smart contract-based access control. The decentralized nature of the framework reduces reliance on centralized databases, significantly lowering the risk of data tampering and breaches. Additionally, the implemented consensus protocol ensures that only verified transactions are recorded, maintaining consistency across distributed nodes. Compared to traditional systems, our blockchain-based solution improved the traceability of health data access events by 100%, ensuring transparency and accountability. These findings validate that blockchain technology can substantially enhance data sharing, integrity, and patient control in modern healthcare systems.
The deployment of artificial intelligence in healthcare is increasingly constrained by privacy, equity, and regulatory compliance challenges, especially in multilingual and cross-border contexts.Traditional centralized machine learning approaches are limited by restrictions on patient data sharing, raising both ethical and legal concerns.Federated learning offers a promising solution by enabling distributed training across institutions without transferring raw data, yet ensuring trust and privacy in federated systems remains a critical barrier.This study proposes a novel framework that combines transformer architectures with encrypted federated datasets anchored by blockchain zero-knowledge proofs (ZKPs) to achieve privacy-preserving, equitable, and multilingual healthcare diagnostics.Transformer-based models, known for their strength in natural language processing and multimodal learning, are adapted to operate on encrypted federated datasets spanning diverse linguistic and demographic contexts.Blockchain provides a decentralized trust layer, while zero-knowledge proofs ensure verifiable model updates without exposing sensitive patient information.This combination allows healthcare providers to collaboratively train diagnostic models that maintain strong predictive performance while adhering to strict privacy guarantees.The framework also advances health equity by enabling multilingual diagnostics that address disparities in underrepresented populations.By integrating explainability mechanisms, stakeholders gain insights into model reasoning across diverse cultural and linguistic datasets.Case applications in federated medical imaging, multilingual clinical notes, and genomic diagnostics highlight the framework's capacity to balance accuracy, privacy, and fairness.Overall, the integration of transformers, federated learning, and blockchain ZKPs represents a pathway toward trustworthy and equitable AI-driven healthcare, enabling collaborative innovation while safeguarding patient rights.
Blockchain technology has become a major focus in data security and reliability. A foundation for innovations such as non-fungible token (NFT), which opens up new opportunities in managing ownership of digital assets. We investigate NFTs in the form of voice, which is digital audio communication. During the COVID-19 pandemic, podcasts have been rampant, creating new business opportunities in digital media such as NFTs, which have explored and evolved in various markets; voice content has gained significant space in sales, promotion, and dissemination/innovation. This research presents a comprehensive analysis of NFTs from 2019 to 2022, focusing on the variable association consisting of the NFT category, the price of each of those NFT categories, NFT editions, and NFT marketplace. We used structural equation modeling (SEM) to clarify the relationship in partial least squares structural equation modeling (PLS-SEM). This study’s findings suggest that music enthusiasts seek NFTs based on the NFT category. Therefore, it is crucial for NFT creators, who are musicians too, to exercise caution when choosing the NFT category that is most popular among music enthusiasts. We suggest that the musicians creating NFTs should consider establishing appealing NFT categories to attract music fans and other collectors.
Khanh Phuong Nguyen, The Due Ong, Xinyu Zhang, Tra My · 6 authors
Ensuring sufficient financial resources for improving effective and efficient procurement of vaccines was highlighted by the Immunization Agenda 2030, which is particularly challenging for middle-income countries (MICs) that are ineligible for support from Gavi, the Vaccine Alliance. In response to the broader decentralization of the governance system and decreased international development aid, the central government of Vietnam decentralized the responsibility of financing the Expanded Program on Immunization (EPI) to provincial governments in 2023 but rapidly reversed it considering its following negative consequences, for example, severe vaccine stockouts and the largest decline in childhood immunization coverage in over 20 years. This paper analyzed the consequences closely linked to decentralization in Vietnam, the underlying reasons, and lessons for other MICs. To avoid recurrence, MICs should ensure the primary role of the central government in EPI financing and management, improve the local management capacity, and allocate more resources toward less-developed areas to narrow the intra-country disparities.
Yasir Habib, Noor Raida Abd Rahman, Shujahat Haider Hashmi, Minhaj Ali
Carbon neutrality and sustainable development goals have become globally imperative, as evidenced by the Paris Agreement, and the Nationally Determined Contributions mechanism. At the recently ended COP28 climate summit, the majority of the participating countries encountered these challenges through financial commitments to attain their objectives of carbon neutrality for sustainable development. Green finance and environmental decentralization play key roles in realizing these targets. The core focus of this study is to demystify the impacts of green finance and environmental decentralization on sustainable development by employing a panel dataset comprising 44 OECD countries, spanning 1995-2022. Ecological footprint serves as an indicator of sustainable development. Financial investment directed towards climate change mitigation and climate change adaptation technologies with alternative output-input green finance indicators are used as measures for green finance. A new index was devised that incorporates multiple indicators of environmental decentralization to gauge its influence on sustainable development. Using OLS, Oster coefficient stability, Lewbel 2SLS, and Kiviet instrumental variable techniques, our findings demonstrate that green finance significantly enhances sustainable development across countries. The empirical findings reveal that green finance and environmental decentralization exhibit a positive, statistically significant influence on sustainable development in OECD countries, while also playing a mitigating role in the reduction of environmental degradation. Considering these findings, it is imperative that OECD countries formulate and implement policies that foster green financing and empower local governments. This formulation and authorization are crucial for reducing pollution through the stimulation of innovation in climate change mitigation and adaptation technologies. In doing so, these policies will substantially reinforce the achievement of the United Nations' Sustainable Development Goals 9 and 12.
This study proposes a novel methodological framework integrating a LightGBM regression model and genetic algorithm (GA) optimization to systematically evaluate the contribution of COVID-19-related indicators to Bitcoin return prediction. The primary objective was not merely to forecast Bitcoin returns but rather to determine whether including pandemic-related health data significantly enhances prediction accuracy. A comprehensive dataset comprising daily Bitcoin returns and COVID-19 metrics (vaccination rates, hospitalizations, testing statistics) was constructed. Predictive models, trained with and without COVID-19 features, were optimized using GA over 31 independent runs, allowing robust statistical assessment. Performance metrics (R2, RMSE, MAE) were statistically compared through distribution overlaps and Mann-Whitney U tests. Permutation Feature Importance (PFI) analysis quantified individual feature contributions. Results indicate that COVID-19 indicators significantly improved model performance, particularly in capturing extreme market fluctuations (R2 increased by 40%, RMSE decreased by 2%, both highly significant statistically). Among COVID-19 features, vaccination metrics, especially the 75th percentile of fully vaccinated individuals, emerged as dominant predictors. The proposed methodology extends existing financial analytics tools by incorporating public health signals, providing investors and policymakers with refined indicators to navigate market uncertainty during systemic crises.
Due to the high flexibility and versatility, unmanned aerial vehicles (UAVs) are leveraged in various fields including surveillance and disaster rescue.However, in UAV networks, routing is vulnerable to malicious damage due to distributed topologies and high dynamics. Hence, ensuring the routing security of UAV networks is challenging. In this paper, we characterize the routing process in a time-varying UAV network with malicious nodes. Specifically, we formulate the routing problem to minimize the total delay, which is an integer linear programming and intractable to solve. Then, to tackle the network security issue, a blockchain-based trust management mechanism (BTMM) is designed to dynamically evaluate trust values and identify low-trust UAVs. To improve traditional practical Byzantine fault tolerance algorithms in the blockchain, we propose a consensus UAV update mechanism. Besides, considering the local observability, the routing problem is reformulated into a decentralized partially observable Markov decision process. Further, a multi-agent double deep Q-network based routing algorithm is designed to minimize the total delay. Finally, simulations are conducted with attacked UAVs and numerical results show that the delay of the proposed mechanism decreases by 13.39$\%$, 12.74$\%$, and 16.6$\%$ than multi-agent proximal policy optimal algorithms, multi-agent deep Q-network algorithms, and methods without BTMM, respectively.