The aim of this research is to analyze the impact of Decentralized Finance (DeFi) platforms on the competitiveness of Iraqi private banks on the basis of the relationship between DeFi and the dimensions of competitiveness which are represented by operational efficiency, financial innovation and market share. This study used descriptive-analytical approach, and A questionnaire was distributed to employees of Iraqi private banks, who constituted the study sample and The study sample consisted of employees of Iraqi private banks. The data were analysed statistically with the SPSS software by appropriate statistical methods like correlation coefficient and regression analysis. The results of the research showed a positive and significant relationship between decentralized finance and banking competitiveness. In addition, the result of the regression analysis showed that the DeFi platforms had a significant effect on competitiveness, accounting for 59.2% of the variance (R-squared). The findings clearly show that decentralized finance helps to increase the operational efficiency and improve financial innovation, but with a moderate effect on market share. The study calls for Iraqi banks to embrace financial technology (FinTech) and improve their digital framework. Further, they need to be innovative and partner with FinTech firms to strengthen their competitive edge, given the fast pace of digital transformation.
Decentralized Autonomous Organizations (DAOs) offer a novel approach to collectively governing projects through a democratic mechanism facilitated by blockchain. DAOs allow members to put forward and vote on proposals, thereby shaping the organization’s future. However, low voter turnout is common in DAO decision-making, particularly in large and active DAOs, where the high volume of proposals makes it unrealistic to expect members to track all proposals. Abstentionism threatens the voting system's effectiveness and the legitimacy of the results. We consider that recommender systems can help boost voter engagement. This article details the design of a recommendation approach tailored to aid DAO members in identifying proposals of interest, alongside its implementation and evaluation. The design accommodates the domain constraints that render off-the-shelf recommendation approaches inadequate, namely that proposals are short-lived and can only be recommended while available for voting. To the best of our knowledge, this is the first study to examine recommendation in DAO governance. To carry out our research, we have compiled a dataset, made publicly available, covering 12 of the most active DAOs. We compare a baseline, specifically designed to accommodate DAO-specific constraints, against a range of recommendation techniques. The findings confirm that personalized recommenders can often anticipate voting preferences, significantly outperforming the baseline. In turn, given the limitations of offline evaluation, an online evaluation using A/B testing would also be needed to fully assess their impact on participation. We also discuss how their implementation must carefully incorporate fairness and transparency to ensure community trust and adoption. We believe that proposal recommender systems in DAOs not only could drive engagement improvements similar to those observed in other online collaborative projects, but can also provide insights for collective governance settings beyond blockchain, such as cooperative organizations or participatory budgeting platforms.
Introduction: Decentralized autonomous organizations (DAOs) are an emerging organizational form that operates entirely on blockchain infrastructure. Within a DAO, organizational governance rules are hardcoded in transparent and immutable smart contracts. In principle, these rules are intended to facilitate decentralized decision-making among token holders who collectively create, discuss, and vote on proposals that govern the organization. Despite their promise, the extent to which DAOs achieve true decentralization in practice remains unclear. This study addresses an underexplored area in the literature by empirically investigating key aspects of DAO governance, particularly the degree of decentralization and participant composition.Method: Network analysis is used to examine proposal voting coalitions among participants as a proxy for decentralization. Sentiment analysis is employed to assess trust among participants. The analysis draws on data from 54 DAOs, including 774 unique proposals and 13,085 associated token holder comments.Results: The findings indicate that DAOs may not achieve the level of decentralization originally envisioned. Moreover, decentralization and participant composition within governance structures play a critical role in shaping trust, voting participation, and overall financial performance in DAOs PracticalImplications: Although current voting mechanisms aim to reduce the dominance of large token holders (whales), DAOs may still fall short of the level of decentralization originally envisioned. Accordingly, more advanced voting mechanisms may be required to further mitigate coordination and strategic behavior in proposal voting. In addition, DAOs could benefit from adjusting the threshold requirements for the Foundation to improve accessibility for token holders and encourage broader participation. Leveraging artificial intelligence (AI) may also help streamline the voting process and improve the clarity of proposals.
With the rapid proliferation and interconnection of massive IoT devices, efficient and secure identity authentication has become a crucial prerequisite for ensuring communication security. Establishing trust among mutually untrusted devices remains a key research focus. Leveraging its tamper-resistance and traceability, blockchain technology has emerged as a foundational infrastructure for building trustworthy identity management systems. However, existing blockchain-based identity authentication schemes face critical challenges in large-scale IoT environments, including low authentication efficiency, complex certificate management, and risks of user privacy leakage. Achieving a balance among authentication efficiency, certificateless key management, and privacy protection remains a pressing challenge. In this paper, we propose a certificateless identity authentication scheme based on blockchain sharding. The scheme employs blockchain sharding to parallelize identity authentication across multiple shards, significantly enhancing overall efficiency. Within each shard, a certificateless public key cryptography (CL-PKC) scheme is adopted to eliminate certificate issuance and enable key generation via user interaction, thereby reducing key management overhead and improving security. For cross-shard authentication, a registration-based encryption (RBE) mechanism is utilized, allowing users to authenticate via their identity after registration. Any verifier can confirm the legitimacy of the authentication message solely based on the registration information and the user ID, ensuring transparency and public verifiability. Furthermore, a zero-knowledge proof-based verifiable credential (VC) selective disclosure mechanism is introduced, enabling users to reveal only the minimal necessary information required for authentication while protecting sensitive identity attributes. Experimental results demonstrate that the proposed scheme maintains high throughput under high-concurrency scenarios while effectively preserving user privacy.
Open access
2 source records
Cryptography and Data Security
Privacy-Preserving Technologies in Data
Advanced Steganography and Watermarking Techniques
Autonomous AI agents are beginning to occupy a position between analytical tools and transacting counterparties. They can interpret goals, call external tools, negotiate with other agents, access data and computation, and in some settings initiate payments or blockchain transactions. This development creates a distinct problem for financial markets: if software agents can act economically, market participants need infrastructure for identity, authorisation, payment, verification, reputation and accountability. This article develops the concept of agent-to-agent finance as the layer of machine-mediated financial interaction in which autonomous agents discover counterparties, purchase services, express transaction intent, execute payments and generate auditable evidence. The argument is not that blockchain is a universal substrate for finance, but that programmable settlement, smart wallets, decentralised registries and verifiable computation can address specific coordination frictions created by autonomous agents. Drawing on recent work on blockchain A2A payments, ERC-8004 agent registries, provenance-based wallets, deterministic inference, DeFi intent mining, and official evidence on AI adoption in financial services, the article situates agent-to-agent finance as an emerging form of financial market infrastructure. It argues that the decisive design question is bounded autonomy: how to let agents transact without making markets more opaque, fragile or unaccountable.
Prediction markets increasingly list contracts settling on an asset price that holders can move by trading the underlying. We build a model showing that such contracts transfer wealth from prediction-market liquidity traders to manipulators and harm price discovery in the underlying, even as it becomes more liquid. After the launch of Polymarket's five-minute Bitcoin contract, settlement-time spot order flow spikes, causing large price reversals after settlement. Manipulators capture a large amount of profit, mostly from retail. Manipulation is largely absent in the fifteen-minute contracts: lengthening the contract horizon removes it, providing the market-design remedy our model and evidence support.
The purpose of the research is to explore the latest trends in blockchain and cryptocurrency adoption. Cryptocurrency has been drawing the attention of individual investors. Although institutional investors had been hesitant to invest in cryptocurrencies due to lack of clarity regarding regulations, recent legislation encouraged them to add cryptocurrency to their investment portfolios. However, blockchain the underlying technology of cryptocurrency, has also drawn the attention of both companies and researchers. The aim of this study is to identify the latest trends through an analysis of publications on blockchain and cryptocurrency adoption. To achieve that, the study adopts a bibliometric approach by using both VOSviewer and Bibliometrix programs after obtaining the required dataset from Web of Science (WOS). The results exhibit the latest trends as well as both qualitative and quantitative statistics, such as the growth rate, density and relations among different studies on the subject.
Liu Hong Yuan Tom, Ruilin Wang, Hairui Wang, Ziqi Cao · 5 authors
This study examines the impact of social media sentiment on Bit-coin market volatility. While existing literature often relies on single-source data or isolated factors, this research introduces a novel three-source pricing framework that integrates Twitter-derived social media sentiment, investor leverage ratios, and historical market data. Using a Weighted Least Squares (WLS) regression model to address heteroscedasticity in financial time series, we analyze daily Bitcoin returns from 2021 to the first half of 2022. Our results indicate that both social media sentiment has a statistically significant positive effect on Bitcoin returns. The model successfully identified high-risk market conditions, as validated by the May-June 2021 crash. These findings demonstrate that social media sentiment has a huge impact on cryptocurrency markets.
The rapid development of blockchain has given rise to smart contracts that challenge traditional legal doctrine, even though the technology is crucial to supporting SDGs (Sustainable Development Goals) 9 and 16. Purpose: This study aims to analyze smart contract governance in Indonesia, Malaysia, and Thailand to support the achievement of the SDGs in the region. Method: A normative-comparative legal method is used with a socio-legal approach. This study examines the synchronization of regulations and the socio-institutional impacts. Results: The validity of smart contracts in the three countries is interpretative due to the lack of specific regulations. The self-executing and immutable nature triggers doctrinal tensions related to agreements and consumer protection, which are increased by the digital literacy gap. Conclusion: Smart contract governance in Southeast Asia requires an adaptive regulatory strategy that balances innovation and legal certainty. Suggestion: Authorities are expected to develop co-regulation-based regulations, strengthen digital institutions, and initiate regional legal standardization across ASEAN (Association of Southeast Nations). Contributions: The contribution is in the development of a blueprint for regional digital law harmonization that integrates aspects of dogmatic law with legal sociology. This study offers a model for ASEAN legal standardization that bridges technological innovation with social justice and provides indicators of institutional readiness replicated by developing countries in embracing an inclusive and sustainable digital economy.
Md. Safaet Hossain, Mohammad Shakibul Hasan Sakib, Md. Rayhan Ahmed Shis, Sakib Ahmed · 5 authors
Modern food supply chains, particularly those involving essential commodities like rice, often suffer from major challenges such as product fraud, inefficient record-keeping, and a lack of consumer trust. Traditional centralized systems are prone to data tampering, limited transparency, and poor traceability, making it difficult to verify the authenticity and origin of goods. To address these issues, our research introduces TraceRoot, a blockchain-based traceability framework designed to enhance transparency, accountability, and trust in agricultural supply chains.TraceRoot leverages the immutability and decentralization of blockchain technology to maintain a secure, distributed ledger that records every transaction and movement of goods across the supply chain. Each stakeholder including farmers, distributors, retailers, and consumers has role-based access to authenticated data through a user-friendly interface. The framework integrates smart contracts to automate transactions and digital signatures to verify the integrity of the data being uploaded, minimizing the risk of human error or manipulation
Deepak Gupta, Temur Eshchanov, Jabbarov Umarbek, Berdiyev Anvar Abduraxmanovich · 6 authors
The global halal industry faces critical challenges in ensuring product authenticity, supply chain transparency, and consumer trust. This chapter examines the transformative role of artificial intelligence (AI) technologies in revolutionizing halal product verification and market analysis. Through comprehensive analysis of machine learning, computer vision, blockchain integration, and advanced analytical techniques, we explore how AI-driven solutions address authentication challenges, detect adulteration, and enhance traceability across halal supply chains. The chapter synthesizes current research on deep learning applications for food fraud detection, spectroscopic analysis coupled with chemometrics, DNA barcoding for species identification, and distributed ledger technologies for certification management. We present frameworks for implementing AI-powered verification systems, discuss technological barriers and opportunities, and propose strategic recommendations for stakeholders.
Smart contracts have attracted rapid development and widespread application. Due to the complexity of real-world smart contracts, it is error-prone to correctly enforce all intended functionalities in code implementations, resulting in unintended functional behaviors and security issues in practice. Code-comment inconsistency detection has emerged as an important solution to these issues, which leverages the redundant functional specifications in comments to detect code implementations that violate developers' intentions. However, existing inconsistency detection solutions are typically pattern-based and limited to fixed types of inconsistencies, which prevents them from detecting the diverse inconsistencies between real-world code implementations and casually written comments. To bridge the gap, this paper presents SmartComment, the first technique that combines LLMs with program analysis techniques for detecting code-comment inconsistencies in smart contracts. SmartComment introduces an LLM-driven workflow which simulates real-world interactions between code reviewers and developers to identify inconsistencies. It incorporates various program analysis techniques into the workflow, including comment propagation and code context extraction for generating input context for inconsistency detection, as well as program variant generation and differential analysis for inconsistency confirmation. Our evaluation results show that SmartComment detects 203 valid inconsistencies from a dataset of 1,000 real-world contracts with a precision of 79.9%, highlighting its effectiveness in detecting prevalent and diverse real-world inconsistencies. Compared to previous work, SmartComment achieves both higher precision and recall, detecting over 90% of inconsistencies that existing methods fail to identify. Furthermore, an ablation experiment demonstrates the effectiveness of incorporating program analysis techniques into SmartComment, improving the F1-score from 58.7% to 81.3%.
The global dairy industry confronts a persistent structural challenge in operationalising food safety and animal welfare compliance. Manual inspection regimes and intermittent audits are demonstrably inadequate for the heterogeneous, geographically dispersed landscape of small-scale farming, where data integrity, real-time monitoring capability, and regulatory transparency are simultaneously compromised. This article presents GreenDairyChain, an integrated compliance innovation framework that synthesises four enabling technologies: GreenEdgeML (a lightweight TinyML inference engine optimised for microcontroller-class devices), Privacy-Preserving Federated Learning (FL) with Graph Attention Network (GAT)-based dynamic clustering, Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (ZK-SNARKs) for cryptographic compliance verification, and a Layer-2 Polygon zkEVM Blockchain with domain-specific smart contracts governing farm identity, violation detection, audit triggers, and licence management. GreenEdgeML executes multimodal sensor fusion across four signal modalities (body temperature, accelerometer activity, ammonia concentration, and milk pH) entirely on-device using 8-bit integer quantisation, consuming 64.6 KB RAM and 82.7 mW per inference cycle on the ESP32 platform. The FL engine employs GAT-based farm clustering with DBSCAN outlier exclusion to address non-IID data heterogeneity while maintaining Byzantine fault resilience. Compliance inferences are encoded as R1CS arithmetic circuits (14,240 constraints) and verified on-chain at O(1) cost through ZK-SNARK proofs generated in 1.25 seconds. Evaluated on the Shahhet28121 benchmark dataset across 16 biomarkers, the full system achieves 96.94% global classification accuracy, a 97.7% reduction in per-round communication payload (4.25 KB), and maintains classification accuracy above 90% under 20% Gaussian sensor noise. Ablation experiments confirm that each architectural component contributes independently to system performance. The findings carry implications for green business innovation, sustainable agriculture governance, and the design of trustworthy AI ecosystems in resource-constrained rural contexts.
Jianzhong Su, Mingxi Ye, Jiachi Chen, Yuhong Nan · 7 authors
With the rapid development of decentralized applications, many malicious actors exploit smart contract vulnerabilities for launching attacks. Moreover, as smart contracts utilize more state variables to support complex functionalities, some vulnerabilities require specific states to trigger (marked as vulnerable states), bringing new challenges to the vulnerability detection task. Although many smart contract fuzzers have been proposed for this task, they face limitations due to their inability to efficiently explore smart contract states. To address this challenge, we propose a novel fuzzer, Odyssey, with fine-grained state modeling and exploration, which increases the probability of reaching vulnerable states. We improve the efficacy of the fuzzer with two key mechanisms: (1) modeling an essential state space consisting of the variables related to sensitive operations to compress the exploration scope; (2) designing state-aware exploration strategies to identify test seeds that cover new state scope or cause new state transitions, to improve the efficiency of exploration. To evaluate the performance in vulnerability detection, we adopt Odyssey to a labeled benchmark consisting of 130 vulnerable contracts. Odyssey detects at least 70% more vulnerabilities than other fuzzers. Moreover, we evaluate Odyssey on a dataset that consists of 143 DApps (involving 437 contracts) from real-world security incidents. The experimental results demonstrate that state-aware feedback enhances the ability of Odyssey in state exploration by achieving 19% higher state coverage. Meanwhile, Odyssey totally finds 15 exploits of vulnerabilities from real-world attacks, showing its advantage in detecting real-world vulnerabilities.
A privacy-preserving compliance audit architecture for unmanned aerial vehicle (UAV) swarm operations. The central contribution is a deconfliction-to-containment reduction: rather than comparing n trajectories after the fact (a quadratic, disclosure-bound check), a planner assigns pairwise-disjoint spatial tubes before take-off and establishes their separation once, so that each vehicle subsequently attests only that its own samples stayed inside its own tube. Collision-freedom follows as a consequence (Theorem 2), and the pairwise cost is paid a single time at planning. The commitment layer (Layer 1) is implemented and evaluated as a decision-support audit pipeline that produces non-disclosing, tamper-evident audit artifacts via pre-flight Merkle commitments. It is evaluated in an emulated UAV swarm environment with systematic adversarial injection, in configurations up to 200 vehicles × 500 samples (100,000 sample statements), reporting artifact size, commit/prove/verify/disjunction times, and tamper-detection rates. We then formally identify the security boundary of the implemented layer: it provides coordinate hiding and tamper evidence, but cannot by itself make self-reported containment truthful, which we state as a security game and an impossibility result (Theorem 3). We specify the additional soundness layers (range proof, continuity, provenance and freshness, and aggregation) needed for full containment assurance, proving that composing a knowledge-sound range argument closes the gap (Theorem 4). Throughout, we separate the implemented and measured Layer 1 from the specified and proved—but not yet benchmarked—Layers 2–4, and we make no claim of full zero-knowledge geofence compliance, of swarm-scale deployment, or of deployment readiness.
Victoria Kovalenko, Sergii Sheludko, Elena Sergeeva
In the context of the unprecedented pace of digital transformation and the escalation of geopolitical risks, traditional methods of monetary regulation require a fundamental reconsideration. Problem statement. The evolution of cyber threats – from financial fraud to complex operations involving artificial intelligence – poses significant risks to macroeconomic stability. The development of an integrated protection system based on central bank digital currencies (CBDCs) and SupTech instruments constitutes a critical prerequisite for preserving financial sovereignty, particularly for Ukraine in the context of European integration and martial law. Unresolved aspects of the problem. The theoretical substantiation and development of practical recommendations for integrating advanced digital instruments (CBDC, artificial intelligence, distributed ledger technology (DLT), and SupTech) into monetary and prudential policy mechanisms in order to form a comprehensive cybersecurity framework for the financial sector remain insufficiently addressed. Purpose of the article. The purpose of this article is to provide a theoretical substantiation and to develop practical recommendations for integrating modern digital instruments (such as artificial intelligence, blockchain technologies, and SupTech) into monetary and prudential policy mechanisms in order to establish a comprehensive cybersecurity system for the financial sector. The study is grounded in a systemic approach to analysing the coordination of regulatory policies. The methodology includes comparative legal analysis (comparing the models of the e-hryvnia and the Digital Euro), structural and functional modelling (two-tier CBDC architecture), and scenario analysis to identify cyber risks (including DDoS attacks and smart contract vulnerabilities) and methods for their mitigation. Presentation of the main material. A model of hybrid coordination has been developed, in which cybersecurity is integrated directly into the mechanism of monetary transmission. It has been demonstrated that the programmability of the e-hryvnia and the application of Zero-Knowledge Proofs (ZKP) technologies enable the automation of prudential supervision while preserving user privacy. Global case studies (China, the European Union, and the Bahamas) have been analysed, and the specific features of the Ukrainian e-hryvnia project have been identified as instruments for enhancing transparency and cyber resilience. For the first time, it is proposed to consider a central bank digital currency not only as a means of payment but also as an active element of the cyber-prudential system, enabling the dynamic adjustment of liquidity and limits under conditions of real cyberattacks. The concept of convergence between SupTech and RegTech systems based on unified distributed ledgers has been further developed. The proposed architectural model and cyber-risk matrix may be utilised by the National Bank of Ukraine in the finalisation of the e-hryvnia project and in the development of digital operational resilience standards in accordance with the DORA regulation. Conclusions. It has been demonstrated that digitalisation transforms the regulator into an architect of a secure financial environment. Further research will focus on the interoperability of CBDCs across countries and the role of artificial intelligence in preventing manipulation in digital asset markets.
Open access
Digital Transformation in Financial Services
Legal, Health, Environmental and COVID-19 Challenges
Smart contracts underpin a wide range of decentralized applications—from financial services to supply-chain management—but their immutability and direct control of assets magnify the impact of any security bugs. Although many fuzz approaches have been proposed and have demonstrated their effectiveness in uncovering vulnerabilities, existing methods often rely on unguided random mutation scheduling, generate redundant inputs, and fail to adapt to smart contract-specific characteristics. To overcome these challenges, we present FuzzMaster, a feedback-driven fuzzing framework that combines deep reinforcement learning (DRL) with lightweight probabilistic scheduling to steer mutation selection at runtime intelligently. By continuously analyzing execution feedback—code coverage, function-call sequences, and vulnerability signals—FuzzMaster’s DRL agent and probabilistic tables prioritize high-impact mutations and avoid wasted effort on redundant seeds. On standard VeriSmart and SmartBugs benchmarks, FuzzMaster achieves a 66.2% detection rate with 100% precision (versus 46.9% for ItyFuzz and 43.1% for Confuzzius) and uncovers most bugs within the first second of execution. Meanwhile, in real-world Ethereum contracts, FuzzMaster identified 97 vulnerabilities in 6 categories. These results demonstrate that dynamic, vulnerability-aware mutation scheduling can dramatically improve both the efficiency and effectiveness of smart contract fuzz testing.
The rapid increase in distributed mobile e-learning systems has resulted in numerous security threats, including student data protection, secure access, transparency, and decentralized education management. Traditional cloud-based e-learning systems have been prone to various risks, such as centralization vulnerability, data access violations, identity theft, and lack of scalability in a highly variable wireless learning environment. This paper proposes a blockchain-integrated, privacy-preserving, distributed mobile e-learning architecture for securely and autonomously managing student data. In this framework, blockchain technology will be used for ensuring a decentralized ledger, lightweight cryptography, smart contract-based authentication, and distributed data storage. Blockchain transaction verification, data encryption and sharing, distributed data storage, and smart contract execution are the methodologies utilized by this system to ensure secure academic record and activity management in a mobile environment. The evaluation of the proposed architecture will involve performance measurement of the following parameters: authentication accuracy, privacy protection capability, transaction processing speed, throughput, and data storage efficiency. It was revealed from experimental studies that the suggested approach provided 98.3% in terms of identification, 97.5% in relation to data privacy protection, and 91.8% concerning storage efficiency compared to other methods, including traditional cloud-based learning systems and previous blockchain-based education platforms. In addition, the suggested system enabled reducing the transaction time to 190 ms and increasing the throughput speed up to 465 transactions per second, which proves its high efficiency and capability of functioning in a distributed wireless environment. Therefore, it can be stated that introducing blockchain technology in distributed mobile e-learning systems enhances the level of privacy, resilience against malicious attacks, traceability, and autonomy in controlling personal information. The introduced concept provides a basis for designing a highly reliable and scalable framework for the future generation of wireless educational communities based on the management of decentralized and reliable data.
Health care data management comes with numerous barriers as a result of the use of different systems of record keeping, which are not compatible and increase the risks for data protection and privacy. Medical records are frequently distributed throughout various clinics and hospitals, and due to this it is hard to share information when patients are being treated. Centralized record systems bring unauthorized access to records and the problems related to the safety of data. In order to enhance the level of confidence of people and improve the level of transparency of health care data, advanced people choose decentralized technologies and uses cryptography for these purposes. Blockchain technology offers an unchangeable and decentralized ledger that guarantees safe monitoring of all information despite the presence of any centralized body. Coupled with sophisticated encryption methods, it provides the ability to limit access to private health information. In order to provide secure and respect privacy regarding medical data sharing, an Electronic Health Record (EHR) system powered by blockchain technologies is proposed. Patient record metadata is recorded on-chain while health data itself is stored on encrypted off-chain storage. In the realm of access management, smart contracts facilitate patients in designating by whom their records can be accessed and modified. The privacy of information is further strengthened by advanced cryptographic techniques like attribute-based encryption and zero-knowledge proofs. The system provides seamless interoperability among hospitals, laboratories, and telemedicine systems while ensuring high levels of security. The results of performance evaluation demonstrate that this method facilitates reliable transaction processing while providing better security, transparency and control than traditional centralized EHR systems.
This technical report provides an empirical evaluation of Bitcoin Layer-2 execution environments (BOB, Bitlayer, Citrea, Stacks, and Rootstock) against a six-layer architectural framework designed for institutional-grade decentralized finance (DeFi). Using Tage_Root — a purpose-built reference implementation operationalizing Bitcoin-native execution (L1) and trust-minimized bridging (L2) — the analysis assesses conditions required for credible BTC-denominated yield markets, including intent-based routing, autonomous capital allocation, zero-knowledge compliance, and accountable governance. The findings reveal that while several systems have substantially solved the bridge problem at the technical level, critical higher-layer infrastructure remains absent or weak. This architectural gap explains the persistent idleness of bridged BTC and the low capital efficiency observed in BTCFi protocols, despite significant growth in bridging capacity. The report offers a code-grounded diagnostic benchmark and lays the empirical foundation for forthcoming theoretical work on the “Bridge Problem” and the pricing of Bitcoin-denominated yield. It argues that trust-minimized execution alone is insufficient for institutional capital formation in Bitcoin DeFi.
Information and Communication Technologies such as blockchain can significantly contribute to achieving the Sustainable Development Goals (SDGs). Without a doubt, blockchain, as one of the most valuable technological advancements, has been introduced over the past decade and has played a significant role in the industrial revolution. Blockchain technology is progressively taking over the business world. Blockchain as a disruptive technology and a driver for social change has exhibited great potential to promote sustainable practices and help organizations and governments achieve the United Nations’ Sustainable Development Goals (SDGs). The emergence of other technologies derived from blockchain, such as decentralized finance (DeFi) and the Metaverse, has fundamentally transformed people’s daily lives and profoundly impacted future versions of digital businesses. The Blockchain technology revamped several industries, including Real Estate, Healthcare, Education, and Legal industry to name a few. It opened new doors of opportunities and profit for the entrepreneurs and established brands. The paper's main contribution is to advance knowledge about the role of blockchain for economic and sustainable development in countries of the world. Grounded in the innovation forecasting literature, this paper explores blockchain-based innovations and research in the context of economic and sustainable development.
Smart contracts have achieved significant success, however, their security remains a long-standing challenge. The immutability and transparency of smart contracts require establishing a strong mechanism to prevent private leakage and trusted data tampering. Apart from traditional logic and code-level vulnerabilities arising from insufficient control over contract variables and function parameters, smart contracts may store private-dependent information in blockchain records, which is a critical type of vulnerability, but often overlooked in existing security analysis. In this paper, we present an automated approach for synthesizing security policies, named SmartIFSyn, to eliminate information flow vulnerabilities in smart contracts. We formalize the semantics of Solidity, the most widely used smart contract language, and analyze information flow security of Solidity smart contracts from two perspectives: local-variable security and global-interaction security. We present a type system to guide the elimination of local-variable vulnerabilities by inferring a policy and resort to constraint solving to synthesize a desired policy in case that the type system fails. The policy ensures both local-variable and global-interaction security while it is maximally aligned with user preference. Furthermore, the policy can be subsequently converted into enforceable specifications. We implement our approach in a tool and evaluate it on 17,160 real-world Ethereum smart contracts. The experimental results demonstrate the efficacy of our approach, e.g., detected 243 vulnerabilities in 223 real-world Ethereum smart contracts.
For over half a century, the core paradigm of query optimization has been defined by a monotonic, scalar minimization convergence model aimed at suppressing computational resource consumption. This paper presents a radical paradigm shift that fundamentally subverts this traditional framework by establishing the Axiomatic Topological Inverse Query and Complexity Maximization Theory (ATIQ-CMT). Instead of pursuing local or global minima within discrete equivalence graphs, we reconstruct the relational algebra space into a non-Hausdorff, locally compact topological space governed by five foundational axioms. By introducing the Inverse Lipschitz Affine Expansion Mapping (ILAEM) under operator braid transformations, we demonstrate how compact query plans can be inversely dilated into divergent flows across high-dimensional complex affine varieties, creating irreversible mathematical obstructions for traditional gradient-based cost models. To maximize computational complexity natively, we execute a non-commutative extension of the relational algebra core via algebraically twisted join operators embedded in infinite-dimensional Lie algebras, effectively destroying the classic commutative-associative symmetry. We further inject un-decidable Diophantine predicates and 3-SAT arithmetical homomorphic graphs as computational obstructions, rigorously proving a non-polynomial exponential lower bound for physical query execution times. Utilizing sheaf theory and de Rham cohomology on chain complexes, we provide a definitive topological proof that the absolute semantic integrity of the query remains invariant throughout this chaotic dilation. Finally, we formulate a deterministic chaotic operator execution flow driven by high-order Lorenz mappings, maximizing the algebraic Shannon entropy of intermediate states. ATIQ-CMT bridges declarative relational logic and high-level structural topology, unlocking revolutionary potentials in zero-knowledge proof circuit synthesis, active cybersecurity defense, and the theoretical computational limits of neuro-symbolic and quantum systems.