Harlequin is a blockchain protocol in which the right to take part in consensus,governance and adjudication comes solely from reputation earned by verifiable acts— never from capital (proof of stake) or expended computation (proof of work).Reputation is a four-dimensional quantity ("the four suits"), computeddeterministically from a public evidence record by a damped trust-propagationfunction, aggregated conservatively (a strong dimension cannot buy authority in aweak one), and subject to time decay so that standing must be continuallyre-earned. Block authorship and committee/jury membership are assigned byreputation-weighted cryptographic sortition; finality is provided by aByzantine-safe gadget over signed votes; disputes are judged by sortitioned jurieswith interest-exclusion, and the only enforced consequence is reputational — theprotocol applies no coercive force. We give the system model, the consensus and justice mechanisms, and a securityanalysis against a state-level adversary whose goal is capture, censorship orde-anonymization rather than direct theft. Two results are emphasized for theirhonesty. First, steady-state Sybil resistance is strong: a Sybil farm withoutearned evidence obtains about 0% of consensus power (17/17 adversarial tests).Second, the cold-start window is not unconditionally safe: a competent adversarypresent at genesis can capture the bootstrap; we show the security of that windowis a race between honest onboarding and adversary mass — bounded, not eliminated,by non-operator personhood verification, an automatic ceiling-halt and theonboarding rate, with the residual risk declared. We report an implementation inRust (dependency-free cores cross-validated against FRAME pallets) and areproducible validation record spanning unit tests and multi-node hardware runs.
Ms. Sanskruti Pawaskar, Mr. Harsh Shinde, Mr. Ruturaj Laad, Vaishali Gatty
Decentralized finance has disrupted the lending process by transferring the intermediary role from institutionally-led balance sheets into a public ledger framework of smart contracts, pooled liquidity, and tokenized governance. The relevance of such a change in the lending paradigm is more of a question of different trust mechanisms, where the solvency of actors can be maintained through the imposition of collateral and automated processing [1][3]. A qualitative comparison is made below through a literature-constrained synthesis of five sources on DeFi architecture, flash loan exploits, lending protocol structure, decentralized governance flaws, and extractable value [1]-[5]. The two protocols of Aave and Compound have been selected for being representative DeFi lending cases, as per the allowed literature that points them out to be the top loanable funds protocols, having liquidity pools and variable rates [1][3]. This comparison is made against CeFi as an institution-driven reference point rather than other DeFi lending protocols owing to the asymmetry of the evidence base. Three conclusions are drawn.Second, the risk architecture of DeFi lending is structurally different from other financial institutions in that flash loans, dependence on oracle feeds, smart contract weakness, composable nature, extractable value, and governance capture are not mere flaws in DeFi but inherent aspects of open and highly coupled financial systems [2][4][5]. Third, governance in DeFi is an additional security mechanism, as the governance of protocol control, parameters and responses to emergencies rests on the robustness of token-based decision-making mechanisms [4].
Digital identity remains structurally rigid when it is bound to provider accounts, mutable handles, single social contexts, and local wallet schemes. This paper defines an accountable hash-anchor tier above wallets and credential schemes. Building on a companion model of legal identity assurance, the tier binds an inert root anchor to an event-backed Entity Actor Identity (EAID) assurance state, supports unlinkable profile anchors for distinct contexts, and lets relying parties evaluate gate-specific assurance-at-time over disclosed independent confirmation-event clusters. The formal model states the identifier and capability separation invariant, defines root and profile anchors, models disclosure and lawful resolution as constraints, and gives a two-layer erasure construction for retained commitments and off-chain personal linkage. It also models trust as a reliance event in which pseudonymous interaction becomes rational when a presentation satisfies the requested gate and recourse can reach the legally relevant imputation point. The result is a scheme-agnostic aggregation layer that complements national wallets, supports natural, juridical, and machine actor constellations, and states conditions under which pseudonymity, lawful access, data erasure, and retention can be jointly satisfied.
Permissioned ledgers are commonly treated as centralised because admission is restricted. This paper separates permissioning from control distribution and proposes identity-staked consensus as a trust model for accountable settlement ledgers operated by chartered validators. The model treats validator identity as externally costly collateral: public legal identity, charter state, institutional reputation, liability, attributable audit exposure, a phase-indexed conditional identity-loss floor, and loss-realisation channels outside the protocol. It distinguishes this construct from proof-of-authority by formalising validator acts as actor constellations, public validator anchoring, affiliation-aware voting caps, threshold class coverage, per-member collusion margins, observer-supported detectability, bootstrap claim discipline, and a consensus/application enforcement boundary. The paper connects the model to a broader identity-infrastructure series: the actor-assurance paper supplies capability-gate evidence, the trust-anchor paper supplies public validator anchoring and assurance-at-time, and the delegated-authority paper can consume the ledger evidence record for mandate and model-attribution records.
Distributed certification is a set of mechanisms that allows an all-knowing prover to convince the units of a communication network that the network's state has a desired property, such as being 3-colorable or free of a predefined subgraph. Classical mechanisms, such as proof labeling schemes (PLS), consist of a message from the prover to each unit, followed by one round of communication among neighbors. Later works consider extensions, called distributed interactive proofs, where the prover and the units can have multiple rounds of communication before the communication among the units. Recently, Bick, Kol, and Oshman (SODA '22) defined a zero-knowledge version of distributed interactive proofs, where the prover convinces the units that the network satisfies the property without revealing any additional information about the network's state or structure.
We study distributed zero-knowledge proofs, introduced by Bick, Kol, and Oshman (SODA 2022). While distributed interactive proofs have advanced rapidly in recent years, general-purpose techniques for distributed zero-knowledge remain scarce and mostly problem-specific. We address this gap by introducing distributed statistical zero-knowledge, requiring that each node's view be simulatable up to negligible statistical distance, and by lifting the robust Sumcheck protocol (Lund, Fortnow, Karloff, and Nisan; FOCS 1990) into a modular primitive for distributed zero-knowledge proofs.
Web3Compass is presented as a novel search engine tailored to the decentralized Web, integrating multiple blockchain-based name services (ENS, UNS, BNB NS) and content storage networks (IPFS, Arweave, Swarm). Our work describes a real-time monitoring architecture: blockchain registries are queried continuously for new domain registrations and updates, content hashes (e.g. IPFS CIDs) are retrieved and fetched, and website data is parsed and indexed for keyword search. We emphasize the system’s novelty in unifying diverse name systems and content networks under one private search interface. A comprehensive literature review covers previous decentralized search efforts (e.g. DEWS 1, DeScan 2, Krypton 3), blockchain naming services (Namecoin 4, ENS 5, Unstoppable 6, Space ID’s .bnb 7), and content- addressed storage (IPFS 8, Arweave 9, Swarm 10). We include an architecture diagram and discuss implementation details (event log watchers, IPFS HTTP retrieval, indexing pipeline, privacy layers). Evaluation uses scalability and latency metrics, compares with existing solutions, and includes ethical/privacy analysis (e.g. query privacy via Hexens 11, censorship resistance 4). Our results show that real-time blockchain- based domain resolution is feasible and complements Web3 infrastructure, while highlighting trade-offs in data completeness and user privacy.
Web3 represents the next-generation value-driven Internet built on blockchain technology, whose realization heavily relies on mobile devices. However, the limited resources of these devices significantly restrict their ability to participate in transaction verification and ledger maintenance in blockchain networks. Existing offloading schemes often overlook storage offloading or adopt oversimplified joint strategies, failing to adequately consider the synergistic effects of storage and computation offloading on network performance. To address this issue, this paper proposes MEChain, a Mobile Edge Computing (MEC)-aided blockchain network that implements a two-layer joint computation-storage offloading mechanism involving edge service providers (ESPs) and cloud service providers (CSPs). The joint computation offloading, ledger storage, and resource pricing problem is formulated as a three-stage Stackelberg game to capture the complexity of multi-party interactions. An iterative algorithm based on backward induction is designed to efficiently solve the Nash equilibrium, thereby ensuring system stability. Theoretical analysis and numerical experiments demonstrate that the MEChain framework not only significantly improves the profit per unit time of mobile devices by 11.3% but also exhibits rapid convergence of the proposed algorithm, providing a practical and theoretical foundation for resource optimization in mobile blockchain systems.
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.
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
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