G. N. Girish, Ashutosh Sahoo, Ajay Bhat, Akshay SP · 7 authors
Incentive programs are central to user acquisition in decentralized finance, but many reward systems rely on raw volume, transaction count, and wallet count, making them vulnerable to bots and sybil operations. We present ZAPs, a reward attribution framework that combines economic contribution scoring with adversarial robustness. A composite activity score uses protocol-specific percentile normalization to limit whale dominance while preserving differentiation among users. A two-layer weighting mechanism combines protocol share within sector and sector share within the ecosystem, which reduces the profitability of farming small protocols. We show that the maximum reward obtainable from any protocol is bounded by that protocol's global volume share. ZAPs also introduces a four-layer defense stack consisting of transaction-level integrity checks, a parallel anomaly ensemble, post-distribution behavioral memory, and graph-based sybil clustering. The anomaly ensemble combines a one-class reconstruction model with an isolation forest and applies graduated rather than binary penalties. On 1,073 labeled malicious wallets covering 124,638 transactions, the ensemble achieves 0.923 +/- 0.013 ROC-AUC, compared with 0.891 +/- 0.016 for the reconstruction model alone, when the isolation forest is trained on benign wallets. Training it on the pooled population reverses its polarity and removes the ensemble gain. Controlled simulations reduce adversarial reward capture by 30-90 percent while legitimate-user scenarios change by 1-8 percent. Live campaigns recorded a 56 percent reduction in sybil allocation, a 49 percent increase in quality-wallet participation, and a 50 percent reduction in sell pressure.
Blockchain is becoming an approachable data platform with several stakeholders having a shared history of transactions without being owned by an individual. The fundamental concepts of cryptographic hashing, peer-to-peer communication, and agreement protocols are sufficiently documented, and a lot of current research focuses on performance, security, privacy, and governance individually rather than as interacting dimensions. Recent blockchain research publications and deployments were structured based on a four-axis perspective that follows the dynamics of a system in terms of security, scalability, privacy, and governance. This lens was applied to consent mechanisms including proof-of-work, proof-of-stake, and practical Byzantine fault tolerance, and to techniques such as sharding and layer-2 that are designed to enhance throughput. Basic throughput calculations using reported block sizes, transaction sizes, and block intervals indicate that configuration limits are usually much larger than the transaction rates achieved in practice, implying that protocol overheads and network behaviour require a major share of the budget. A survey of attacks and defences indicates that increases in speed and programmability often expand the attack surface at the consensus and smart contract layers, which motivates the development of better analysis and monitoring tools. The results are applied to draw design insights for domains of finance, supply chains, healthcare, identity, and smart city platforms, and to highlight remaining problems in benchmarking and cross-chain coordination. A practical mapping of major blockchain platforms, namely Bitcoin, Ethereum, and Hyperledger Fabric, onto the four-axis framework was also provided to demonstrate its utility for platform comparison and selection.
Counterfeit medicines, fragmented record management, limited end-to-end visibility, and dependence on centralized databases create security and traceability challenges in pharmaceutical supply chains. This article presents Pharma-Chain, a blockchain-enabled pharmaceutical supply chain management system developed to record medicine registration, stakeholder interactions, ownership transfer, and authenticity verification on an Ethereum blockchain. The system integrates a React.js frontend with Web3.js, MetaMask wallet authentication, Solidity smart contracts, Truffle deployment tools, and Ganache for local blockchain development and validation. Manufacturers register medicine batches with product and lifecycle information; distributors and retailers verify blockchain records before accepting and transferring ownership; and customers retrieve medicine details and transaction history before purchase. The implementation was tested module by module and as an integrated application in a local Ethereum environment. Testing confirmed successful authentication and role-based access, medicine registration, ownership transfer, transaction validation, blockchain record retrieval, and medicine verification. Each successful supply-chain operation generated a blockchain transaction record, providing an immutable ownership history and improving transparency and auditability. The project demonstrates a practical decentralized approach for secure pharmaceutical tracking while identifying QR-code verification, IoT monitoring, AI-assisted analytics, mobile access, and deployment on public or enterprise blockchain networks as future extensions.
Open access
2 source records
Blockchain Technology Applications and Security
Pharmaceutical Quality and Counterfeiting
Physical Unclonable Functions (PUFs) and Hardware Security
Sybil bots are Ethereum actors that imitate legitimate users to extract airdrop rewards or influence governance. Recent Sybil detection methods increasingly use deep learning and treat blockchain activity as a quasi-linguistic sequence. However, complex sequence models are computationally expensive for real-time monitoring, and their reported performance may be inflated by label leakage from high-signal smart contracts. We ask whether and how organic users, Sybil bots, and MEV bots differ in the structural complexity of their transaction histories; whether sequential models outperform tree-based tabular models once leakage is reduced; whether transaction order or timing provides the stronger behavioral signal; and whether the resulting models are practical for low-latency deployment. Our approach to leakage-aware Sybil bot detection consists of a Blind-Spot protocol and a Transaction Grammar representation of wallet behavior. The former eliminates shortcuts associated with high-signal contracts, whereas the latter models wallets using rhythm, EVM execution structure, and intent. We evaluate this approach on Ethereum actor classification by comparing Transformer and BiLSTM sequence models against XGBoost and SVM baselines. We contribute a framework for leakage-aware Ethereum actor classification and a Transaction Grammar representation of wallet behavior. Our results demonstrate that, under leakage-aware evaluation, XGBoost outperforms Transformer-based sequence models while providing lower latency and estimated energy use.
Context: AI-assisted software development can speed up coding and review, but it also makes accountability harder to establish. Developers may submit insufficiently verified code, reviewers may approve changes with limited inspection, and centralized reputation records may be difficult to audit. Objectives: This study introduces TrustChain-Review, a framework that combines verifiable evidence, strategic incentives, and risk-sensitive governance to support more trustworthy code review. Methods: The framework includes a blockchain-based evidence layer, a three-player game-theoretic model for developers, reviewers, and the platform, and a rule that applies stronger governance when the expected benefit justifies its cost. The evaluation uses a controlled simulation calibrated with the Diff Quality Estimation dataset. Six governance configurations are compared over 30 independent runs using reputation accuracy, trust convergence, malicious-review detection, superficial-review detection, net platform utility, governance cost, and cost-efficiency. Results: The full-evidence configuration produces the strongest reputation, trust, and detection results, but it also has the highest governance cost. The risk-adaptive configuration lowers this cost and improves cost-efficiency by applying stronger controls selectively, although its trust and detection results are lower than those of the full-evidence setting. Conclusion: Strong evidence-based governance is most appropriate for high-risk or audit-sensitive changes. For routine or lower-risk contributions, selective governance offers a more practical balance between trustworthiness and operational cost.
Sui's object-centric data model enables parallel transaction execution, but realised performance is fundamentally bounded by workload contention. Prior empirical analyses of Sui have relied on "read+write" (R+W) conflict graphs inherited from account-based blockchains. Because Sui's engine serialises only on mutable shared access, R+W graphs contain spurious edges, bounding contention from above. In this paper, we adopt a complementary "write-set-only" (W-only) model in which every edge represents a real write-serialisation event, providing a lower bound on contention. Together, the two models bracket Sui's true execution-dependency structure. Applying the W-only analysis to Sui mainnet data through 2025 yields three primary findings. First, removing read-only dependencies (notably the system clock) causes previously reported "hub-and-spoke" structures to collapse. The remaining contention topology is highly assortative and clique-dominated, with the W-only bound shaving roughly $30$--$40\%$ off the R+W estimate of Sui's optimal-parallelism headroom. Second, via union-find object grouping, we isolate DeepBook (Sui's native central limit order book). While it dominates contention by volume, its underlying logic does not impose disproportionate sequential bottlenecks. Finally, we quantify the economic cost of contention, showing that $10$--$50\%$ of the network's USD-denominated value flows through sequentially constrained execution paths, exposing it to potential ordering effects.
Uncertainty plays a significant role in shaping investment decisions, both directly and indirectly. In an uncertain economic environment, investors’ motivations for decision-making may vary. While some investors tend to seek safe-haven assets, others may engage in speculative behavior. Therefore, uncertainty can influence financial instruments through various mechanisms. One of these instruments is Bitcoin, which is often regarded as the “gold” of cryptocurrencies. Compared to traditional financial investment instruments, Bitcoin exhibits higher volatility and is among the primary assets that may be affected by uncertainty. However, an important question is whether this effect is temporary or permanent. The main objective of this study is to address this question by examining the causal nexus between Global Economic Policy Uncertainty (GEPU) and Bitcoin by employing a frequency-domain causality approach. In this context, the causal relationships between GEPU and BTC prices are examined for the entire period and for different sub-periods. Although the study's findings show no causal relationship between the variables over the entire period, the analyses for the short-, medium-, and long-run indicate a causal relationship from GEPU to BTC in the medium run. Accordingly, GEPU can be considered one of the factors affecting BTC price; however, its impact does not appear to be persistent.
Smart contract vulnerability detection requires evaluation protocols that separate real representation signal from dataset-specific artifacts. DIVE provides lifecycle-based tabular features for Ethereum smart contracts, but benchmark performance alone cannot show whether a dominant feature group is useful or only benefits from having many columns. This study examines Opcode Distribution features using 22,330 contracts, 397 processed features, and eight DASP-aligned vulnerability labels. Five multi-label learning configurations were evaluated under 3 x 5 repeated cross-validation, followed by global feature-group ablation, size-controlled random opcode ablation, per-label degradation analysis, cumulative stability analysis, and opcode-profile group-aware robustness checking. MultiOutput LightGBM achieved the best baseline performance, with Micro-F1 of 0.91396, Macro-F1 of 0.82464, and Macro-PR-AUC of 0.90146. Removing the full Opcode Distribution group reduced Macro-F1 to 0.78745, while removing a same-sized random opcode subset produced Macro-F1 of 0.82404. The findings indicate that Opcode Distribution acts as a collective predictive representation rather than a feature-count artifact, without implying causal vulnerability mechanisms.
This project introduces "X-142's Blockchain," a novel multi-layer architecture designed to resolve key bottlenecks in existing distributed ledger technologies. Traditional Order-Execute (OE) and Execute-Order-Verify (EOV) transaction models introduce severe sequential processing delays under peak loads, while standard Proof-of-Authority (PoA) consensus mechanisms remain vulnerable to malicious validator collusion. To address these limitations, our methodology implements a dual-layer network infrastructure consisting of a Main Layer and a Subordinate Layer governed by a "Guardian" node tracking reputation metrics to neutralize routing attacks. Furthermore, the framework integrates cryptographic trust via Zero-Knowledge Proofs (ZKP), utilizing an optimized Nova IVC-PloNK framework to guarantee fixed-size proof validation regardless of off-chain network states.Experimental benchmarks demonstrate that the proposed architecture achieves linear throughput degradation with increasing validator counts while maintaining strong Byzantine Fault Tolerance (BFT) consensus properties. The multi-layer anchoring system successfully enforces data sovereignty and real-time network visibility without penalizing nominal network throughput. Future development will focus on optimizing the SN-PoA PPO selectors using real operational traces, replacing current consensus components with quantum-resistant cross-layer proofs, and validating the framework across heterogeneous, multi-organizational Wide Area Network (WAN) deployments.
With the advent of digital services, various vulnerabilities in centralized identity management systems such as Single Point of Failure (SPOF), data leakage, and user privacy invasion are evident. In this paper, an architecture framework for designing Decentralized Identity Management System (DIDMS) by leveraging blockchain technologies is proposed for privacy preserving authentication in digital age. The DIDMS framework makes use of Decentralized Identifier (DID) with Verifiable Credentials (VC) and Zero Knowledge Proof (ZKP) techniques to implement the concept of self-sovereign identity. Four phases of framework including registration, credential issuance, authentication and storage is designed and tested. The performance results obtained from the experimentation indicate that the proposed framework gives an authentication latency of 320 ms, storage overhead reduction of 42%, and verification accuracy of 98.6% with 31% gas optimization over conventional smart contract approaches. The comparative evaluation with existing frameworks indicates improvement in the areas of privacy protection, scalability and user control.
Scaling Up the Internet of Things (IoT) Safely Using Smart Cryptography The Big Picture Problem: The Traffic Jam of Smart Devices Imagine a world where your smart fridge, your fitness watch, your car, and the security cameras at your local hospital all need to talk to each other securely. To trust each other, they use a Blockchain—a digital, un-hackable ledger that keeps track of every device's true identity. Here is the catch: traditional blockchains are notoriously slow. If thousands of smart devices try to log in, update their status, or check their permissions at the exact same second, the system gets clogged. It creates a massive digital traffic jam. The Proposed Solution: "The Digital Carpool" (ZK-Rollups) This research introduces a framework that fixes this traffic jam using two concepts: Rollups and Zero-Knowledge Proofs. What is a Rollup? Instead of every single IoT device sending its identity data directly to the main blockchain one by one, a Rollup groups thousands of these transactions together off the main chain, bundles them into a single neat package, and sends just that one package back to the main blockchain. It’s like forcing 50 individual drivers to get into a single bus—suddenly, the highway clears up. What is Zero-Knowledge (ZK)? When you bundle all those devices together, how does the main blockchain know nobody cheated or snuck a fake device into the bundle? Usually, the blockchain would have to unpack the bundle and check everything, which defeats the purpose of saving time. A Zero-Knowledge Proof is a mathematical certificate attached to the bundle. It proves mathematically that every single transaction inside the bundle is valid, without actually revealing the private data of the devices inside. How the Framework Works (Step-by-Step) Device Action: Your smart smartwatch or factory sensor wants to verify its identity. Off-Chain Bundling: Instead of bothering the main blockchain, the device sends its request to a side-processor (the Rollup). The Rollup collects thousands of these requests. Generating the Proof: The system creates a ZK-Proof—a cryptographic receipt that says: "We checked all 1,000 devices, they are all authentic, and here is the math to prove it." Final Verification: The main blockchain receives just the receipt. Because the math is undeniable, the blockchain approves all 1,000 devices instantly in a fraction of a second. Why This Matters Massive Speed (High Throughput): Instead of handling maybe 15 device checks per second, the system can now handle thousands per second. The traffic jam is gone. Bank-Grade Security: Because it relies on advanced mathematics (Zero-Knowledge), hackers cannot forge a device identity or trick the system, even though the heavy lifting is done off the main blockchain. Low Cost: Smart devices usually have weak batteries and low computing power. By moving the heavy math away from the devices and onto the Rollup system, the devices save energy and operational costs. Conclusion So, we don't have to choose between speed and security. By bundling IoT data and verifying it with modern mathematical shortcuts, we can build a future where billions of smart devices connect instantly, safely, and without crashing the system.
This study investigates two questions relating to cryptocurrency market dynamics. First, whether a composite skew measure derived from MicroStrategy (MSTR) trading activity can predict future Bitcoin (BTC) and Ethereum (ETH) volatility. Second, whether Ethereum volatility exhibits reproducible structural properties consistent with established theories of volatility persistence and cascading shock dynamics. Using rolling out-of-sample testing, autocorrelation-adjusted significance testing, regime classification, shock-decay modelling, return-interval analysis, and earthquake-inspired cascade frameworks, the study finds no evidence that MSTR composite skew provides a useful forecasting signal. More broadly, no forecasting model tested outperforms naive benchmark models beyond horizons of approximately three to five days. However, several descriptive properties of Ethereum volatility appear robust, including volatility persistence, regime structure, extreme-event clustering, non-simple shock decay, and partially transferable aftershock dynamics. In particular, while Omori-style decay and the productivity law are supported, Bath's Law fails consistently, suggesting cryptocurrency volatility cascades may differ fundamentally from those observed in traditional financial markets. The findings contribute to the understanding of volatility organisation in digital asset markets while highlighting the difficulty of extracting persistent predictive signals from historical OHLCV
Sybil attackers are Blockchain actors that adopt the characteristics of regular users to exploit airdrops or influence governance. Current methods of Sybil actor detection include constructing graphs, which requires token transfers between examined wallets. Machine learning algorithms have been employed as well, but they treat the task as a closed-set classification problem, making them vulnerable to frequent changes in attack strategies or evasion tactics. We address the following questions: can compression-based similarity differentiate Sybil bots, organic users, and arbitrage bot wallets without direct financial links? What is the effect of high-signal contracts on the discovery of Sybils, and how robust are behavioral graphs under temporal drift and adversarial perturbations? Our approach synthesizes a symbolic Transaction Grammar from EVM (Ethereum Virtual Machine) traces, capturing separately transaction rhythm, execution structure, and functional intent. The high-signal contracts are filtered with our own protocol, called the Blind-Spot Protocol. Gzip-based NCD is used to construct a behavioral graph for Sybil discovery. We validate this framework against supervised machine learning baselines, a temporal split, and synthetic camouflage stress tests. Ultimately, we contribute a leakage-aware behavioral framework for Sybil candidate discovery. Its core NCD primitive requires no supervised training and can expand suspicious seed wallets without explicit funding links. We position the method as a training-free local discovery primitive for open-world blockchain audits, rather than as a formal open-set recognition system.
Q-PROOF is an experimental blockchain architecture based on adaptive consensus, aperiodic topology, and quadratic governance. The model integrates Aperiodic Consensus Relaxation (ACR), distributed reputation, Sybil attack defense, coordinated attack detection, and post-quantum migration pathways. This technical white paper outlines the core consensus engine, mathematical modeling of system tension, correlation-aware consensus mechanics, and benchmark comparisons demonstrating enhanced finality and resilience against coordinated network capture.
Prajakta Khule, K. Kumaraswamy, Puja Bhardwaj, Meghana Bhilare
Abstract The increasing complexity of global financial systems has exposed the limitations of conventional centralized banking infrastructures in managing transparency, operational efficiency, security, and real-time transaction processing. Distributed Ledger Technology (DLT), particularly blockchain, has emerged as a transformative digital architecture capable of addressing these structural challenges through decentralized record management, cryptographic security, and automated transaction validation. This study examines the architectural foundations and strategic viability of blockchain-enabled distributed ledger technologies within modern banking and corporate finance. Using a qualitative research approach based on an extensive review of recent scholarly literature, industry reports, and practical financial applications, the study evaluates how different blockchain architectures contribute to organizational transformation. Three representative case studies—consortium corporate lending and syndicate management, cross-border settlement systems, and decentralized Know Your Customer (KYC) identity management—are analyzed to demonstrate the practical implications of enterprise blockchain adoption. The findings indicate that permissioned and consortium blockchain architectures significantly enhance operational transparency, reduce intermediary dependence, improve data integrity, automate compliance through smart contracts, and accelerate financial transactions while strengthening governance and auditability. However, the study also identifies challenges associated with regulatory uncertainty, interoperability with legacy systems, scalability, and institutional readiness that continue to influence large-scale implementation. The research contributes to the growing body of knowledge by integrating architectural analysis with strategic business evaluation and proposes a comprehensive perspective on the role of distributed ledger technologies in reshaping banking operations and corporate financial management. The findings provide useful insights for researchers, financial institutions, technology professionals, and policymakers seeking to develop secure, efficient, and sustainable digital financial ecosystems. Keywords: Distributed Ledger Technology (DLT), Blockchain, Smart Contracts, Consortium Lending, Cryptographic Auditing, Cross-Border Clearance, Financial Disintermediation, Asymmetric Cryptography, Banking and Finance.
Relating low-level executable code to a high-level account of its behavior has been a central concern of programming-language research for decades. From formally verified compilers to translation validators, certifying compilers, and proof-carrying code, each approach chooses between laborious but foundational mechanized proofs and automation that costs completeness, generality, and an increased trusted base. Recently, large language models (LLMs) have begun to change the economics of formal verification. Agentic proof development is now capable of producing machine-checked proofs at a scale and speed that were previously out of reach. In this paper, we evaluate the capabilities of LLMs to produce foundational, machine-checked proofs of refinement between executable code and its high-level specification, as post hoc, per-artifact certificates. We study this in the context of the Ethereum Virtual Machine (EVM), a low-level virtual machine that executes smart contracts on the Ethereum blockchain. We build EquiVM, a foundational framework in Lean comprising an executable EVM semantics and a specification language that characterizes the intended behavior of smart contracts, but commits to no source language or compilation toolchain. In EquiVM, refinement is stated for deployed bytecode of arbitrary provenance, interaction with unknown code is part of the semantics, and each proof is a replayable, machine-checked certificate. No previous technique achieves this combination. Using frontier commercial LLMs, twenty-three real-world contracts are proved end to end with minimal human guidance, among them most of the MakerDAO stablecoin system, at up to a hundred million tokens and a hundred hours of proof time per contract. We conclude that foundational mechanized proofs can now be bought at the price of tokens, and that this shift can reshape how verification frameworks are architected.
Requirement-to-Code traceability has been widely studied, yet existing research and public benchmarks remain largely centered on functional requirements (FRs). In contrast, traceability for non-functional requirements (NFRs) remains more difficult and underexplored, which hinders the verification of critical quality concerns such as security and reliability.This paper studies NFR-to-Code traceability based on a real-world blockchain-IoT project. We design an annotation protocol for constructing trace links across heterogeneous artifacts and build a manually curated subset containing both FR and NFR links. Using this subset, we examine the characteristics of NFR traceability and further evaluate four representative retrieval baselines: TF-IDF, BM25, LSI, and WMD. The results show that FR-to-Code tracing is consistently easier than NFR-to-Code tracing, while security-related NFRs are the most difficult subset. They further indicate that the main challenge of NFR traceability lies not in requirement availability, but in implementation evidence that is distributed and not clearly localized in code.
Tim K. Mackey, Alec J. Calac, Tiana McMann, Ken Miyachi · 10 authors
Background: Historic and ongoing problematic practices regarding the collection, storage, and use of Indigenous health data have led to the need to ensure principles of Indigenous Data Sovereignty (IDS) are followed in research practices and technology development. Objective: This project, a partnership between UC San Diego and the Native BioData Consortium (NativeBio), sought to explore the practical application of blockchain technology and its potential to facilitate Indigenous-led research collaboration. Methods: This project first undertook purposeful relationship building with NativeBio to form a Community Advisory Board (CAB) for identifying community and technology needs for a blockchain research collaboration platform with an initial focus on genomic data. Over a 2-year project period, a series of public meetings and presentations at Indigenous-led conferences introduced the concept of exploring compatibility between blockchain and IDS principles, followed by iterative prototyping and co-design of a blockchain platform with NativeBio, using Ethereum as the underlying protocol. Results: Direct engagement with NativeBio and the CAB informed the initial design and development of a "b-IDS" proof-of-concept (POC) blockchain platform. The POC consists of three main components: (1) the web front-end layer, (2) the Ethereum network that executes the smart contract and blockchain storage aspects of the framework, and (3) the back-end database that stores off-chain interactions and data for future use with external genomic data repositories. After refinement of the POC, a community-based participatory research (CBPR) use case aligned with IDS principles was identified as a practical workflow and incorporated into the design of the POC for implementation. Conclusions: The findings from this project demonstrated the potential use of operationalizing IDS through blockchain technology with proactive and sustained engagement with Indigenous partners. Blockchain technology may have certain advantages over other data governance approaches and systems, facilitating timely oversight, shared decision-making and consent structures, and direct involvement of Indigenous communities in technology design, respecting the core principles of IDS and CBPR. Future development of the blockchain-IDS POC will need to incorporate other research practices and ethics frameworks to expand its use to other public health and biomedical research use cases.
Against the backdrop of rapid technological innovation and the growing use of alternative investment instruments, this study examines the dynamic connectedness among decentralized finance assets, AI-based stocks, Islamic stocks and commodities. Covering the period from December 2019 to June 2022, we use the time-varying parameter vector autoregression (TVP-VAR) model to measure the magnitude, direction and evolution of return spillovers across Chainlink, Maker, Basic Attention Token, NVIDIA, Amazon, Google, Microsoft, DJIM World, DJIM EM, gold, crude oil and Global X Lithium and Battery Tech. The connectedness literature has examined spillovers across different asset classes during crisis periods. However, much of this literature focuses mainly on pairwise relationships among traditional asset classes, with limited attention to how emerging, alternative and technology-driven assets interact within a single network. We further assess the role of investor sentiment and network topology in identifying systemic transmitters and receivers. The results show strong interconnectedness, with an average total connectedness index (TCI) of 68.81%. Notably, AI-based stocks, especially Microsoft and NVIDIA, consistently emerge as net transmitters of return shocks, while commodities like gold and crude oil serve as absorbers of shocks. The portfolio results show that network centrality improves risk-adjusted performance by reducing volatility and downside risk. These insights have practical implications for policymakers and market participants, offering guidance for developing effective regulatory frameworks, investment strategies and risk management approaches in an increasingly interconnected financial landscape.
Introduction: Blockchain technology has emerged as a transformative innovation in the financial sector by enhancing transparency, security, and operational efficiency. As academic interest in blockchain applications continues to grow, understanding the development, structure, and direction of research in this field has become increasingly important. Novelty: Although previous studies have examined blockchain broadly, limited research has specifically mapped the intellectual structure, thematic evolution, and collaboration patterns of blockchain research in finance, particularly regarding transparency and security. To address this gap, the present study provides a comprehensive bibliometric mapping of blockchain research in finance from the perspectives of transparency and security. Methods: This study applies a bibliometric analysis approach to 256 Scopus-indexed publications related to blockchain in finance. Data were analyzed using the Bibliometrix package in R to examine annual scientific production, keyword co-occurrence, thematic development, historiographic structure, and collaboration networks among authors, institutions, and countries. Results: The findings reveal a significant increase in blockchain-related publications, particularly after 2021, indicating growing scholarly attention. Major research themes include blockchain foundations, security and privacy, decentralized finance, regulation, and cross-sector applications. The analysis also demonstrates increasingly interconnected global collaboration networks led by several productive countries. Conclusion: Overall, the findings indicate that blockchain has become an increasingly prominent research domain within financial studies. This study contributes by providing a comprehensive mapping of research trends and offering insights for future studies and policy development related to transparency and security in digital financial systems.
Industrial supply chains involve multiple stakeholders, complex logistics operations, and financial transactions that require transparency, traceability, and secure coordination.Traditional supply chain systems suffer from limited transparency, the risk of data manipulation, and insufficient trust among participants.To address these challenges, this paper proposes a decentralized industrial supply chain management system implemented on an Ethereum-compatible blockchain network.The proposed architecture integrates smart contracts to automate workflows, including stakeholder registration and verification, multi-item order processing, shipment tracking, simulated delivery verification (SDV), and escrow-based conditional payment settlement.The system adopts a hybrid on-chain/off-chain storage architecture in which transactional records are maintained on-chain, while raw material and product images are stored off-chain using the InterPlanetary File System (IPFS).This design reduces blockchain storage overhead while preserving data integrity through cryptographic hash references.To improve operational efficiency and reduce overhead from repeated transactions, the proposed system supports multi-item batch transactions during procurement and ordering, while the logistics and settlement stages maintain per-item execution to preserve traceability and accountability.Experimental evaluation was conducted on the Celo Sepolia network to measure gas consumption and transaction fees for both batch-based and functionally equivalent per-item execution workflows under controlled conditions.The evaluation included multiple predefined workload configurations, and statistical analysis using mean and standard deviation was performed to assess execution stability.The results indicate that transaction aggregation reduces gas consumption by approximately 40-43% for raw material order creation and by 40-48% for raw material operations (addToMultipleCart).Product aggregation workflows also demonstrated measurable gas-efficiency improvements.These findings demonstrate the efficiency benefits of multi-item transaction aggregation within the proposed implementation while preserving lifecycle traceability and escrow-enabled settlement correctness.The reported results represent controlled implementation-level efficiency measurements within the proposed blockchain-based supply chain architecture.
High-Frequency Foreign Exchange (FX) electronic execution networks process in excess of $7.5 trillion in daily spot volume across geographically distributed matching engines. Modern institutional trading infrastructure relies heavily on automated limit order book (LOB) forecasting and real-time natural language processing of macroeconomic news feeds. However, this convergence of deep learning and automated execution introduces systemic attack surfaces that traditional risk engines are unequipped to handle. In this paper, we present FOREX-SHIELD, an integrated, multi-modal cyber-defense pipeline engineered to mitigate spoofing, news injection, and regulatory privacy leaks in high-frequency FX settlement. First, we model high-frequency 40 x N LOB dynamics using a spatio-temporal DeepLOB framework combining 2D convolutional layers and recurrent units. We demonstrate that unhardened spatial price-volume representations are vulnerable to microsecond Targeted Projected Gradient Descent (PGD) perturbations, suffering an Attack Success Rate (ASR) up to 37.50% (and 15.62% under expanded 64-sample batch evaluations). To counter this, we implement dynamic on-the-fly adversarial retraining, which elevates model defense robustness up to 84.38%–100.00% (preventing 54 out of 64 prediction flips). Second, we fine-tune a domain-adapted financial Transformer (FinBERT) using class-weighted cross-entropy optimization to detect synthetic macro news attacks, achieving 85.71% accuracy, an F1-score of 85.71%, and 100.00% recall across adversarial probes. Third, we construct a Zero-Knowledge Proof (ZK-SNARK) settlement layer that deterministically validates Anti-Money Laundering (AML) risk limits and liquidity constraints (R <= 75) without leaking transaction balances or institutional counterparty metadata. End-to-end backtests show a full multi-stage evaluation latency of approximately 120 ms, proving operational viability for real-time institutional clearing.
Decentralized manufacturing faces a pre-contractual impasse: a Provider cannot price a service accurately without inspecting the design file, yet the Consumer cannot share that file without exposing intellectual property. We introduce the Optimistic Verifiable Claim (OVC), a blockchain protocol that lets a Consumer publish a verifiable claim about a concealed design (such as the material it consumes) and a Provider price and bid on it without seeing the design. The claim is committed when the service is posted and stands unless the selected Provider challenges it; a challenge triggers a deterministic on-chain check that exposes any dishonesty, and the design is disclosed only to settle a dispute, never on the honest path. We implement four checks (authorized key access, delivery-channel integrity, syntactic conformance, and declared material consumption) in Solidity and measure them on a real 6.41 MB G-code file, the 3DBenchy, across Ethereum, Arbitrum, and opBNB. Every service incurs the cost of posting the encrypted design, with or without a dispute. For the 3DBenchy, the no-dispute outcome costs \$7,207 in up to 9 hours on Ethereum, \$288 in 3 min on Arbitrum, and \$2.87 in 2 min on opBNB, and a fully contested dispute costs \$49,660 in up to 57 hours on Ethereum, \$1,988 in 19 min on Arbitrum, and \$19.73 in 13 min on opBNB. Costs and times grow with size: for a 50 MB industrial design, an undisputed service reaches \$56,173 and up to 3 days on Ethereum against \$22.36 and 16 min on opBNB, and a fully contested dispute reaches \$488,440 over up to 18 days on Ethereum against \$195 and 1.6 hours on opBNB. Of the four, the material-consumption check is the costliest, its predicate being the most expensive to evaluate on-chain. OVC makes confidential, claim-based bidding economically feasible on Arbitrum and opBNB, but not on Ethereum at industrial scale.