Distributed ledger technology (DLT) initiatives are frequently designed from a predominantly technical and functional perspective. Such a narrow focus often overlooks stakeholder-oriented non-functional requirements, thereby limiting adoption in complex socio-technical environments. To address this gap, this paper develops the SERS framework, a stakeholder-oriented framework that structures four key dimensions for the design and evaluation of decentralized systems: Security, Efficiency, Resiliency, and Sustainability. Following a Design Science Research approach, the framework is derived from a systematic literature review and refined through expert validation using the fuzzy Delphi technique. The resulting framework provides a domain-independent typology of stakeholder-oriented performance dimensions and associated assessment criteria. To demonstrate its applicability, the framework is used to guide the design of a multi-layer architecture integrating distributed ledger technologies, IoT, and Cloud/Edge/Fog computing within a healthcare context. The architecture is subsequently evaluated using the Fuzzy Analytic Network Process (Fuzzy-ANP) based on the SERS dimensions. The results indicate that the proposed architecture performs particularly strongly in terms of security and resiliency, while highlighting efficiency and sustainability as areas requiring further improvement. Beyond healthcare, the SERS framework provides a reusable mechanism for designing and evaluating decentralized socio-technical systems across a broad range of technology adoption contexts.
Open access
IoT and Edge/Fog Computing
Mobile Crowdsensing and Crowdsourcing
Innovative Approaches in Technology and Social Development
Supervisors need reliable assurance over balances, issuance, and transaction integrity. Institutions need toprotect sensitive financial data. Traditional audit practice often resolves this tension by granting broad accessto ledgers. That approach is effective, but costly in privacy, operational risk, and cross-border data exposure.This note presents a settlement architecture in which compliance statements can be proven cryptographicallywithout disclosing the full ledger. A supervised entity can demonstrate that a balance equals a value, exceeds athreshold, or lies within a band. Verifiers check the proof without receiving account-level books. Spending keysremain on the client side and do not travel to the operator to authorize a transfer.A second confidentiality property is reported that is easy to miss in architectural summaries: in a settlementthat updates both accounts in a single transition, the payer must know the recipientâs balance in order toconstruct the proof. Paying someone therefore reveals what they hold. The architecture addresses this with atwo-phase transfer, at a stated cost in finality latency.The paper is deliberately non-utopian. It specifies which properties become demonstrable and which residualtrust remainsâespecially in a single-node deployment where the operator may still observe state, sequencetransactions, or censor. The institutional claim is modest: zero-knowledge settlement can reduce routine fullledgerdisclosure while improving the quality of evidence for specific supervisory questions.
We present a comparative implementation of the same financial settlement circuit in five zero-knowledge proofsystemsâGroth16, Halo2/IPA, STARK/FRI, PLONK/KZG, and Nova/foldingâand report the design consequencesthat appear only when the comparison is performed on a complete stateful application rather than onmicrobenchmarks.The application is a minimal settlement layer with double-entry value conservation, spending authority, doublespendprevention, threshold issuance, burns, selective disclosure for supervision, and account freezing. Unlikebenchmark suites centered on primitives such as SHA-256, this setting forces persistent state updates, globalinvariants, and multi-authority rules into every backend.We document nine implementation findings absent from typical comparative tables. The most consequential forarithmetization is that AIR lacks native copy constraints, which creates a silent soundness gap when updatingMerkle state unless a lockstep dual-climb pattern is enforced. The most consequential for the application is thatsingle-step settlement discloses the recipientâs balance to the payer, which forced a two-phase transferdesign. We also report identical-condition measurements for setup, proving, verification, and proof size; explainwhy Groth16 was rejected despite superior proof size; and record methodology errors that were detected andcorrected during the work.The reference implementation is public and test-backed. It is not third-party audited and does not implementdistributed consensus. We treat both limitations as part of the result.
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.
Every widely followed Bitcoin cycle indicator (Pi Cycle, MVRV, Mayer, Puell) called turns precisely for a decade, then degraded in one sequence: precise, then early, then silent. This is one structural phenomenon. Across the four halving epochs (2011-2026), the per-cycle maxima of five top-calling oscillators decline monotonically while minima end higher, so any threshold calibrated on past cycles must stop firing; short-horizon indicators decay toward zero and several invert sign; yet Bitcoin's time structure stays fixed, with mature-cycle tops 525/546/534 days after their halvings and bottoms 406/364/366 days after their tops. Turns are identified retrospectively by a fixed mechanical rule, not a real-time record. Timing-free nulls put the joint clustering at 5e-6 to 1e-3 across every variant. A harder empirical null (block-bootstrapped paths under the identical rule) never reproduces the top cluster under its deterministic construction (0 of 10,000); the bottom cluster is largely intrinsic to the drawdown process (31-43% of paths), so the evidence concentrates in top phase-alignment. In block height (the exact 210,000-block unit) the top null stays 0 of 10,000 and partial bottom structure emerges; shape and volatility overlays do not improve. A causal power law in time-since-genesis (exponent near 5.6) is the only signal whose sign is stable across mature epochs, replicates on a second source and Ethereum, and whose timing edge over buy-and-hold turns positive in the current cycle (one holdout, suggestive not decisive). We rest nothing on per-epoch significance: a rotation null shows HAC inference over-rejects here (size 0.33 at nominal 0.05; p=0.21). Macro drivers (M2, yield curve) show the same instability and lose a joint horse race. We pre-register falsifiable windows: a 2026 bottom (Oct 5-Nov 16) and a next top 525-546 days after the following halving.
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.
This paper is a core incision paper from the Mathematical Canon of the Tri-Source System of The Unmanifest Selecting the Manifest. It aims to provide a unified structural common-root explanation for the Riemann Hypothesis, the Goldbach Conjecture, and GĂśdelâs Incompleteness Theorem, starting from the âPrimordial Oneâ as the sole foundational axiom, while bridging the underlying logics of mathematics, physics, and philosophy. The central thesis is that existing mathematics is built upon sensory intuition and operational habits, and is not foundational mathematics. The deviation begins at the very definition of â1ââwhich has been superficially treated as an isolated unit rather than the minimal complete structure of âdual-state unification of inward and outward orientations.â This initial misalignment has led to irreducible structural cracks in number theory, analysis, and logical systems; the Riemann Hypothesis, the Goldbach Conjecture, and GĂśdelâs Incompleteness Theorem are manifestations of these three cracks in their respective domains. Taking the dual-state unification of the Primordial One as the sole axiom (1 = inward ½ + outward ½, the two states indivisible), the paper redefines the ontological classification of numbers: 0 as the Origin Number (the unmanifested starting position); 1 as the Primordial Number (the minimal complete whole of dual-state unification); 2 as the dual-state juxtaposition position (geometrically bisectable but lacking skeletal-carrying capacity); and 3, 5, and 7 as Skeleton Numbersâdefined by the rule that, under exhaustive two-dimensional and three-dimensional geometric bisection attempts, no bisection can be performed without breaking at least one complete Primordial-One unit, i.e., âgeometric bisection necessarily breaks the One,â manifesting as self-locking between units. 3 is the first Skeleton Number (the smallest nucleus-bearing number), 5 is the second (the skeleton can expand outward), and 7 is the third (the skeleton can systematically unfold). The paper asserts that the Skeleton Numbers are exclusively 3, 5, and 7, and that no fourth Skeleton Number greater than 7 exists. On this classification, prime numbers are redefined as ânonequilibrium numbersâânumbers that cannot be received and structurally locked by the skeleton structure; composite numbers are those that can be received and structurally locked. The Goldbach Conjecture is thereby rewritten as the dual-point compensation closure problem of even structures: the structural rigidity of even structures requires two nonequilibrium numbers (primes) to complete compensation, rather than being an empirical additive coincidence. The reason that all nontrivial zeros of the Riemann zeta function lie on the critical line Re(s)=½ is traced to the symmetric midline of the Primordial Oneâs dual statesâ½ is not a technical coincidence but a shadow projection of the overall balanced structure in the language of classical analysis. GĂśdelâs Incompleteness Theorem is repositioned as a consequence of the old systemâs foundational distortion arising from starting with an isolated â1,â rather than an ultimate fate of logic. The paper also connects the dual-state Primordial One to physical phenomena such as quantum entanglement and wave-particle duality, arguing that quantum entanglement observed in physics is precisely the ontological manifestation of the Primordial Oneâs dual-state unificationâthe mathematical âOneâ and the physical âentanglementâ are reunified under the same primordial ground. Four explicit falsification conditions are provided: if a fourth Skeleton Number greater than 7 exists; if the Goldbach Conjecture produces a counterexample under this system; if any nontrivial zero of the Riemann zeta function strictly deviates from Re(s)=½ and cannot be explained within the structural projection framework; or if, after supplementing the Primordial One axiom, GĂśdel-type incompleteness reemerges with the same structural strengthâverification of any single condition would falsify this system. The paper does not claim to have completed the final formal proofs of all three problems, but rather to have provided a unified structural common-root explanation for the three ultimate mathematical problems, and to have established a unified floor from the Primordial One to number theory, analysis, logic, and physics. Readers with genuine academic judgment can, from this paper alone, recognize the structural trajectory of the higher-order propositions and proceed with professional derivation or translation tools as needed. This is a constraint of circumstance, not a diminishment of scholarly value. May this knowledge reach the place it is meant to reach.
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.
Victor Michelle, Natalie Michelle, Emilie Michelle, Elias Michelle
This paper introduces Prediction Assets â a fundamentally new class of financial instruments where the underlying asset is market consensus on probability itself. Unlike traditional prediction markets, where binary event contracts terminate abruptly upon resolution, Prediction Assets are engineered as perpetual financial instruments that evolve rather than expire. Upon event occurrence, the asset does not liquidate to zero or a fixed payout; instead, it programmatically transforms into a new functional asset form (such as a currency, index, or memory asset) via smart-contract-enforced conversion ratios, establishing an infinite lifecycle and continuous capital efficiency. Key Structural & Mathematical Contributions: Core Asset Pricing Model: Establishes the foundational pricing equation \(P_{asset} = P(E) \times M\) driven entirely by open order-book decentralized exchange (DEX/AMM) spot liquidity without reliance on subjective analytical oracles. Systemic Market Efficiency: Implements an exact arbitrage condition boundary constraint (\(\sum P_{asset,i} = M\)) to incentivize algorithmic market-making and eradicate structural price variance. Programmatic Post-Event Evolution: Introduces the deterministic conversion coefficient \(C(t, state)\) locked at genesis to handle automated migration profiles (Currency, Index, Memory, and Derivative states) with zero administrative discretion. Decentralized Governance: Outlines a 4-channel Multi-Chain Consensus Verification Layer (CVL) requiring a strict 3-of-4 quorum across official APIs, open-source replicas, academic mirrors, and market sentiment vectors. Regulatory Engineering: Delivers a comprehensive compliance analysis under the U.S. Securities Framework (Howey Test and Reves Test boundaries) and CFTC Event Contract frameworks, positioning the topology as a non-security utility asset. Prospective Implementation:The paper presents AIVA (Artificial Intelligence Valuation Asset) as the world's first prospective implementation tracking the global macro-consensus probability of achieving Artificial General Intelligence (AGI), which programmatically transforms into an operational settlement currency for autonomous multi-agent economic environments upon verification. Keywords: Prediction Assets, Probability Markets, Financial Instruments, AGI, AI Agents, Decentralized Finance, Synthetic Assets, Valuation Markets. Citation Note: This specification expands upon the sovereign fintech frameworks established in IP Stock Exchange v3.3-Evolution (DOI: 10.5281/zenodo.20687136).
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.
A controlled timestamp-only projection of an ABD-style multiwriter quorum-register tag leaves completed reads current, exact, and linearizable while omitting the writer-identity component of the full protocol tag. Correctness therefore does not imply auditability at a distributed read boundary. A deterministic five-state host gives the protocol-independent companion: four sequential operations are totally ordered in real time, yet the read boundary exposes no source evidence. For an external specification fixed before the realization is chosen, zero-error boundary recovery exists exactly when all co-encoding locations agree and native source evidence aligns with the specified source and derivation relation. Under edit-generated source semantics, boundary agreement is equivalent to temporal zero-delay synchronization. Disjoint audited systems compose, but two independently verified services can assign incompatible roles to one shared location. Value projection likewise fails to preserve auditability; a certified refinement law transports it. A CAP partition carrier induces the corresponding read-boundary discriminator, but carrier data alone cannot supply the scheduler continuations required for an FLP transport. Finite DLS histories yield an exact stabilization-certificate bound and exclude one fixed evidence budget across all horizons. Protocol and replicated-storage rows illustrate the criterion across distributed boundaries. The substrate-independent ledger records the broader scope. Lean 4 verifies the finite models, reductions, abstraction maps, and internal bounds; the premises linking named systems to those models remain documentary.
Ledgeral Mathematics: A Finite Algebra of Recursion, Admissibility, Projection, and Survivor Structure This repository contains the complete public edition of Ledgeral Mathematics, a foundational mathematical monograph that develops a finite algebra of recursion, admissibility, projection, survivor formation, residue retention, transport, composition, optimization, falsification, and audit. The theory begins from the retained finite record, an explicitly formed object whose carrier, addresses, entries, active support, inactive structure, status, formation history, comparison discipline, readout route, and audit relation remain part of its mathematical identity. Ledgeral Mathematics begins at a more primitive level than mathematical systems that take numbers, points, sets, spaces, functions, graphs, trajectories, or continua as already available objects. Those structures may be constructed and used within the theory, though they do not receive automatic foundational standing. Every object must first declare what carries it, what occupies each retained address, how it was formed, what operations may act upon it, what transformations are permitted, and what information must remain available after those transformations have occurred. The central admission principle is straightforward. Nothing enters the mathematics by implication. Every lawful object must have a finite retained form. Every operation must declare its input region, carrier rule, entry rule, legality conditions, invalidity conditions, and output status. Every comparison must identify the equality relation being used. Every readout must preserve a trace to the record from which it was produced. Every projection must identify what survives, what is rejected or displaced, and how the full event can be audited. This discipline allows Ledgeral Mathematics to preserve distinctions that conventional notation may compress or erase. A lawful null record is different from an invalid expression. A missing object is different from a retained object with inactive support. Candidate status is different from survivor status. Residue is different from error, absence, or nonexistence. Carrier equality, support equality, entry equality, readout equality, provenance equality, and full record equality are separate mathematical claims. The relevant comparison must therefore be declared rather than assumed. One of the central structures of the theory is the survivor-residue-audit form of projection. A candidate record is submitted to a declared admissibility rule and projection procedure. The projection produces a survivor, a residue, and an audit packet. The survivor contains the structure admitted by the projection. The residue retains rejected, displaced, suppressed, obstructed, unresolved, or otherwise excluded structure. The audit records the candidate, the governing admissibility conditions, the projection route, the resulting survivor, the resulting residue, and the verification status of the event. Projection therefore does more than select an accepted output. It retains the mathematical consequences of exclusion. Loss becomes inspectable. Rejection becomes information. Suppression remains traceable. A lawful null survivor may coexist with nonempty residue. An active survivor may retain displaced structure outside its support. A mixed event may preserve admitted components, rejected components, and formation failures under different statuses. These distinctions allow later analysis of irreversibility, obstruction, instability, hidden coupling, model disagreement, implementation failure, measurement conflict, and operation-order dependence. Recursion is developed through the same finite retained discipline. A process does not receive an unbounded history in advance. It is represented through finite depth carriers, finite update words, finite survivor chains, finite branch records, finite residue histories, and finite continuation audits. Persistence is established through repeated admitted continuation across retained recursion depth. Branching, merging, recurrence, stabilization, obstruction, termination, return, cyclic behavior, and irreversible loss remain available as explicit finite structures. The monograph extends this foundation into operator-word algebra, holonomy calculus, finite transport and boundary accounting, constitutive algebra, branching and capacity calculus, co-admissibility, convergence, directed persistence, signal and readout calculus, finite recursion-spectral analysis, regime classification, construction and optimization, audit and falsification, and representation-layer quarantine. The full work is organized across twenty-three major sections, a global closure, and five technical appendices devoted to notation, dependency tracking, result indexing, verification, reproduction, serialization, archiving, implementation boundaries, and execution audit. Representation remains available throughout the theory, though its role is controlled. Equations, arrays, tables, coordinates, diagrams, graphs, curves, spectra, statistical models, analytic expressions, and continuous systems may be generated as readouts from ledgeral records. A representation does not become a native object merely through familiarity or usefulness. It may enter native calculation only after it has been reconstructed as a finite retained record with a declared carrier, entries, role, formation rule, and audit trace. This separation preserves the distinction between a mathematical object and the representation used to inspect, communicate, or calculate with it. Ledgeral Mathematics was developed partly in response to the foundational requirements of Post-Temporal Physics, though it is presented here as an independent mathematical system. Its potential applications extend across foundational mathematics, algebra, logic, proof theory, discrete systems, physics, computation, artificial intelligence, formal verification, data provenance, system assurance, engineering, sensing, control, optimization, scientific measurement, model comparison, reproducibility, and falsification. The theory does not claim that established mathematical systems are unnecessary. It presents a distinct foundational program organized around finite formation, retained accountability, explicit admissibility, preserved residue, and auditable transformation. This repository contains the foundational public volume. Implementation-oriented methods, domain-specific extensions, and the separate companion program known as Applied Ledgeral Mathematics are outside the scope of this release and are not presently being distributed openly. Portions of that work may carry significant dual-use implications. Any future distribution of unpublished applied material may therefore be considered individually following appropriate legal, export-control, security, intellectual-property, and end-use review. This publication-scope notice does not designate the public monograph or any unpublished companion material as classified, ITAR-controlled, EAR-controlled, export-controlled, or otherwise restricted by the United States Government. Any legal determination of that kind must be made by qualified authorities or professional counsel. The published monograph is released under the Creative Commons Attribution 4.0 International License. That license applies only to the material contained in the publicly released volume. It does not apply to unpublished manuscripts, software, datasets, implementation packages, technical materials, or companion works unless those materials are separately released under the same license.
Niko Silitonga, Harya Widiputra, Fangky Antoneus Sorongan
Fiscal decentralization has been widely implemented to improve regional fiscal efficiency and strengthen local fiscal capacity. However, empirical evidence regarding its effectiveness remains inconclusive, particularly in developing countries with diverse institutional capacities. This study examines the associations between regional fiscal policy instruments and provincial fiscal performance in Indonesia, proxied by the growth of Locally Generated Revenue (PAD), while investigating the moderating role of fiscal decentralization. Unlike previous studies that examine fiscal instruments separately or focus mainly on macroeconomic outcomes, this research develops an integrated framework that evaluates financing allocation, development expenditure, transfer funds, and other legitimate revenues within a moderated panel-data model. Using panel data from 33 provincial governments during 2017â2024, the study applies a fixed-effects regression model with interaction terms. The results show that development expenditure is positively and significantly associated with provincial fiscal performance, indicating that productive public spending strengthens regional fiscal capacity. In contrast, financing allocation and transfer funds show no significant direct associations with fiscal performance. Other legitimate revenues demonstrate a positive but limited association. Fiscal decentralization plays a dual moderating role by strengthening the association between transfer funds and fiscal performance while weakening the effects of development expenditure and other legitimate revenues. These findings suggest that the effectiveness of fiscal decentralization depends on fiscal instruments and local institutional capacity rather than producing uniform outcomes. This study contributes to the fiscal decentralization literature by providing an interaction-based empirical framework and practical evidence to support more effective decentralization policies and improve provincial fiscal performance in Indonesia.
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%â100.00% (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.
The advent of blockchain technology has created a paradigm shift in the way digital data can be securely, transparently and decentralized managed, a paradigm that could potentially replace the longstanding centralized digital information systems. This is a full journal-grade review of blockchain as a tool to ensure trustless, immutability and decentralized data governance in a variety of important application fields. The study, which is based on a systematic review of 20 peer-reviewed publications from 2023 to 2025, explores the essential structural elements of blockchain systems: Distributed ledger structures, cryptographic hash functions, Merkle tree integrity verification, consensus mechanisms, and smart contracts, and how they all contribute to removing single points of failure and institutional trust dependencies. There is a comparative study of the various public, private and consortium blockchain types, as well as the evaluation of the various consensus algorithms, such as Proof of Work (PoW), Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT). The results show that data management systems based on blockchain technology always have superior data integrity, access auditability, censorship resistance, and user data sovereignty properties compared to centralized systems, and come with trade-offs in scalability, energy efficiency, and compliance with regulations. Evidence collected for the application has come from health care organizations' record management, supply chain traceability, decentralized identity systems, Internet of Things (IoT) data integrity, an energy company data management system, and cybersecurity threat intelligence, among other contexts. Key challenges and emerging technologies, such as quantum computing systems, post quantum cryptographic standards, layer-two rollups, sharding and zero-knowledge proofs, are explored in tandem with the blockchain trilemma, GDPR compliance issues and cross-chain interoperability. The study finds that blockchain-based data management is moving from the experimental stage to becoming a core component to the digital economy's infrastructure.
While decentralized fintech platforms (DFPs) are a viable alternative to traditional financial systems, their widespread adoption is hindered by cybersecurity concerns. This study investigates the factors shaping user intentions to adopt DFPs amid these perceived cyber risks. Situated within the global challenge of financing the Sustainable Development Goals (SDGs), this research explores DFP adoption as a potential mechanism for enhancing financial inclusion (SDG 10), fostering innovation in financial infrastructure (SDG 9), and promoting inclusive economic growth (SDG 8). Using a hybrid model that merges the technology acceptance model with trust theory, we surveyed 554 business and engineering students across five Indian metropolitan hubs a demographic representing a tech-forward segment of Gen Z poised to be early adopters. Structural equation modelling reveals that while fintech literacy and risk tolerance are significant preconditions, adoption intention is most powerfully predicted by social influence (β = 0.271, p < 0.001) and perceived security in the underlying technology (β = 0.186, p < 0.000). This suggests that for these vanguard users, the decision to adopt is driven more by social validation and technological faith than by individual technical competency. Furthermore, trust moderates the relationship between fintech literacy, perceived security and risk tolerance on the intent to adopt. The paper contributes a comprehensive framework that clarifies usersâ adoption motives, aiding researchers who study DFP user behavior in developing nations. The findings provide a critical roadmap for policymakers, developers, and international development agencies aiming to harness digital finance for the 2030 Agenda, demonstrating that building trusted, community-driven ecosystems is paramount to realizing the developmental potential of decentralized technologies.
Proof of Work (PoW) and Proof of Stake (PoS) remain the dominant consensus mechanisms for public blockchains, yet both suffer from well-documented limitations: PoW incurs prohibitive energy costs and scales poorly with network size, while PoS tends to concentrate validator influence among large stake holders, reintroducing a form of economic centralization.This paper proposes Proof of Effort (PoE), a novel consensus mechanism in which validator eligibility is determined exclusively by verifiable, continuous protocol participation rather than by computational expenditure or financial stake.PoE integrates four lightweight security primitives -node-identity binding via asymmetric key pairs, sequence-number-based replay prevention, digital-signature message authentication, and threshold-based malicious-node detection -chosen specifically to impose negligible per-node overhead while maintaining Byzantine resilience.We formalize the effort accumulation model mathematically, provide a detailed pseudocode specification of the protocol, and evaluate the mechanism through an agent-based simulation implemented in NetLogo 6.4 with 500 heterogeneous agents (350 honest, 150 adversarial).Simulation results, presented with full graphical and tabular support, demonstrate that PoE achieves an average throughput of 900 tx/s (vs. 25 tx/s for PoS and 7 tx/s for PoW), reduces average consensus latency to 56 ms (vs.400 ms and 1,200 ms), and correctly identifies 92-97% of malicious agents while accepting fewer than 0.2% of forged or replayed messages.The effort gap between honest and adversarial nodes remained below 0.21%, confirming protocol fairness under adversarial load, and the validator-selection Gini coefficient of 0.18 represents a 71% improvement in participation equality over PoS.These results position PoE as a compelling candidate for permissioned and consortium blockchain deployments where energy efficiency, participation fairness, and lightweight security are primary requirements.
Decentralised Autonomous Organisations (DAOs) raise fundamental questions for private law. While scholarly and regulatory attention has primarily focused on the legal status and governance of DAOs, comparatively little consideration has been given to how disputes involving these organisations should be resolved. This article examines the suitability of dispute resolution mechanisms for DAO-related disputes. Analysing the principal categories of disputes that have emerged in practice, the article critically evaluates blockchain-based dispute resolution mechanisms alongside traditional arbitration, in light of due process, enforceability, party autonomy, and the unique features of decentralised governance. It argues that neither purely code-based dispute resolution nor conventional litigation provides a satisfactory response to the complexity of DAO disputes. It concludes that arbitration, appropriately adapted to the technological and organisational realities of DAOs, offers the most promising framework for balancing decentralisation, legal certainty, and procedural fairness within the evolving Web3 ecosystem.
Vabuk Pahari, B. Chandrasekaran, Johnnatan Messias, Krishna P. Gummadi ¡ 5 authors
A decentralized autonomous organization (DAO) is a governance entity that allows its stakeholders to manage blockchain-based protocols through smart contracts. The DAO explicitly specifies how stakeholders make and enforce decisions concerning a protocol's operation in a smart contract, aptly referred to as its governance contract. The design of this governance contract, therefore, has far-reaching implications for the security (trust) and privacy (transparency) of the smart contracts managed by the DAO and its stakeholders. In this work, we (i) explicate the trust and transparency trade-offs of the design choices in implementing a DAO and (ii) highlight how poor choices introduce critical vulnerabilities, using real-world examples as case studies. To this end, we analyze $48$ public, actively used Ethereum-based DAOs that control a vast capital. We classify the design choices into a handful of key dimensions that succinctly capture how a DAO's stakeholders initiate a protocol change, vote on it, and, based on the voting outcome, execute that change. Our analyses crucially uncover a new class of attacks, which we call governance attacks, that directly exploit the fundamental design of a DAO's governance mechanisms, even if we assume bug-free implementations.
Jemima Owusu-Tweneboah, Amani Altarawneh, Deepti Gupta, Maria Luisa Figueroa
Cybersecurity and privacy frameworks such as NIST SP~800--53, ISO/IEC~27001, GDPR, and HIPAA are widely used to guide organizational security posture and regulatory compliance. In practice, however, framework adoption is often assessed through point-in-time audits, self-attestations, and fragmented evidence reviews, providing limited assurance that controls are consistently implemented, independently validated, and sustained over time, particularly in environments that rely on third-party vendors. These limitations are amplified in multi-vendor ecosystems, such as healthcare remote patient monitoring (RPM), where compliance obligations span organizational boundaries and assessments are conducted by multiple independent assessors. This paper investigates how permissioned blockchain systems can support framework implementation maturity measurement rather than static compliance verification. We propose a blockchain-based Third-Party Risk Assessment (TPRA) framework that operationalizes assessment workflows, enforces multi-party governance, and preserves longitudinal assessment state using programmable smart contracts. Building on this framework, we introduce a set of evaluation metrics and a qualitative maturity model designed to assess whether compliance controls are verifiably implemented, governed, and sustained across repeated assessment cycles.
Blockchains rely on transparency and immutability to ensure trust, but these guarantees come at the cost of an ever-growing ledger that increasingly threatens decentralization by making it more expensive to store and maintain the full transaction history. In this work, we introduce state-invariant transactions, defined as transactions whose inclusion or removal does not affect the resulting blockchain state beyond transaction fees. We argue that these transactions constitute a form of on-chain spam because they consume execution, bandwidth, storage, and blockspace without contributing to the final ledger state. We present the first large-scale measurement of state-invariant transactions across Ethereum, Optimism, and Base, identifying nearly 1.4 billion such transactions. While only 2.6% of Ethereum transactions are state-invariant, they account for 24% of transactions on Optimism and 37% on Base, representing a significant source of unnecessary resource consumption on Layer-2 blockchains. We show that speculative Maximal Extractable Value (MEV) is the dominant source of state-invariant transactions on Optimism and Base, accounting for 57% and 68%, respectively, but is not the only source as previously assumed. Moreover, despite its popularity, speculative MEV is not the most profitable strategy once the costs of state-invariant transactions are considered. Beyond MEV, we identify substantial malicious activity, with address poisoning campaigns accounting for 53% of non-reverted state-invariant transactions on Ethereum. Our findings suggest that mitigating state-invariant transactions could substantially reduce blockchain resource consumption and transaction costs while limiting phishing campaigns and other forms of blockchain abuse.