Authentication in Web 3.0 faces a structural conflict. Systems that offer full anonymity leave no lawful way to identify a malicious actor. Systems built for accountability expose a persistent wallet address to blockchain-graph analysis, or fall back on centralized key recovery. Existing designs solve one side of this conflict at the cost of the other. This paper presents PEUAP-W3, a Privacy-Enhanced and User-centric Authentication Protocol. Its contribution is the integration of five established components into a single deployed and formally analyzed system. A Circom 2 circuit of 1579 Groth16 constraints proves four facts in a single 192-byte on-chain proof: knowledge of an opening of the session credential commitment, an SpO2 value inside an 85â100% band, single-use nonce binding, and HMAC integrity. Shamir (k = 2, n = 3) sharing distributes the identity payload across three independent relays. The coordinator reconstructs an identity only after a threshold vote has been recorded on chain. Credentials are issued as W3C Verifiable Credentials 2.0 in did:key form. Four Solidity contracts verify the proof on Ethereum Sepolia. Verification costs about 241,000 gas and takes roughly 3 ms. ProVerif and Scyther find no attack under the DolevâYao model. A concurrency sweep to 500 simultaneous requests completes 1191 requests with zero failures at about 15.4 requests per second. A behavioral gate screens commodity abuse as a supplementary control; it is not treated as a security boundary. Against a nine-property framework, PEUAP-W3 satisfies six properties. Three remain conditional and are not verified in the current deployment: biological-origin assurance and digital replay prevention, both of which need an attested sensor; and GDPR erasure equivalence. Here, formally verified refers to the protocol models and theorems, not to the complete deployed software.
A Research Program on the Gift as Economic Primitive, and the Register of Everything That Could Show It Wrong This document states a research program and the conditions under which it should be abandoned. The program's hard core is a single claim about direction: that value can be organized to move only forward â from giver to receiver to the next receiver â and that a system built on that constraint circulates better than one that permits return to the source. Four chapters name the ways the core can break: whether receiving creates the capacity to give, whether the constraint survives a change of currency, whether it survives past the family, and whether it survives the giver. Each chapter is attached to pre-registered predictions, published here as a register of sixty-six items with their falsifiers, their instruments, and their status. The program is published at a deliberate moment: almost nothing in it has been run. Two desk censuses have returned results, both null or partial-null. There have been no field tests. The first is gated on a product launch in August 2027. A register published after the data arrives cannot be distinguished from a register assembled to fit it; this one is published while the outcome is unknown, which is the only condition under which it constitutes evidence of anything. --- Provenance. This paper is part of the THonly research corpus, dedicated to the public domain under CC0 1.0. The canonical version is at https://thonly.org/research/which-way-value-moves. Its SHA-256 is 89dfd48398c1c23ec6613ae953a3b326a469f8a14e76258fcdabc46fea156d5b, independently timestamped to the Bitcoin blockchain via OpenTimestamps and signed under RFC 3161 by three trust authorities, one of them eIDAS-qualified. AI co-authorship is disclosed. Miss Aquarius is the consistent name used for the AI collaboration across all venues.
The present research explores how blockchain technology and cryptocurrencies challenge the traditional continental civil law framework. By reassessing the legal taxonomy of digital assets, the paper argues against their strict classification as jura in personam, primarily due to the absence of a designated debtor in permissionless networks. Alternatively, it supports the recognition of a sui-generis real right (jus in re) grounded in the concept of âcryptographic possessionâ. Furthermore, classical civil classifications are reinterpreted to address the ubiquitous nature of digital assets and the inherent complexities of jurisdictional localization. The study highlights the legal distinction between fungible cryptocurrencies and Non-Fungible Tokens (NFTs), alongside the emerging fructiferous character of assets deployed within Decentralized Finance (DeFi) ecosystems, which generate civil fruits. To contextualize these shifts, three European regulatory paradigms are evaluated: the French dualist approach under the PACTE Law, the German institutional integration into the banking sector, and the Swiss DLT framework, which innovatively merges substantive rights with digital tokens. Finally, the analysis focuses on the practical implications for the pathology of legal relations, particularly regarding the efficacy of forced execution and the safeguarding of the creditors' general pledge. The paper concludes with targeted de lege ferenda proposals for the Romanian legal system. These include the express statutory recognition of digital assets as intangible movable property in the Civil Code, alongside modern civil procedure mechanisms, such as the judicially mandated surrender of private keys under penalty and automated electronic garnishment, aimed at harmonizing state coercive power with the realities of the Web 3.0 economy.
Decentralized unmanned aerial vehicle (UAV) swarms require low-latency peer communication while remaining resilient to spoofing, replay, command injection, key compromise, and malicious membership changes. This study develops a zero-trust communication framework that separates the real-time swarm data plane from a permissioned Byzantine-fault-tolerant trust ledger. The method is grounded in an existing GPS-denied UAV software baseline implementing canonical packet hashing, HMAC-SHA256 authentication, trust epochs, timestamp and sequence freshness checks, onboard security-state transitions, command-policy gating, and firmware trust records. The proposed extension introduces per-node identities, authenticated session establishment, AEAD-protected peer traffic, and event-sparse ledger anchoring for trust-changing evidence. Formal models are derived for message acceptance, trust dynamics, Byzantine tolerance, consensus traffic, storage growth, processing overhead, and energy cost. Under a representative analytical case of 100 swarm messages/s, a 1% anchoring ratio reduces ledger event rate and modeled consensus-control traffic by 100 times compared with per-packet anchoring. Repository benchmark measurements are reported separately from blockchain projections. The analysis supports using blockchain as a decentralized trust anchor rather than as a transport for flight-critical telemetry.
Background: In recent years, the Internet of Medical Things (IoMT) has transformed the healthcare sector through real-time patient monitoring and continuous data collection.However, transmitting sensitive medical information over public networks exposes IoMT systems to significant security threats, while emerging quantum computing technologies challenge the reliability of traditional cryptographic systems.Objective: The objective of this study is to propose PQAC-BIoMT, a secure and robust model for remote user authentication and access control in IoMT environments, capable of withstanding both conventional and quantum attacks.Methods: This article proposes a decentralized authentication framework that integrates post-quantum cryptography using Kyber Public-Key Encryption (Kyber-PKE) into blockchain-based smart contracts.Fog computing nodes are used to reduce the authentication latency and improve the system scalability.A role-based authorization mechanism is integrated to link user identities to functional roles and enforce authorization to medical data and system resource access.Formal security verification is conducted using Burrows-Abadi-Needham (BAN) logic to validate the correctness of authentication, and the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool is used to assess resistance to known attacks.PQAC-BIoMT is further evaluated through a comparative analysis of the computational load, energy consumption and security properties.Results: Our security analysis demonstrates that PQAC-BIoMT effectively resists common attacks while providing quantum-resistant protection against them.The performance evaluation shows that the proposed scheme achieves relatively lower computational and energy overhead compared to existing approaches, making it suitable for resource-constrained IoMT devices.Conclusion: The proposed PQAC-BIoMT scheme delivers a secure, quantum-resilient authentication and authorization mechanism for IoMT systems, enhancing both data protection and operational efficiency, which can support practical deployment in real-world IoMT applications.
Open access
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
Centralised cloud authentication and authorisation commonly rely on a trusted server for identity, policy enforcement, and audit logging, which can increase the impact of compromise and complicate tamper-evident accountability. This paper presents a decentralised biometric authentication and authorisation framework that integrates fingerprint minutiae verification with Ethereum smart contracts and IPFS-based off-chain storage. Biometric templates are processed off-chain using a NIST NBIS-based pipeline ( MINDTCT minutiae extraction and Bozorth3 matching) and protected via SHA-256 hashing, zlib compression, and authenticated encryption (Fernet), while the blockchain stores only integrity metadata (hashes and IPFS content identifiers) and authorisation state. To mitigate replay attacks without introducing additional session-creation transactions, the framework uses a domain-separated request-binding hash ( tokenHash ) over the caller, nonce, timestamp, chain id, and contract address, validated on-chain together with one-time nonce consumption. A prototype was implemented using Solidity on a local Ethereum-compatible test network (Ganache), with Python/Web3 tooling and an IPFS-compatible content-addressed storage interface. Biometric performance is reported using standard metrics (FAR/FRR/EER and ROC/DET curves) on SOCOFing and FVC2002/FVC2004 benchmarks. In the core blockchain experiment, the proposed single-call attendance workflow required 74,773 gas while adding nonce-based replay protection and auditable authorisation checks on top of CID/hash anchoring (the separate literature benchmark reports end-to-end call-path gas under a shared harness). Compression reduces encrypted-template storage by approximately 76â84% across tested dataset sizes. Security analysis and attack simulation show that replay attempts (nonce/token reuse), unauthorised access, and off-chain payload tampering are rejected under the stated threat model.
Blockchain-based credential verification provides durable integrity and decentralized validation, but the persistence of blockchain commitments can make premature disclosure of sensitive attributes difficult to reverse. This study developed a pre-commitment privacy risk intelligence framework that assessed potential information leakage before credential commitment and used the estimated risk to guide adaptive protection. Synthetic credential records and simulated attacker-side information were employed to characterize privacy exposure through uniqueness, re-identification susceptibility, cross-source linkage, attribute inference, and attributeinteraction effects. These indicators were transformed into predictive features and evaluated using machinelearning models for continuous privacy-risk estimation and categorical risk assessment, followed by an optimization stage that considered privacy reduction and retained utility. In the reported training experiment, the gradient-boosted regression model achieved a mean absolute error of 0.03290, root mean square error of 0.04645, and coefficient of determination of 0.84398. Logistic regression provided the strongest classification performance, attaining 85.63% accuracy, 81.33% recall, an F1-score of 82.44%, and a privacy false-negative rate of 18.67%. A subsequent evaluation of 1,100 records reduced the mean privacy-risk score from 0.81826 to 0.67337, corresponding to a 17.71% relative reduction, while maintaining a mean utility score of 0.84227. The findings indicated that pre-commitment privacy assessment could support risk-sensitive protection decisions while retaining substantial utility for verifiable credential processing.
Cybercrimes that exploit virtual assetsâincluding laundering, concealment, and illicit financing through the dark webâare increasing rapidly, while existing tracking tools remain limited when offenders leverage multi-layer blockchain architectures and off-chain mechanisms to obscure fund flows. This paper proposes a practical full-node-based blockchain forensic framework for the automated detection and tracking of illicit virtual asset transactions across Layer-1 and Layer-2 environments. The framework operates a full-node network to construct a continuously updated database of all on-chain transactions, from which exchange-controlled internal addresses are identified using six formalized heuristics (H1âH6) expressed as a weighted-sum scoring model. A unified multi-layer transaction graph incorporates Layer-2 eventsâpayment channel closures, rollup batch submissions, and bridge deposits and withdrawalsâas contextual edge attributes correlated with Layer-1 settlement. Protocol-specific cross-layer correlation procedures, covering Arbitrum retryable tickets, Optimism cross-domain messages, zkSync Era batch commitments, and third-party bridge relays, were validated on live main-net transactions. Applying the framework to 7511 suspect wallet addresses, 821 (10.93%) were attributed to four Korean exchanges, and real laundering cases involving mixing and swappingâtogether with integrated real-time alerting and transaction-freeze request functionsâdemonstrate its direct applicability to law enforcement investigations. In addition, attribution reliability is quantified through the 98.80% labeling consistency observed across repeated independent collections of the same addresses, the standard forensic metrics are formally defined together with publicly released evaluation tooling, and the end-to-end detection latency is bounded analytically by the confirmation properties of the underlying protocols, substantiating the real-time capability of the framework.
Ho Thanh Tri, Le Hoang Minh Khue, Le Dinh Van, Tran Gia Linh ¡ 6 authors
As the digital economy rapidly develops, quick access to capital has become a critical survival factor for individuals intending to start a new business. However, under traditional bank lending systems, these aspiring entrepreneurs face significant barriers due to complex financial documentation requirements and stringent credit history checks. Drawing on the Technology Acceptance Model (TAM), this study investigates factors influencing usersâ adoption of blockchain-enabled digital lending platforms among individual customers with startup intentions in Vietnam. The empirical model examines the effects of Perceived Ease of Use and Perceived Usefulness on Attitude Toward Using, and the effect of Attitude on Behavioral Intention to Use. The results show that both perceived ease of use and perceived usefulness positively influence usersâ attitudes, while attitude strongly affects behavioral intention. Blockchain-related characteristics, including decentralization, data immutability, and smart contracts, are discussed as technological mechanisms that may improve lending efficiency, transparency, and usersâ confidence in digital lending systems. The study provides practical implications for banks and FinTech firms seeking to design user-friendly and secure digital lending platforms for underserved entrepreneurial users.
Stian Lybech, Eun-Young Kang, Riccardo Tonello, Anders Dalskov
This paper develops a model of a smart-contract language for a blockchain architecture with off-chain components. Off-chain components are pieces of smart contracts that execute at designated locations outside of the network of blockchain nodes, but remain synchronised with the on-chain contract state. They react to changes to the on-chain state, but may also notify the on-chain component about events in the world, e.g. stock prices, weather data etc., or even act as a bridge between different blockchains. This affords greater flexibility for the developer, but may also enable new vulnerabilities. As a concrete example, we use the model to study the problem of ensuring integrity and secrecy of data between the on-chain and off-chain components, using static information flow control techniques. This fails, even in the absence of a loop construct, because off-chain components act as separate threads and can encode a blocking construct e.g. through recursive method calls. We end the paper with a discussion of possible ways to remedy this situation.
Alifta Dicasani, Galih Mahardika Munandar, Muhammad Nur Wahyu Hidayah, Faradhina Azzahra
Post-disaster aid logistics is frequently affected by fragmented data, mismatches between needs and availability, distribution delays, and weak accountability. This study integrates a systematic literature review (SLR) and a proof-of-concept (PoC) to examine how blockchain can support data governance and aid traceability. Articles published in 2020-2025 were retrieved from ScienceDirect and Scopus, selected using PRISMA principles, and synthesized thematically from 31 eligible studies. The synthesis identified coordination and information alignment, along with traceability of goods, funds, beneficiaries, and delivery status, as the dominant issues. Frequently reported mechanisms included distributed ledgers, smart contracts, audit trails, cryptographic identities, and role verification. These findings were translated into a Solidity-based smart-contract PoC on a local Ganache network covering requests, stock, allocation, shipment, receipt validation, and event logs. Three synthetic scenarios and an access-control test showed that valid transactions were recorded, over-allocation was rejected, receipt discrepancies were flagged, and unauthorized operations were reverted. Blockchain is therefore better positioned as an infrastructure for data governance and transaction validation than as a standalone solution to all disaster-logistics problems.
A Universal, Options-Free, One-Byte Market-State Primitive: Cross-Asset Transfer, Distributional-Novelty Detection, and Privacy-Preserving Federation Randolph James Ferlic, M.D. and Kimberly Kate Ferlic (Fieldstone Analytics, LLC, Austin, TX, USA) Preprint ¡ Zenodo DOI: 10.5281/zenodo.22116173 ¡ CC-BY 4.0 ¡ Community: spiral-domain-encoder-campaign Abstract A companion study established that a fixed class-discriminant encoder â which reduces a window of a multivariate stream to a single 8-bit token â is a near-lossless detector of market stress across asset classes, but a taxed classifier and a null forecaster. This study asks, under strict pre-registration, whether that detector becomes a *universal, deployable primitive*, and finds that it does, along five axes, with an honest boundary preserved throughout. (1) The codebook is universal: fit on equities alone, it detects stress zero-shot across cryptocurrency, commodities, emerging-market funds, credit, and foreign exchange at a median AUC of 0.84, within 0.01 of a bespoke per-asset codebook, because the calm-to-stress discriminant direction is nearly identical across asset classes (median cross-asset cosine 0.94). (2) The token detects distributional regime-change that volatility structurally misses â the one place it beats volatility â and is an orthogonal channel that improves a volatility-only stack by +0.18 AUC on a mixed target. (3) It generalizes to credit and rates (pooled AUC 0.91) and to currency crosses (0.80), including markets with no options index. (4) It is cheap at scale and low-data: a genuine one-byte-on-the-wire firm-wide dashboard detects systemic stress at 0.92 at 200Ă compression â a 42-instrument universe fits in ~10 kilobytes per year â and a new deployment is usable after ~3 months of calm history. (5) The same one-byte token supports privacy-preserving federation: institutions each sharing one token per day match full-data systemic-detection utility while their individual positions are unrecoverable even to a non-linear, colluding, market-informed adversary (incremental reconstruction R² â 0.01). All headline results survive a permutation-placebo leakage test, moving-block bootstrap confidence intervals, and robustness to split, seed, stress-label, and a strict recent holdout. Every negative is reported, and two honest scope refinements are disclosed: a formal differential-privacy guarantee is event-level, not stream-level, and portfolio membership is coarsely inferable while positions are not. Highlights ¡ Universal codebook â one equity-fit codebook detects stress zero-shot across every asset class (median 0.84, transfer loss ~0.01, 22/23 targets); mechanism = a shared cross-asset stress direction (cosine 0.94). ¡ A capability volatility lacks â the token detects distributional (shape) regime-change where trailing volatility is at or below chance; an orthogonal channel that complements any GARCH/VIX stack. ¡ Coverage â credit/rates (pooled 0.91) and FX/carry white-space (0.80), options-free, at one byte per instrument. ¡ Compression-at-scale â a 1-byte-on-the-wire firm-wide dashboard at AUC 0.92, 200Ă compression, ~10 KB/instrument-year; detection-sufficient encoder â 590 multiply-accumulates and < 3 KB (microcontroller-class). ¡ Low-data cold-start â usable after ~3 months of calm data; saturates within ~2 years; train on a trailing window. ¡ Privacy-preserving federation â full-data-equivalent systemic detection with individual positions non-invertible even under a strong adversary; optional event-level differential privacy; manipulation-robust median aggregation; per-channel attribution; a yield-curve-shape token. ¡ Information-theoretic characterization â detection is a ~1-bit decision while classification is high-bit; competitive with principled two-sample/OOD tests at O(1) online cost. ¡ Honest negatives and hardening â network topology, temporal-token, cross-sectional ranking, and tail-edge results are all reported null; every headline result is leakage-tested (permutation placebo â chance) and confidence-bounded. What this record contains ¡ Manuscript_Paper40.pdf â the manuscript (single-column preprint). ¡ PAPER_40_ZENODO_ARCHIVE.zip â the reproducibility archive: seven pre-registration scopes (frozen outcome bands) covering studies FIN-15 ⌠FIN-45; the deterministic per-study runners and the shared frozen encoder/loader and federation modules; the per-study JSON result summaries behind every figure and table value; the figure-rebuild script; and the eight figures. All paths and identifiers are scrubbed and leak-scanned per the campaign deposit discipline; no raw market data is redistributed. Cite as R. J. Ferlic and K. K. Ferlic, "A universal, options-free, one-byte market-state primitive: cross-asset transfer, distributional-novelty detection, and privacy-preserving federation," Zenodo, 2026, doi: 10.5281/zenodo.22116173. License and patent notice Released under the Creative Commons Attribution 4.0 International License (CC-BY 4.0). Consistent with that license, no patent, patent-application, or other intellectual-property right of the authors is licensed, waived, granted, or otherwise conveyed by this deposit; the methods described â including the single-token class-discriminant encoder, its multi-token product-quantization variant, its unsupervised nearest-centroid-distance monitoring mode, and the privacy-preserving multi-party aggregation of its tokens â are the subject of filed and pending U.S. patent applications. Licensing inquiries: randolphf@fieldstoneanalyticsllc.com. Companion deposits (spiral-domain-encoder-campaign) ¡ Single-token financial market-state monitor (companion study): doi:10.5281/zenodo.22101085 ¡ Single-token industrial sensor substrate: doi:10.5281/zenodo.20854722 ¡ Hardening and generality characterization: doi:10.5281/zenodo.20802759 ¡ Deterministic multi-token token ladder: doi:10.5281/zenodo.22003179 Keywords universal codebook; decision-preserving compression; distributional novelty; federated privacy; differential privacy; systemic risk; market-stress detection; class-discriminant codebook; cross-asset transfer; edge computing; anomaly detection; pre-registration; honest negatives; financial time series; regime detection; credit risk; yield curve; volatility regime; VIX; market surveillance
The rapid diffusion of crypto currency in Nigeria has attracted considerable attention from academics, practitioners, and policymakers. This study investigates the determinants of crypto-currency adoption, market growth, and price dynamics in Nigeria, with a particular focus on financial inclusion, regulatory environment, technological advancement, investor sentiment, and macroeconomic factors. The research objectives are (i) to assess the appeal and growth trajectory of crypto-currencies in Nigeria; (ii) to identify the risk factors that shape their evolution; and (iii) to derive policy-relevant insights for regulators and industry stakeholders. A quantitative approach was employed using quarterly data spanning 2012-2023 (N = 43). Five hypotheses were formulated and tested using a battery of time-series techniques: Granger-causality, unit-root tests, Johansen cointegration, and autoregressive distributed-lag (ARDL) modelling. The proxies for the independent variables were: number of crypto users, transaction volume, and number of exchanges (cryptocurrency adoption); number of regulatory approvals, regulatory clarity, and regulatory support (regulatory environment); internet penetration, mobile-phone adoption, and tech-startup count (technological advancement); social-media mentions, sentiment analysis, and investor-confidence index (investor sentiment); and GDP growth, inflation, and exchange rate (economic factors). Dependent variables included percentage of the population with financial-service access, number of bank accounts, mobile-money adoption (financial inclusion); market capitalization, trading volume, and new listings (crypto-market growth); standard deviation of price returns and frequency of price jumps (price volatility); and number of transactions and users (crypto demand). The empirical findings reveal a complex interplay among the variables. Granger-causality tests indicate bidirectional predictability between crypto currency adoption and financial inclusion, as well as unidirectional causality from regulatory environment, technological advancement, investor sentiment, and economic factors to their respective outcomes (p < 0.05). Unit-root tests confirm stationarity of all series (I(0)), justifying the use of cointegration analysis. Johansen tests detect at least one cointegrating vector for each hypothesis, suggesting long-run equilibria. ARDL models provide nuanced short-run dynamics: a 1 % improvement in regulatory quality raises market growth by 0.98 % (p < 0.001); technological advancement has a modest, borderline-significant short-run effect on adoption (p = 0.09); investor sentiment exhibits a contemporaneous calming effect on volatility followed by a lagged increase (p = 0.04); and economic factors display a near-unit elasticity (0.98, p < 0.001) with crypto demand in the short run but a negative long-run association, implying that sustained economic improvement may reduce cryptoâs appeal. The study concludes that while regulatory clarity, technological infrastructure, and macroeconomic stability are pivotal in shaping the short-run trajectory of the Nigerian crypto market, their long-run impact can be ambivalent. Investor sentiment emerges as a significant driver of price volatility, underscoring the role of behavioural factors in this emerging asset class. The findings underscore the need for a balanced regulatory framework that encourages innovation while safeguarding financial stability, alongside targeted investments in digital infrastructure and financial-literacy programmes.
Liu Jin, Yahya M.H., Saidatunur Fauzi Saidin, Li Lu
Abstract Multivariate cryptocurrency forecasting is challenging because market series exhibit non-stationarity, cross-variable dependence, heterogeneous temporal scales, and abrupt short-term fluctuations. Although Transformer-based forecasting models can capture long-range temporal relationships, directly modeling raw high-frequency sequences may obscure dominant periodic structures and increase computational cost. This study proposes a frequency-guided multi-scale decomposition and patch Transformer, termed FMDP-Transformer, for multivariate cryptocurrency time-series forecasting. First, a frequency-guided multi-scale representation module estimates dominant temporal periods from the Fourier amplitude spectrum and constructs scale-specific representations through period-dependent average pooling. This module is designed to extract multi-scale periodic information and attenuate short-term disturbances rather than to perform explicit anomaly detection. Second, the resulting representation is decomposed into trend and residual components. A lightweight linear projection is used for parsimonious trend extrapolation, while the residual component is divided into overlapping patches and processed by a Transformer encoder to model local and long-range temporal dependencies. The forecasts produced by the two branches are subsequently combined. Experiments on Bitcoin, Dogecoin, and Binance Coin data derived from the G-Research Crypto Forecasting dataset evaluate the model under multiple forecasting horizons. Comparisons with recurrent, decomposition-based, patch-based, inverted-Transformer, and multi-scale forecasting models, together with component ablations and computational-complexity analysis, are used to assess its effectiveness. The results indicate that frequency-guided multi-scale representation, decomposition, and patch tokenization provide complementary benefits for multivariate cryptocurrency forecasting. Nevertheless, the proposed frequency-guided smoothing operation does not explicitly identify statistical anomalies, and abrupt market movements may contain predictive information rather than noise.
Abstract The emergence of cryptocurrencies has presented investors with novel portfolio diversification opportunities. This study investigates the interplay between precious metals and cryptocurrencies, examining their potential for enhancing portfolio returns and mitigating risk. Using daily closing prices from August 2017 to November 2022, we employ an autoregressive distributed lag (ARDL) approach to analyze comovement and causality between these asset classes. Our findings reveal a short-run linkage between Bitcoin, precious metals, and other cryptocurrencies but no long-run cointegration. Notably, gold prices unidirectionally influence cryptocurrency prices, a relationship not observed with other assets. This absence of long-term comovement suggests that precious metal investors can leverage modern portfolio theory to diversify cryptocurrency volatility risk. These results offer valuable insights for investors seeking to optimize portfolios by strategically incorporating cryptocurrencies for improved risk-adjusted returns.
This study proposes a Bitcoin price prediction model utilizing Long Short-Term Memory (LSTM) networks, integrating technical indicators, Reddit sentiment indicators, and on-chain data. The cryptocurrency market, particularly Bitcoin, exhibits extreme price volatility, despite its high profit potential. This volatility stems from a combination of macroeconomic factors, market participant sentiment, and fluctuations in supply and demand within the blockchain ecosystem. Existing literature typically examines only one or two types of dataâwhether technical, sentiment, or on-chainâwithout systematically verifying the complementary effects of integrating these heterogeneous data sources on predictive performance. To address this gap, this study quantitatively analyzes the contribution of each data type by constructing four experimental settings combining technical indicators, Reddit sentiment, and on-chain data based on Bitcoin price movements. Moreover, the proposed LSTM model is benchmarked against Linear Regression (LR), Random Forest (RF), and XGBoost (XGB) under identical experimental conditions. Evaluation metrics, including RMSE, MAE, and MAPE, indicate that while the LSTM model demonstrates superior predictive performance using only technical indicators, the inclusion of both Reddit sentiment and on-chain indicators results in a slight increase in error metrics. Nevertheless, this study emphasizes the potential of capturing the multifaceted characteristics of the market, which are often overlooked with single price-based indicators. It provides an empirical foundation supporting the effectiveness of heterogeneous data integration in future cryptocurrency price prediction research.
Zaid Tahat, Ahmad Alomari, Ibrahim Al-Radaideh, Adham Taher Alessa ¡ 7 authors
This study examines the mediating role of investor trust in the relationship between perceived blockchain integration and perceived stock market efficiency within the Amman Stock Exchange (ASE). The Amman Stock Exchange (ASE), established in 1999, is the sole securities exchange in Jordan and one of the leading emerging markets in the Middle East and North Africa (MENA) region. Drawing on technology acceptance theory, trust theory, and market efficiency theory, the research develops and tests a dual-pathway model wherein perceived blockchain integration relates to perceived market efficiency both directly and indirectly through investor trust. Using structural equation modeling with data collected from 400 market participants, the findings reveal that perceived blockchain integration is significantly and positively associated with investor trust (β = 0.849, p < 0.001) and with perceived stock market efficiency (β = 0.448, p < 0.001). Importantly, investor trust partially mediates this relationship (β = 0.380, p < 0.001), confirming the dual-pathway impact. Among blockchain dimensions, security demonstrates the strongest effect on both investor trust and market efficiency. The study contributes to the emerging literature on blockchain in financial markets by empirically validating the psychological mechanisms through which technological innovations translate into more favorable perceptions of market functioning. For market regulators and exchange administrators, the findings suggest that comprehensive blockchain implementation strategies should address both technological deployment and trust-building initiatives to strengthen favorable investor perceptions of market efficiency in emerging markets.
Type of the article: Research ArticleAbstractThe blockchain financial system allows users to send money fast without any border restrictions. However, the same structure of the blockchain may be used as a means of laundering money. This paper assesses the relationship between the complexity of transaction networks and the likelihood of their illicit nature within the public Elliptic Bitcoin benchmark and examines whether anomaly detection using machine learning helps to interpret risks from an AML/CFT perspective. This empirical analysis assumes that Elliptic provides an anonymized transaction network in which nodes correspond to Bitcoin transactions, edges reflect directed transactions, and anonymized features facilitate licit/illicit classification of transactions. Furthermore, the dataset is not considered evidence of sender wallet addresses, receiver wallet addresses, transaction amount, timestamp, ownership of exchanges, user geography, and national AML/CFT effectiveness. Based on the labelled analytical dataset presented in the uploaded workbook (46,564 observations, including 42,019 licit transactions and 4,545 illicit transactions), a logit model found a significant positive correlation between illicit transactions and degree centrality (beta = 1.870, p < 0.001), clustering coefficient (beta = 0.940, p < 0.001), and flow entropy (beta = 0.680, p < 0.001). Isolation Forest and Autoencoder reached AUCs of 0.866 and 0.841, respectively. In turn, the coefficient measuring a countryâs regulatory capacity and its interaction term are not included in the estimation because there is no country-window marginal effect. Therefore, this paper does not test for the impact of regulatory capacity of the USA, Singapore, and UAE on transaction classification.
Blockchain and distributed ledger technology (DLT) have been proposed for humanitarian operations because their shared-ledger characteristics may support transparency, traceability, accountability and coordination across organisations. This systematic evidence review synthesises research on operational benefits, adoption barriers, implementation conditions and evidence gaps in humanitarian supply chains. The evidence includes systematic reviews, empirical pilot research, expert-based barrier analysis, case-based design research and implementation-framework studies. Across the literature, the most consistently reported potential benefits are visibility, traceability, transparency, auditability, trust and inter-organisational information sharing. The empirical base is smaller than the conceptual literature, and barriers include regulatory uncertainty, skills and training, sustainability costs, privacy, infrastructure, scalability, interoperability and governance. The review proposes an eight-stage implementation pathway centred on problem diagnosis, technology justification, governance, privacy-aware architecture, piloting, capacity building, evaluation and controlled scaling. The framework is a synthesis proposed by the author from the reviewed evidence, rather than a tested causal model. The review concludes that blockchain should be selected conditionally, where multiple independent actors need a shared auditable record and where the expected coordination value justifies the additional technological and governance complexity.
The rapid digitalisation of healthcare has accelerated the adoption of telemedicine, Electronic Health Records (EHRs), and the Internet of Medical Things (IoMT), transforming healthcare delivery into a highly interconnected and patient-centric ecosystem. In response to growing concerns about data security, privacy, and interoperability, blockchain technology has emerged as a promising solution for its decentralization, immutability, auditability, and secure access control. However, many existing blockchain infrastructures rely on classical cryptographic primitives, including RSA- or elliptic-curve-based public-key mechanisms and cryptographic hash functions such as SHA-256, whose relevant security properties may be affected by sufficiently powerful quantum attacks. This review investigates the convergence of blockchain and quantum technologies to address emerging security threats in e-health systems. A structured literature review was conducted in accordance with the PRISMA 2020 guidelines using the IEEE Xplore, PubMed, ACM Digital Library, Google Scholar, and Crossref databases, covering studies published between January 2018 and June 2025. Following a systematic screening and eligibility-verification process, 57 relevant studies were selected and analyzed. The review evaluates quantum-resilient security mechanisms, including Quantum Key Distribution (QKD), Quantum Random Number Generation (QRNG), and NIST-standardized Post-Quantum Cryptography (PQC) algorithms specified in FIPS 203, FIPS 204, and FIPS 205. Based on the identified research gaps in the state of the art, this study also proposes a novel four-layer Quantum-Blockchain Security Architecture (QBSA) designed for secure healthcare environments. The analysis further reveals significant challenges associated with lightweight PQC deployment for IoMT devices, interoperability standardization, quantum hardware limitations, and regulatory compliance in cross-institutional healthcare systems. The findings highlight the necessity of integrating quantum-resilient cryptographic frameworks with blockchain infrastructures to support the development of secure, scalable, and patient-centric next-generation e-health ecosystems.
Open access
Blockchain Technology Applications and Security
Cryptography and Data Security
Physical Unclonable Functions (PUFs) and Hardware Security
Ida Bagus Mahayana Pidada, Made Aditya Pramana Putra
Blockchain-based smart contracts automate the execution of transactions through computer code, but automation alone does not determine whether the resulting arrangement is a legally binding contract. This article examines the legal status and enforceability of blockchain-based smart contracts as electronic agreements under Indonesian law and evaluates how the validity requirements in Article 1320 of the Indonesian Civil Code operate in a pseudonymous, automated, and relatively immutable technological environment. Using doctrinal legal research, the study integrates the Indonesian Civil Code, the Electronic Information and Transactions Law (EIT Law) as amended by Law No. 1 of 2024, Government Regulation No. 71 of 2019, and current financial-sector regulation, supported by comparative analysis of the UNCITRAL Model Law on Automated Contracting, the UK Law Commission's work on smart legal contracts, and relevant European regulatory design. The analysis finds that Indonesian law can recognize a blockchain-based arrangement as an electronic contract when an identifiable agreement is formed through an electronic system and the substantive validity requirements are satisfied. Government Regulation No. 71 of 2019 is particularly significant because Article 46 reproduces the core elements of contractual validity for electronic contracts, while Article 47 requires party identity, transaction terms, cancellation procedures, and choice-of-law provisions. The principal legal uncertainty therefore lies less in basic contractual validity than in attribution of consent and capacity, code-text inconsistency, automated-agent responsibility, coding or oracle errors, reversibility of remedies, and cross-border enforcement. The article recommends targeted, technology-neutral rules and contractual design safeguards rather than assuming that either code alone or a comprehensive blockchain-specific statute can solve these problems.
Marco Scarpa, Mohammad Sadeghzadeh, Saeed Javanmardi, Bahareh Pahlevanzadeh ¡ 5 authors
Blockchain provides secure and decentralized data storage. Normal blockchains permanently store data. Mutable blockchains allow users to change data, but this reduces tamper resistance. This paper tests both methods in PaB-PIF, a hybrid architecture for IoT-Fog networks. Our design uses an immutable mainchain in the cloud layer and mutable sidechains in the fog layer. We analyze throughput, latency, and tamper resistance using math models and simulations. Results show that blockchain greatly improves network security. Without blockchain, the network has zero tamper resistance. The mutable blockchain in the fog layer has a tamper resistance of 0.58. The immutable blockchain in the cloud layer reaches 0.99. However, this extra security increases latency and reduces throughput. The mutable blockchain has lower latency, so it is a good fit for the fog layer. The immutable blockchain provides maximum security, which is best for the cloud layer. This trade-off works well for IoT systems like the Internet of Vehicles, where data integrity and legal rules are essential. We also compare PaB-PIF with an IoT-Fog network that has no blockchain.
The integration of artificial intelligence (AI), the Internet of Things (IoT), and blockchain may provide a viable approach to tackle persistent operational and informational challenges in cleaner production. This conceptual review synthesizes existing literature and presents an integrated AI-IoT-blockchain framework mapped across the four sequential stages of cleaner production: source reduction, process control, end-of-pipe treatment and recycling, and full-chain traceability. The literature indicates that IoT enables real-time sensing, AI drives predictive and prescriptive analytics, and blockchain ensures tamper-proof record-keeping and stakeholder trust. Together, these technologies may help address long-standing barriers including fragmented data, delayed responses, and a lack of verifiability. Despite challenges such as high costs, technical fragmentation, and organizational resistance, several emerging strategies have been proposed in the literature to address these challenges. These include modular deployment, federated learning, permissioned blockchains, and regulatory sandboxes. The frameworkâs underlying architecture appears transferable across sectors, subject to industry-specific adaptation, supporting sustainable manufacturing, the circular economy, and low-carbon development.
Blockchain technology (BCT) is often discussed as digital support for timber traceability in the context of the EU Deforestation-Free Products Regulation (EUDR), which requires operators and traders to demonstrate legality, deforestation-free production, geolocation at the forest parcel level, and verifiable supply chain documentation from 30 December 2026, with an extended timeline for micro, small, and medium-sized enterprises (SMEs) until 30 June 2027. This study assesses where BCT can realistically add value in timber supply chains under operational forestry conditions and identifies the necessary technical, organizational, and legal prerequisites. A combination of a targeted literature review and empirical input from experts in the forestry and timber industry, including guided expert web-conferencing interviews (n = 41), an online survey (n = 69 completed responses), and a transdisciplinary workshop (n = 18) was utilized to obtain a comprehensive overview of industry perspectives. Qualitative data from interviews and workshop sessions were analyzed using structured qualitative content analysis, while survey data were evaluated using descriptive statistics. The expected benefits are associated with the introduction of tamper-proof timber harvesting practices and cross-organizational verification mechanisms. To address the discrepancy between biological uncertainty and digital rigidity, the study proposes a dynamic allocation model adapted from the energy sector that distinguishes between fixed and variable wood capacities to automate logistical planning via smart contracts. However, respondents emphasize that practical obstacles, such as limited digital maturity in forestry, fragmented data infrastructures across the supply chain, and unresolved issues of data sovereignty hinder the implementation of BCT. BCT alone is unable to resolve the problem of weak physical-digital identity continuity, a phenomenon widely known as the oracle problem; however, coupling the ledger with physical or biological anchors (e.g., photo-optical, automated inkjet marking identification) can re-establish this physicalâdigital continuity and thereby resolve the oracle problem. The results demonstrate that blockchain acts most plausibly as a supporting component within hybrid traceability architectures that prioritize event-based authentication, off-chain data processing where appropriate, and integration with existing certification systems. The study highlights the necessity of defining distinct organizational roles and responsibilities while integrating user-centric digital solutions tailored specifically to small and medium-sized enterprises (SMEs).