The rapid progress of large-scale models, including foundational and generative, brings to the forefront the tension between data-driven innovation and core privacy concerns. Such contracts as the GDPR and the undue privacy threats of data aggregation make centralized training approaches less desirable. To analyze the data’s distributed characteristics and their application to FLO, we investigate the role of federation analytics in a plausible paradigm that shunts data. In this paper, we present a new federated learning (FL) framework enhanced with cutting-edge privacy technologies (PET) such as Differential privacy for user-level formal guarantees of confidentiality, and strengthened secure Multi-Party Computation (SMPC), which guards the model updates. This paper studies more recent approaches to resolving the principal challenges of FL: statistical heterogeneity, communication bottlenecks, and vulnerability to adversarial attacks. We greatly appreciate what this new method portends, especially for training large language models (LLMs) and the more delicate areas of healthcare and finance. By evaluating certain existing limitations, such as the complexities of federated fine- tuning and model fairness, it is clear that an architecture with exemplary performance in FL serves as a model for scalable, secure, and privacy cop.
Aldi Bastiatul Fawait, Muh Jamil, Sitti Rahmah, Sugiarto Sugiarto
Perkembangan teknologi blockchain dalam beberapa tahun terakhir memberikan dampak besar terhadap sistem keuangan global, salah satunya melalui Ethereum (ETH) yang berfungsi sebagai aset kripto sekaligus fondasi ekosistem smart contract. Namun, volatilitas tinggi harga ETH membuat metode prediksi tradisional sulit menangkap pola nonlinier yang kompleks. Penelitian ini menerapkan metode Long Short-Term Memory (LSTM) untuk memprediksi harga ETH menggunakan data time-series dari investing.com periode 1 Januari 2021 hingga 21 Agustus 2025. Model LSTM dengan tiga lapisan menghasilkan performa baik dengan MAE 0,0387 dan R² 0,9741 pada data latih, serta MAE 22,59% dan R² 80,55% pada data uji. Hasil ini membuktikan bahwa LSTM efektif dalam mempelajari pola fluktuasi harga ETH meskipun akurasi pada data baru masih dapat ditingkatkan. Kontribusi penelitian ini adalah memperkuat literatur terkait prediksi kripto berbasis data jangka panjang sekaligus memberikan manfaat praktis bagi investor dan regulator dalam memahami dinamika volatilitas ETH.
Mr. Aditya S. G., Mr. Ram Anil Ainkar, Prof. Ms. Pranalini Joshi
The current Know Your Customer (KYC) ecosystem is largely built on centralized systems, which are vulnerable to data breaches, incur high operational costs, and often require customers to repeat verification steps unnecessarily [1], [2]. Such centralized designs concentrate sensitive data in single repositories, creating “honeypots” that conflict with modern data privacy standards like the General Data Protection Regulation (GDPR) [3], [4]. At the same time, the transparent nature of public Distributed Ledger Technology(DLT) presents challenges for maintaining privacy in financial transactions, giving rise to what is often called the “Blockchain-PrivacyParadox” [5]. This survey explores cutting-edge DLT-based solutions that integrate Self-Sovereign Identity (SSI) and Zero-KnowledgeProof (ZKP) techniques. Key challenges in current approaches include scalability limitations in certain permissioned blockchains [6],inadequate mechanisms to fully support GDPR’s Right to Erasure [3], [4], [7], and the absence of reliable protocols to ensure legal access for Anti-Money Laundering (AML) compliance when users are uncooperative [8], [9].
Narendar Kumar, Surendar Kumar, Abdul Waqar, Clavincy Francis Yohanes Ngantung
This research article provides the design of an in-person and remote voting system, while at the same time ensuring the privacy of users that would guarantee openness, transparency, and at the same time fraud-free results. The aim is to solve various common problems associated with most conventional elections including fraud, vote manipulation, through adaptation of the usage of a safe, highly transparent decentralized logical Hyperledger Fabric-based system provided by blockchain implementation. The methodology in this article is to be implemented for the sheer reason of urgency needed in making a more secure and transparent system for voting, considering even the rising frauds in elections. The addition of Zero Knowledge Proof (ZKP) guarantees that votes are confident and correct, yet anonymous between a voter and their vote. Biometric identification makes the system resistant to double spending. This incorporation of technologies ensures there is privacy and immutability against the double transactions, which, in turn, would be put in place as foundation for the future to be provided wherein every process in an election becomes safe and transparent. Innovation via creating a voting system to be trusted to meet today's demands and set standards for future electoral processes.
Abstract: The transition from centralized digital ecosystems to decentralized, trust - driven architectures represents a defining paradigm shift in Customer Experience (CX). This paper presents a strategic blueprint for leveraging block chain technologies to build secure, transparent, and interoperable customer - centric environments between 2025 and 2030. Through a comprehensive review of market forecasts, enterprise case studies, and emerging regulatory frameworks, the study demonstrates how decentralized identity (DID), verifiable credentials, and tokenized loyalty systems fundamentally reshape customer engagement, ownership of personal data, and trust models. Findings indicate that block chain adoption empowers customers with self - sovereign identity control, enhances privacy compliance, and delivers measurable efficiency gains in verification, loyalty management, and supply - chain transparency. Case evidence from leading enterprises — including JPMorgan, AXA, Santander, and Accenture — highlights significant improvements in transaction speed, operational costs, and customer engagement. Despite challenges such as legacy system integration and GDPR - related constraints, hybrid architectures, Layer - Two scalability, and permissioned block chain environments provide viable adoption pathways. This paper concludes that block chain is not a supplementary technology for CX, but a foundational enabler of decentralized trust, competitive differentiation, and customer - driven digital ecosystems. Keywords: Block chain; Customer Experience (CX), Decentralized Identity (DID), Verifiable Credentials, Tokenized Loyalty Programs, Digital Trust, Self - Sovereign Identity, Smart Contracts, Hybrid Data Architecture, GDPR Compliance, Enterprise Digital Transformation, Web3 Customer Strategy
Climate governance is entering a period of turbulence, with policy reversals in some democracies and rapid expansions elsewhere. This paper compares how centralized, decentralized (federal), and polycentric/hybrid governance designs shape mitigation and adaptation outcomes. Using a qualitative comparative approach across China, the United States, Canada, Türkiye, Norway, and Saudi Arabia, assessing policy ambition, legal instruments, implementation capacity, subnational authority, stakeholder participation, finance mobilization, and equity considerations. A qualitative comparative approach is applied across six country cases - China, the United States, Canada, Türkiye, Norway, and Saudi Arabia - evaluating policy ambition, legal instruments, implementation capacity, subnational authority, stakeholder participation, finance mobilization, and equity considerations. Insights are then extended to the Central Asian context, where climate governance remains predominantly centralized, shaped by Soviet-era institutional legacies, uneven local capacity, and constrained civic participation. The analysis demonstrates that no model is universally superior; the most effective arrangements combine top-down coherence with bottom-up experimentation and social legitimacy. Norway’s polycentric governance model and Türkiye’s hybrid approach illustrate how localized climate planning can be integrated within broader national frameworks. For Central Asia, pragmatic hybrid pathways are recommended that align national targets and financing with empowered regional pilots, transparent monitoring, and inclusive engagement. These context-sensitive combinations offer the best prospects for durable emissions reductions, climate resilience, and just transition outcomes in the region.
Stanislav I. Trofimov, Leonid Voskov, Mikhail Komarov
In the face of growing competition in the transportation market, companies are looking for new ways to improve operational efficiency and reduce fleet maintenance costs. This article presents an innovative vehicle technical condition management model that describes a mechanism for assessing the condition of vehicles using distributed ledger technology (DLT) and smart contracts. An information system for automating maintenance is proposed that can perform monitoring functions and initiate vehicle maintenance without human intervention by automatically registering operation and maintenance events, as well as using smart contracts to launch predefined actions. This level of automation allows timely prevention of unplanned breakdowns, which directly contributes to an increase in the service life of vehicles. The proposed solution allows transport companies to automate decision-making processes on maintenance, reduce transport downtime and optimize operating costs. The model ensures transparency of vehicle operation data, increases trust in information and shortens the decision-making chain. The solution is of particular value for public transport companies, where uninterrupted transportation and passenger safety are critically important.
Open access
Transportation Systems and Logistics
Advanced Research in Systems and Signal Processing
Cryptocurrency is a highly volatile digital asset, necessitating accurate and adaptive forecasting methods. This study implements a Long Short-Term Memory (LSTM) model to predict the daily closing prices of two leading cryptocurrencies Bitcoin (BTC) and Ethereum (ETH) using historical data from Yahoo Finance and Binance. To enhance data richness and model robustness, datasets from both sources were vertically merged. The methodological framework included data preprocessing, Min–Max normalization, formation of 24-day sliding input windows, and training across three data split ratios (70:30, 80:20, and 90:10). Model performance was evaluated using the Root Mean Squared Error (RMSE). Results indicate that the LSTM model achieved high prediction accuracy, with the lowest RMSE values of 0.0137 for BTC and 0.0152 for ETH using the combined dataset with a 90:10 split. Beyond modeling, a web-based application was developed using Streamlit, enabling users to perform real-time predictions and export results. This study contributes to the field of cryptocurrency forecasting by demonstrating that multi-source data integration significantly improves predictive accuracy and model generalization. The proposed framework offers both theoretical insights and practical tools for researchers and investors in financial technology.
We present a graphics processing unit (GPU)-accelerated Proof-of-Work (PoW) blockchain design tailored for secure healthcare data management. Our Compute Unified Device Architecture (CUDA)-optimized PoW achieves throughput improvements of approximately 5× to 100× and reduces block-formation latency compared to Central Processing Unit (CPU) mining, making blockchain practical for high-volume health records. We benchmark against standard platforms-Bitcoin, known for its robust security but slow block times; Ethereum (legacy PoW), widely adopted yet less efficient; and Hyperledger Fabric, a permissioned enterprise framework-to quantify performance gains. Empirical tests show GPU-Advanced Encryption Standard in Counter Mode (AES-CTR) processes large health-record payloads in under one second, while our PoW mining throughput improves by approximately 5×, to 100× relative to unaccelerated baselines. We also evaluate end-to-end encryption latency and discuss privacy trade-offs, including that lightweight Advanced Encryption Standard (AES) yields minimal delay, whereas fully homomorphic methods, although privacy-preserving, remain impractical for real-time permissionless blockchains and are not included in our design. We explicitly address regulatory compliance: personal health data are stored off-chain (e.g., Interplanetary File System [IPFS]), preserving the "right to erasure" via deletion of off-chain records, and we implement strict access controls to meet Health Insurance Portability and Accountability Act (HIPAA) security rules. The design includes validator selection rules that limit Sybil attacks by requiring costly work (or stake) and supports post-quantum cryptographic agility (e.g., Falcon signatures). We define our research question ("Can CUDA-accelerated PoW enable a high-performance yet compliant health data blockchain?") and hypothesize that GPU parallelism will yield substantial increases in speed. Results confirm our hypothesis: throughput and latency are significantly improved while preserving data privacy and compliance. This work makes a comprehensive contribution by detailing implementation methods, performance benchmarking, and analysis of security and legal requirements in a unified blockchain framework for healthcare.
Objective: This research aims to develop a comprehensive framework to identify and prevent money laundering in Decentralized Finance (DeFi) by leveraging big data analytics, integrating advanced machine learning algorithms, and network analysis techniques to address the challenges of pseudonymity and decentralization inherent to this ecosystem. Research Design & Methods: This research utilizes a mixed method approach with machine learning analysis based on Elliptic Dataset and qualitative policy study, applying graph models and classification algorithms to detect illegal transactions with precision in the context of imbalanced data. Findings: The results show that the MLP and GCN models achieve high accuracy (98% and 97.3%) and excellent recall (99.5% and 99.4%) on the Elliptic Dataset, significantly outperforming traditional methods. Exploratory data analysis and graph visualization confirmed that illegal transactions form denser clusters and more complex paths, indicating a layering pattern. Implications and Recommendations: Theoretically, this research extends the application of big data and graph theory to new financial systems, providing a blueprint for future RegTech and FinTech research. Practically, the framework offers tangible tools for regulators, law enforcement, and DeFi platforms to enhance AML capabilities, supporting the development of real-time monitoring tools and risk assessment models. Contribution and Value Added: The main contribution of this research is the development of a robust and adaptive big data analytics-based AML framework, which effectively addresses the unique challenges of DeFi.
Abstract The global financial markets are being changed by DeFi's ability to remove central actors to facilitate peer-to-peer transactions. DeFi promotes efficiency, globalization, and economic inclusion, and at the same time, it has raised tax compliance. This study attempts to bridge the gaps by analyzing available scholarly and policy-oriented research, along with recent regulatory initiatives. The study concludes that the tax compliance challenges posed by DeFi's Decentralization, Shrouded Identity, and Composability Features are serious and can overcome the traditional tax reporting mechanisms. The study also suggests the broad directions of gaps in the literature to be addressed in policy-driven and empirical studies in the future. Keywords: DeFi, Blockchain, Tax Compliance, Fintech
Amal Yousseef, Shalaka Satam, Banafsheh Saber Latibari, Mai Abdel-Malek · 6 authors
Autonomous vehicles (AVs) rely on pervasive connectivity to enable cooperative and safety-critical applications, but this connectivity also exposes them to a wide range of cybersecurity threats. Existing perimeter-based security and centralized identity management approaches are inadequate for highly dynamic V2X environments, as they depend on implicit trust and suffer from scalability and single-point-of-failure limitations. This paper proposes D-IM, a Zero Trust-based decentralized identity management and authentication framework for secure V2X communication. D-IM integrates continuous verification with a permissioned blockchain to eliminate centralized trust assumptions and enforce explicit, verifiable identity relationships among vehicles and infrastructure. The framework is designed around clear Zero Trust-aligned goals, including mutual authentication, decentralization, privacy protection, non-repudiation, and traceability, and addresses a comprehensive attacker model covering identity, data integrity, collusion, availability, and accountability threats. We present the D-IM system architecture and identification and authorization protocol, and validate its security properties through both qualitative analysis and a formal BAN logic-based verification. Simulation results in urban and highway scenarios using DSRC and C-V2X demonstrate that D-IM introduces limited overhead while preserving network performance, supporting its practicality for real-world AV deployments.
Collaborative and distributed learning techniques, such as Federated Learning (FL) and Split Learning (SL), hold significant promise for leveraging sensitive data in privacy-critical domains. However, FL and SL suffer from key limitations -- FL imposes substantial computational demands on clients, while SL leads to prolonged training times. To overcome these challenges, SplitFed Learning (SFL) was introduced as a hybrid approach that combines the strengths of FL and SL. Despite its advantages, SFL inherits scalability, performance, and security issues from SL. In this paper, we propose two novel frameworks: Sharded SplitFed Learning (SSFL) and Blockchain-enabled SplitFed Learning (BSFL). SSFL addresses the scalability and performance constraints of SFL by distributing the workload and communication overhead of the SL server across multiple parallel shards. Building upon SSFL, BSFL replaces the centralized server with a blockchain-based architecture that employs a committee-driven consensus mechanism to enhance fairness and security. BSFL incorporates an evaluation mechanism to exclude poisoned or tampered model updates, thereby mitigating data poisoning and model integrity attacks. Experimental evaluations against baseline SL and SFL approaches show that SSFL improves performance and scalability by 31.2% and 85.2%, respectively. Furthermore, BSFL increases resilience to data poisoning attacks by 62.7% while maintaining superior performance under normal operating conditions. To the best of our knowledge, BSFL is the first blockchain-enabled framework to implement an end-to-end decentralized SplitFed Learning system.
Blockchain technology has spawned a vast ecosystem of digital currencies with Central Bank Digital Currencies (CBDCs) -- digital forms of fiat currency -- being one of them. An important feature of digital currencies is facilitating transactions without network connectivity, which can enhance the scalability of cryptocurrencies and the privacy of CBDC users. However, in the case of CBDCs, this characteristic also introduces new regulatory challenges, particularly when it comes to applying established Anti-Money Laundering and Countering the Financing of Terrorism (AML/CFT) frameworks. This paper introduces a prototype for offline digital currency payments, equally applicable to cryptocurrencies and CBDCs, that leverages Secure Elements and digital credentials to address the tension of offline payment support with regulatory compliance. Performance evaluation results suggest that the prototype can be flexibly adapted to different regulatory environments, with a transaction latency comparable to real-life commercial payment systems. Furthermore, we conceptualize how the integration of Zero-Knowledge Proofs into our design could accommodate various tiers of enhanced privacy protection.
Several recent proposals implicitly or explicitly suggest making use of randomized transaction ordering within a block to mitigate centralization effects and to improve fairness in the Ethereum ecosystem. However, transactions and blocks are subject to gas limits and protocol rules. In a randomized transaction order, the behavior of transactions may change depending on other transactions in the same block, leading to invalid blocks and varying gas consumptions. In this paper, we quantify and characterize protocol violations, execution errors and deviations in gas consumption of blocks and transactions to examine technical deployability. For that, we permute and execute the transactions of over 335,000 Ethereum Mainnet blocks multiple times. About 22% of block permutations are invalid due to protocol violations caused by privately mined transactions or blocks close to their gas limit. Also, almost all transactions which show execution errors under permutation but not in the original order are privately mined transactions. Only 6% of transactions show deviations in gas consumption and 98% of block permutations deviate at most 10% from their original gas consumption. From a technical perspective, these results suggest that randomized transaction ordering may be feasible if transaction selection is handled carefully.
We formulate the design of a threshold signature scheme as made possible on cryptocurrency protocols like Bitcoin. The funds are secured by an m-of-n threshold signature, where at least m signatures are needed to unlock the funds. A user designs this scheme knowing that a malicious attacker can also obtain the signatures with some probability. Higher thresholds offer more security, but also risk locking the user out of his own funds. The optimal threshold balances these twin effects. Interventions like increasing the security or usability of the signatures allow for higher thresholds. We model dynamic threshold signature schemes, where the probability of a user or attacker obtaining signatures decays with time. A dynamic threshold signature scheme is optimal, and increasing security or usability allows for higher thresholds and longer time locks.
Multicriteria decision-making methods exhibit critical dependence on the choice of normalization techniques, where different selections can alter 20-40% of the final rankings. Current practice is characterized by the ad-hoc selection of methods without systematic robustness evaluation. We present a framework that addresses this methodological sensitivity through automated exploration of the scaling transformation space. The implementation leverages the existing Scikit-Criteria infrastructure to automatically generate all possible methodological combinations and provide robust comparative analysis.We apply this approach in an evaluation dataset of cryptocurrencies with 6 methodological scenarios, showing a range of correlation between methods, explicitly quantifying the methodological sensitivity limits.
Bruno Mazorra, Burak Öz, Christoph Schlegel, Fei Wu
Ethereum's upcoming Glamsterdam upgrade introduces EIP-7732 enshrined Proposer--Builder Separation (ePBS), which improves the block production pipeline by addressing trust and scalability challenges. Yet it also creates a new liveness risk: builders gain a short-dated ``free'' option to prevent the execution payload they committed to from becoming canonical, without incurring an additional penalty. Exercising this option renders an empty block for the slot in question, thereby degrading network liveness. We present the first systematic study of the free option problem. Our theoretical results predict that option value and exercise probability grow with market volatility, the length of the option window, and the share of block value derived from external signals such as external market prices. The availability of a free option will lead to mispricing and LP losses. The problem would be exacerbated if Ethereum further scales and attracts more liquidity. Empirical estimates of values and exercise probabilities on historical blocks largely confirm our theoretical predictions. While the option is rarely profitable to exercise on average (0.82\% of blocks assuming an 8-second option time window), it becomes significant in volatile periods, reaching up to 6\% of blocks on high-volatility days -- precisely when users most require timely execution. Moreover, builders whose block value relies heavily on CEX-DEX arbitrage are more likely to exercise the option. We demonstrate that mitigation strategies -- shortening the option window or penalizing exercised options -- effectively reduce liveness risk.
Adam Zahir, Milan Groshev, Carlos J. Bernardos, Antonio de la Oliva
Edge computingbrings computation near end users, enabling the provisioning of novel use cases. To satisfy end-user requirements, the concept ofedge federationhas recently emerged as a key mechanism for dynamic resources and services sharing across edge systems managed by different administrative domains. However, existing federation solutions often rely on pre-established agreements and face significant limitations, including operational complexity, delays caused by manual operations, high overhead costs, and dependence on trusted third parties. In this context, Distributed Ledger Technologies (DLTs) such asblockchaincan create dynamic federation agreements that enable service providers to securely interact and share services without prior trust. This article first describes the problem of edge federation, using the standardized ETSImulti-access edge computing (MEC)framework as a reference architecture, and how it is being addressed. Then, it proposes a novel solution usingblockchainandsmart contractsto enable distributed MEC systems to dynamically negotiate and execute federation in a secure, automated, and scalable manner. We validate our framework’s feasibility through a performance evaluation using a private Ethereum blockchain, built on the open-source Hyperledger Besu platform. The testbed includes a large number of MEC systems and compares two blockchain consensus algorithms. Experimental results demonstrate that our solution automates the entire federation lifecycle-from negotiation to deployment–with a quantifiable overhead, achieving federation in approximately 18 seconds in a baseline scenario. The framework scales efficiently in concurrent request scenarios, where multiple MEC systems initiate federation requests simultaneously. This approach provides a promising direction for addressing the complexities of dynamic, multi-domain federations across the edge-to-cloud continuum.
While LLM-based specification generation is gaining traction, existing tools primarily focus on mainstream programming languages like C, Java, and even Solidity, leaving emerging and yet verification-oriented languages like Move underexplored. In this paper, we introduce MSG, an automated specification generation tool designed for Move smart contracts. MSG aims to highlight key insights that uniquely present when applying LLM-based specification generation to a new ecosystem. Specifically, MSG demonstrates that LLMs exhibit robust code comprehension and generation capabilities even for non-mainstream languages. MSG successfully generates verifiable specifications for 84% of tested Move functions and even identifies clauses previously overlooked by experts. Additionally, MSG shows that explicitly leveraging specification language features through an agentic, modular design improves specification quality substantially (generating 57% more verifiable clauses than conventional designs). Incorporating feedback from the verification toolchain further enhances the effectiveness of MSG, leading to a 30% increase in generated verifiable specifications.
Yury Yanovich, Victoria Kovalevskaya, Maksim Egorov, Elizaveta Smirnova · 9 authors
The Open Network (TON) blockchain employs an asynchronous execution model that introduces unique security challenges for smart contracts. A primary concern is race conditions arising from unpredictable message processing order. While previous work established vulnerability patterns through static analysis of audit reports, dynamic detection of temporal dependencies through systematic testing remains an open problem. This study proposes a dynamic evaluation methodology based on controlled message orchestration to systematically expose vulnerabilities in asynchronous smart contracts. By synthesizing precise message queue manipulation with differential state analysis and probabilistic permutation testing, we establish a framework (namely, BugMagnifier) for identifying execution flaws that static methods miss. Experimental evaluation demonstrates BugMagnifier's effectiveness through extensive parametric studies on purpose-built vulnerable contracts and five real-world vulnerability cases reproduced from recent security audits. Results reveal message ratio-dependent detection complexity that aligns with theoretical predictions. This quantitative model enables predictive vulnerability assessment while shifting discovery from manual expert analysis to automated evidence generation. By providing reproducible test scenarios for temporal vulnerabilities, BugMagnifier addresses a critical gap in the TON security tooling, offering practical support for safer smart contract development in asynchronous blockchain environments.
In recent years, decentralization and regional governance reforms have become a key priority for many countries to promote sustainable territorial development. In Morocco, the 2011 Constitution introduced advanced regionalization, granting regional governments greater autonomy and responsibilities in financing and managing local development. However, more than a decade later, questions remain about the financial performance of these regions and their capacity to mobilize and manage resources effectively. This paper aims to assess the financial performance of Moroccan regions through a case study approach. It examines regional revenue structures, expenditure patterns, fiscal autonomy, and investment capacity to evaluate the alignment between financial capabilities and the objectives of advanced regionalization. Relying on data from official sources, this study aims to provide an analytical overview of regional financial capabilities within the framework of advanced regionalization, contributes to the discussion on regional finance and governance in Morocco and formulates policy-oriented insights to support more effective and sustainable territorial development.
The article examines the legal mechanism for regulating the circulation of virtual assets in Ukraine and the regulatory and legal support for countering illegal activities with various types of cryptocurrencies. The provisions of the Law of Ukraine “On Virtual Assets”, amendments and additions to civil legislation in terms of introducing the concept of “digital thing” are analyzed. It is proven that the provisions of the European Regulation “Markets in Crypto-Assets” (“MiCA”) are essential for the legal regulation of the circulation of virtual assets and countering illegal activities with them. The classification of virtual assets contained in the European Regulation “MiCA” is disclosed in order to understand the essence of various types of cryptocurrencies. The peculiarities of the circulation of such crypto-assets as Bitcoin, Ethereum are disclosed and noted; the concepts of “blockchain”, “validator”, “service token”, “crypto-asset issuer”, etc. are investigated. The role of a number of state bodies in countering the illegal circulation of virtual assets in Ukraine is highlighted. It is argued that the coordination of analytical work and the detection of risky transactions is provided by the State Financial Monitoring Service of Ukraine. It is substantiated that the detection of criminal schemes and ensuring the prosecution of those guilty of offenses with virtual assets is entrusted to the National Police, the Security Service of Ukraine, the State Bureau of Investigation, the Bureau of Economic Security, and the Prosecutor’s Office. Such bodies as the National Bank of Ukraine, the National Securities and Stock Market Commission, and the Ministry of Digital Transformation of Ukraine form a regulatory framework that should prevent the use of crypto-assets for illegal purposes. It is established that countering the illegal circulation of virtual assets in Ukraine is carried out both through preventive measures, analytical work and improvement of the regulatory and legal framework, and through operational-search and criminal-law jurisdiction. This comprehensive model allows responding to the latest challenges, in particular, the use of decentralized finance, anonymous technologies, and cross-border schemes for the illegal circulation of virtual assets.
Abstract The rise of decentralized technologies introduces challenges in fairness, efficiency, and scalability within distributed ledger protocols. The Internet of Things Applications (IOTA) Tangle, a directed acyclic graph (DAG)-based structure, addresses these challenges by enabling scalable, feeless transactions for IoT applications. This study presents a novel Partially Observable Markov Decision Process (POMDP)-based Tip Selection Algorithm (TSA) to optimize fairness in the IOTA Tangle. The proposed TSA reduces orphaned transactions to as low as 0.003% and eliminates lazy tip selection under medium network loads. Extensive simulations demonstrate that the POMDP-based TSA confirms up to 107 transactions at optimal lambda values, outperforming existing algorithms like Weighted TSA by 328% in efficiency. This algorithm offers significant scalability, fairness, and adaptability, making it a robust solution for IoT-based decentralized applications. These findings advance DAG-based distributed ledger systems by addressing orphaned transactions and lazy behavior, ensuring secure and efficient operations under diverse network conditions.