Blockchain Papers

Mahdiyeh Barzegar, Hamid Reza Barzegar, Nabil El Ioini, Claus Pahl

No abstract is available for this record.

Caleb Price, Mckenzie Curtis

The Court of Justice of the European Union's landmark ruling in Skatteverket v. David Hedqvist (Case C-264/14) established that Bitcoin-to-fiat exchanges constitute VAT-exempt services under Article 135(1)(e) of the VAT Directive, on the basis that Bitcoin serves as a contractual means of payment analogous to legal tender. However, the rapid proliferation of Non-Fungible Tokens (NFTs)-which are increasingly characterized as electronically supplied services (ESS) rather than currency-has fragmented the uniform fiscal landscape envisioned by the ruling. This paper examines a critical, underexplored nexus: whether divergent VAT/GST treatments of NFTs across EU Member States create incentives for regulatory arbitrage that systematically increases the cyber-risk profile of decentralized exchanges (DEXs). As recent incidents involving Aerodrome Finance, CoW Swap, and dYdX demonstrate, the decentralized finance (DeFi) sector remains acutely vulnerable to Domain Name System (DNS) hijacking attacks that exploit the Web2 front-end infrastructure upon which DEXs rely. The paper argues that when tax uncertainty drives platforms toward jurisdictional optimization-often involving complex routing, cross-border operations, and reliance on lessregulated infrastructure-they inadvertently expand their attack surface for adversarial DNS tunneling. Employing a socio-technical analysis that bridges fiscal harmonization and network security, this study proposes a harmonized VAT framework for NFTs to reduce the compliance-security paradox that currently incentivizes risk-increasing operational behaviors. It further recommends the integration of decentralized naming systems, such as the Ethereum Name Service (ENS), as a countermeasure to DNS-based exploitation. The findings contribute to both tax policy discourse and cybersecurity scholarship by demonstrating that fiscal harmonization is not merely an economic concern but a foundational component of DeFi infrastructure resilience.

Kumar Rohan

This study explores trade surveillance models especially for wash trading and off-market pricing. These are few fraud behavior patterns that cause persistent threat to market integrity in traditional equity markets, cryptocurrency exchanges and decentralized non-fungible token (NFT) ecosystems. Although there is growing regulatory attention but there is less improvement in the current surveillance systems and remain fragmented and inconsistent in their ability to uncover manipulative behavior for different market structures. This paper summarizes findings electronic copy available at: https://ssrn.com from three peer-reviewed empirical studies to evaluate the effectiveness of current trade surveillance models and proposes an integrated detection framework that combines graph-based network analysis, econometric modeling, machine learning classifiers, and blockchain transparency tools. These reviewed literatures all together demonstrate that: (1) directed graph algorithms achieve more than 95% detection accuracy for collusive wash-trading patterns in traditional regulated markets; (2) around 70% of reported trading volume on certain cryptocurrency platforms is fabricated using coordinated self-transactions; and (3) AI-assisted blockchain analytics can identify wash-trading loops in decentralized NFT markets with more than 95% precision. Researching and studying these findings, this paper proposes a unified, AI driven surveillance architecture integrating real-time graph traversal, cross-exchange auditing, and blockchain forensic analysis. The framework designed to improve detection speed, reduce false positives, and support regulatory enforcement for both centralized and decentralized financial environments. Paper discusses the implications for regulatory policy, financial compliance infrastructure, and future surveillance system design.

Reem E. Mohamed, Mukhtar Hussain, Zahra Jadidi, Ernest Foo · 5 authors

No abstract is available for this record.

Wei Wang, Pengyu Guan, Tao Leng, Zhiyuan Peng · 10 authors

No abstract is available for this record.

Rajan Kumar

The evolution of organised crime in India and its transforming relations between power and criminality increasingly reflect the logic of decentralisation, digital technologies and networked connections. The gangs headed by dons, which rely on sharp hierarchical authoritybased structures and are punctuated by layers of absentees serving only their personal interests, are giving way to a quasi-invisible architecture of fluidity, cell-based interactions and technological apparatus. Our paper places the Lawrence Bishnoi Gang at the centre of the inquiry of a contemporary interstate gang network in the digital era, exploring the dynamics of how new digital appreciation, reconnaissance, social media, encrypted exchanges and prison forays emerge to provide new reach and resiliency. Networks beg to differ; if we are to extend networkable theory to include also the study of criminality, then the Bishnoi network is the proof in the pudding. Making a special place for the records of recent homemaking, we examine how they view high visibility, dalliance in extortion, recruitment, targeted killings and crossborder desiring playing host to an archipelago in constancy even as the leaders are locked away in prisons away from home. Features like the need for hub and spoke models and high visibility required need to be varied in digital amplification, conspiring to leverage symbolic power, fear and reputation that take over physicality and locomotion for effective control over regions. Under considerable stake in the legal framework, the duration study examines most existing laws in the Indian Constitution that deal with gangsterism, such as the Indian Penal Code, better coupled with the Unlawful Activities (Prevention) Act and new media protocols dilemmas seen in the context of jurisdictions, lax borders and differential standards in prosecuting loosely accreted actors. Themes, such as suggestions, Honda variants like focus on interstate security units in combating the malady of cyber threats are safeguarded, and digital surveillance, though with care for the future.

Zhen Chen, Fang Zhou, Bo Liu, Haixia Long · 5 authors

In recent years, phishing scams have become one of the most rampant criminal activities on Ethereum, causing significant financial losses to investors and disruptions to the Ethereum ecosystem. Existing phishing scam detection methods typically model Ethereum transaction records as graphs, extracting features from paired nodes based on the topological relationships. However, these methods mostly focus on low-order relational aspects, neglecting higher-order structural information in the network. In this paper, we propose a new method — Ethereum Phishing Scam Detection by Higher-Order Topology (EPSD-HOT), which improves phishing scam detection performance by mining higher-order topological features from the network. We conduct experiments on a public dataset and a crawled real-world dataset, extracting ten subgraphs with distinct network characteristics. The experimental results show that the average AUC-ROC for the ten subgraphs is 0.9970, with improvements ranging from 0.0181 to 0.1658 compared to baseline methods. This indicates that our approach is highly robust and can effectively detect phishing scams across different subgraphs while overcoming the issue of class imbalance. By incorporating higher-order structural information into node features, this work offers new insights for enhancing phishing scam detection in Ethereum.

Fabian Schär, Dario Thürkauf, David Yermack

We develop a model of aberrant behavior by Bitcoin miners and test it with a new 2017-2025 dataset.Miners' rewards, comprised partly of user fees, exhibit variability across blocks of transactions.When large reward disparities exist between adjacent blocks, miners have incentives to attempt alternative versions of prior blocks and claim other miners' rewards for themselves.Regression analysis shows that fee differentials are associated with these attacks and longer waiting times between blocks.These patterns imply potential destabilization of the Bitcoin blockchain as future mining rewards become more volatile due to gradual withdrawal of fixed block subsidies.

Steven Paul Nohr

Airdrops are widely used in blockchain ecosystems as mechanisms for token distribution, user bootstrapping, and governance decentralization. These mechanisms frequently rely on balance or stake snapshots taken at specific blockchain heights or epochs to determine eligibility. However, snapshot-based distribution introduces a critical temporal vulnerability: the assumption that momentary state accurately represents sustained economic participation.This paper defines and analyzes <b><i>Airdrop Snapshot Spoofing</i></b>, a class of temporal state manipulation attacks in which adversaries exploit the gap between snapshot definition, execution, and settlement to illegitimately capture token allocations. We demonstrate that such exploits are not edge cases but structural weaknesses inherent to snapshot-based systems across Proof-of-Stake (PoS) and tokenized networks. We further argue that snapshot spoofing represents a core-layer economic security failure rather than a marketing or distribution flaw, and we outline mitigation strategies based on time-weighted enforcement and validator-level continuity checks.

Christos A. Makridis

Abstract Cheap, disposable online identities make abuse easier to externalize. Users can harass, evade bans, amplify content through fake accounts, or abandon a damaged reputation at low cost, while other users, moderators, and platforms bear the consequences. This paper examines verified pseudonymity as an institutional response to that problem. First, I model online communities as club-governed informational commons in which incivility degrades the shared environment and raises enforcement costs. The model shows that conduct can improve when sanctions attach to a persistent pseudonymous identity and when users have future access, reputation, or governance rights at stake. Second, I compare verified pseudonymity with open pseudonymity, real-name mandates, centralized know-your-customer verification, algorithmic moderation, and no intervention. Decentralized identifiers, verifiable credentials, proof of personhood, and non-transferable standing credentials matter because they can separate authentication from public identification. Third, I add a community-currency layer that separates access to scarce attention from governance rights. The result is a governance framework in which accountability depends less on public naming than on durable standing, credible sanctions, and reusable privacy-preserving credentials.

Kevin Leyes

No abstract is available for this record.

Andrii Svintsytskyi

The expansion of Decentralized Finance (DeFi) and Anonymity-Enhancing Technologies (AETs) has complicated the tracking of illicit financial flows. This article analyzes three distinct AETs—Tornado Cash, Monero, and Zcash—to assess how specific protocol mechanisms degrade transaction‑graph attribution and obstruct compliance. Synthesizing technical literature, AML/CFT frameworks, and recent judicial documentation, the study traces how design choices translate into investigative challenges. The analysis yields three key findings. First, “decentralization” rarely eliminates control; instead, it shifts choke points to infrastructure layers such as bridges and RPC providers. Second, while AETs significantly raise attribution costs, their effectiveness is often conditional and dependent on usage patterns. Third, the Tornado Cash enforcement saga illustrates the limitations of applying traditional sanctions to autonomous code. The paper concludes by proposing a mitigation agenda focused on measurable risk reduction at entry/exit points without compromising legitimate privacy.

Yihan Sun, Katherine Webb, Mohamed Alamin Ezeldin, VALERII A. ANUFRIEV · 6 authors

No abstract is available for this record.

Loso Judijanto

This study presents a comprehensive bibliometric analysis of blockchain identity management research published between 2010 and 2025, aiming to map its intellectual structure, thematic evolution, and global collaboration patterns. Using data retrieved from the Scopus database and analyzed with VOSviewer, the study applies network visualization, overlay visualization, density mapping, citation analysis, and co-authorship analysis to uncover dominant research streams and emerging frontiers. The results reveal that the field is conceptually centered on blockchain-based authentication and decentralized identity management systems, with increasing scholarly attention toward privacy-preserving mechanisms such as zero-knowledge proofs, anonymity, and data protection. Thematic evolution indicates a clear transition from foundational infrastructure-oriented studies to application-driven and regulatory-sensitive research domains, including e-government, IoT, healthcare, and digital governance. Collaboration analysis highlights the leading role of China and India, supported by strong transcontinental linkages with the United States and European countries, reflecting a globally interconnected yet regionally concentrated research landscape. By systematically mapping publication trends, thematic clusters, and collaboration networks, this study provides a structured knowledge base that supports future theoretical development, guides practical implementation, and informs policy formulation in blockchain-based digital identity ecosystems.

Adarsh Singhal, Mahendra Jangir

The rapid proliferation of digital assets — encompassing cryptocurrencies, non-fungible tokens (NFTs), and related virtual instruments — has generated a parallel ecosystem of sophisticated financial crime that challenges the structural architecture of India's legal order. This article provides a critical, multidimensional analysis of the intersection between digital assets and criminal liability in India, with a specific focus on cryptocurrency-enabled cyber offences. Drawing on statutory interpretation, judicial precedent, and comparative regulatory analysis, the article examines the definitional evolution of 'virtual digital assets' under the Income Tax Act, 1961; the extension of the Prevention of Money Laundering Act, 2002 (PMLA) to virtual asset service providers in March 2023; the application of the Information Technology Act, 2000; the transitional criminal jurisprudence introduced by the Bharatiya Nyaya Sanhita, 2023 (BNS); and the Enforcement Directorate's growing arsenal of investigative tools. The article further analyses landmark case law including Internet and Mobile Association of India v. Reserve Bank of India (2020), and the recent WazirX security breach (2024) and its regulatory aftermath. Finally, it critically evaluates the persistent lacunae in India's legal framework — including the absence of dedicated cryptocurrency legislation, low conviction rates, jurisdictional challenges in cross-border offences, and the threat posed by decentralised finance (DeFi) protocols — and proposes a comprehensive reformative agenda rooted in the principles of legal certainty, technological neutrality, and international cooperation.

Hazik Mohamed

The rapid rise of Decentralized Finance (DeFi) and anonymity-focused cryptocurrencies has transformed financial systems by eliminating intermediaries and enabling peer-to-peer transactions. While these innovations offer numerous benefits, they also present unprecedented challenges for crime prevention and regulatory enforcement. This paper examines how DeFi and privacy-enhanced cryptocurrencies, such as Monero and Zcash, facilitate financial crimes, including money laundering, ransomware attacks, and fraud. By applying criminological theories—Strain Theory, Routine Activity Theory, and Rational Choice Theory—this study reinterprets traditional crime models in the context of blockchain-based financial ecosystems. Law enforcement agencies face significant hurdles in investigating and prosecuting crypto-enabled financial crimes due to jurisdictional limitations, privacy-enhancing technologies, and decentralized governance. This paper explores how blockchain analytics, artificial intelligence-driven risk assessment, and cross-border regulatory collaborations, such as the Financial Action Task Force (FATF) Travel Rule and the EU’s Markets in Crypto-Assets (MiCA) regulation, are being developed to counter these emerging threats. Additionally, it assesses the institutional limitations of law enforcement agencies, the role of DeFi governance communities in mitigating financial crimes, and the potential impact of central bank digital currencies (CBDCs) on reducing illicit transactions. To enhance regulatory effectiveness, this study recommends strengthening international cooperation, improving forensic capabilities for tracking illicit blockchain transactions, and implementing ethical frameworks that balance financial privacy with security. The findings contribute to criminology, financial regulation, and cybersecurity by offering insights into evolving digital crimes and proposing solutions to mitigate their risks. Future research should explore the role of artificial intelligence in DeFi crime detection and the impact of regulatory advancements on illicit financial flows in decentralized ecosystems.

Dib Akram

The contemporary world has witnessed a technological revolution in the field of financial technology, which gave rise to cryptocurrencies as a decentralized electronic monetary system.However, this technological development has also entailed serious criminal uses, as criminal organizations have exploited the characteristics of these currencies to facilitate human trafficking crimes.This study addresses the conceptual framework of cryptocurrencies and human trafficking crimes by analyzing their definitions and distinctive features.It then provides a detailed review of the methods of using cryptocurrencies in various stages of human trafficking crimes, starting from financing recruitment and transportation operations, through collecting proceeds from the sexual exploitation and forced labor of victims, to money laundering and concealing criminal proceeds using advanced technologies.The study aims to uncover the technical and financial mechanisms exploited by criminal organizations in using cryptocurrencies to finance human trafficking crimes, analyze the legal and security challenges facing international counter-efforts, and offer practical recommendations to develop legal frameworks, enhance international cooperation, and introduce advanced regulatory technologies to confront this growing phenomenon.

Anita Quas

No abstract is available for this record.

Liang Zhao

Amid the rapid evolution of digital currencies and the decentralized finance (DeFi) ecosystem, technology-driven, anonymous, and cross-border financial crimes pose systemic challenges to traditional regulatory frameworks. Grounded in three core theories of criminal psychology—Rational Choice Theory, Routine Activity Theory, and Techniques of Neutralization—and integrating the “technology–society co-construction” perspective from the sociology of technology, this study constructs a three-dimensional analytical framework encompassing “technological ecology, social cognition, and individual psychology.” It systematically elucidates the psychological formation logic and evolutionary pathways of financial crimes within the DeFi domain. The research reveals that the technical features of DeFi—anonymity, decentralization, and code autonomy—collectively create a “structural opportunity space” characterized by low accountability costs and weakened moral constraints. Subcultural communities further supply “morally neutralizing scripts” through narratives of crypto-libertarianism and the myth of “code as law.” Under these dual influences, individual psychology undergoes transformation, manifesting as complex motivations, distorted risk perceptions, and heightened moral disengagement, ultimately leading to a rationalization mechanism for criminal acts veiled behind “technological neutrality.”

Minal Digambar Jangale

Digital forensic investigation in 2025 faces unprecedented challenges posed by the convergence of decentralized web technologies (Web3), adversarial generative AI systems, and darknet infrastructure. Traditional attribution and evidence preservation methodologies prove in-sufficient when adversaries exploit blockchain immutability, synthetic media generation, and privacy-enhancing technologies to obscure malicious intent. This paper in-traduces SHARD (Shadowed and Silicon Hybrid Attribution and Reconstruction Diagnostic), a multi-modal forensic framework designed to recover, correlate, and at-tribute malicious artifacts across distributed ledger systems, synthetic content generators, and anonymized net-works. Through systematic analysis of 47 real-world cybercriminal cases and forensic evaluation against 12 at-tack vectors, SHARD achieves 89.2% attribution accuracy while reducing investigative timelines by 64% com-pared to conventional methods. We present novel techniques for blockchain temporal analysis, deepfake prove-nance tracking, and Tor-exit node correlation. The frame-work integrates machine learning-based anomaly detection with cryptographic verification to distinguish legitimate decentralized activity from adversarial manipulation. Our contributions include: (1) a formal threat model encompassing Web3 forensics; (2) a hybrid architecture combining on-chain and off-chain analysis; (3) algorithmic innovations for synthetic media fingerprinting; and (4) extensive empirical validation against contemporary attack scenarios. This work addresses a critical gap in digital forensics as investigative techniques must evolve alongside the technological infrastructure that criminals exploit.

Yosita Saikhwan, Khwanmanee Maneechot, Kanokphol Nikornpest

This research undertakes a comparative analysis of Thailand’s anti-money laundering (“AML”) regulatory framework in relation to the most recent recommendations issued by the Financial Action Task Force (“FATF”) concerning money laundering risks associated with Security Token Offerings (“STOs”) conducted via blockchain technology. The objective is to identify potential regulatory gaps and areas for improvement in Thailand’s existing AML measures, particularly in the areas of regulatory oversight, licensing requirements, customer due diligence (“CDD”), recordkeeping obligations, and the reporting of suspicious transactions by virtual asset service providers (“VASPs”). The methodological basis of the research is the comparative analysis method, examining Thailand’s applicable AML laws and regulations alongside FATF guidelines, relevant literature, and case law. The research found that Thailand’s applicable AML laws, including the relevant regulations, are inadequacies and inefficiencies in the regulatory oversight of securities offerings that utilize emerging technologies. Specifically, the current regulatory framework is insufficient in effectively preventing or mitigating risks related to money laundering and the financing of terrorism for investors. As a result, it does not adequately ensure the security and integrity of investments in decentralized systems, therefore, it fails to provide sufficient safeguards to protect investors from inadvertently becoming involved in unlawful activities. These shortcomings indicate a lack of alignment with international standards issued by the FATF. This research is useful to legislative authorities, lawyers, law students, and regulatory bodies, especially in Thailand, and only limited to the regulation of money laundering in Thailand and does not provide empirical research.

Mallikarjun Reddy Gouni

Financial crime detection faces unparalleled challenges as criminal networks exploit digital payment channels, cryptocurrency platforms, and cross-border transaction systems outside traditional monitoring frameworks. In this respect, AFCI introduces a novel framework for federated machine learning, regulatory reasoning engines, and real-time risk propagation analytics to build unified global privacy-preserving anti-crime intelligence ecosystems. The framework lets organizations train collaborative models with decentralized institutions, safely aggregating information from multiple parties without sharing sensitive transaction data by means of secure aggregation protocols and differential privacy mechanisms. Large language models coupled with knowledge graphs automate the processes of regulatory interpretation and rule generation, and graph neural networks enable the detection of coordinated criminal activities on a large scale in transaction networks through temporal message passing mechanisms. Reinforcement learning agents continuously optimize detection policies to balance the identification of genuine threats against the goal of minimizing false alarms. The framework bridged critical gaps in cross-border compliance coordination and empowered institutions to develop shared detection capabilities in support of data localization requirements and an array of diverse regulatory frameworks. Long-term security of privacy-preserving federated computation would be guaranteed with post-quantum cryptography. This convergence of advanced technologies allows next-generation financial crime prevention systems to remain effective against evolving criminal methodologies while preserving fundamental privacy rights.

Hugo Almeida, Pedro Pinto, Ana Fernández Vilas

The widespread adoption of cryptocurrencies has transformed the financial landscape by enabling swift, decentralised transactions. However, the pseudonymous nature of digital currencies has also fuelled illicit activities, such as money laundering. Criminals perform money laundering to access illicitly acquired funds without detection and convert illegally obtained assets into untraceable commodities, seamlessly integrated into the financial system. Although new regulatory measures have been introduced, illicit actors continue to exploit various methods, from peer-to-peer exchanges to cryptocurrency mixing services, to obscure the origins of illegal funds. This study presents a parametric analysis of these methods, examining dimensions such as duration, number of actors, contextual requirements, operational difficulty, traceability, and costs across each stage of the money laundering process: placement, layering, and integration. The analysis indicates that, while more sophisticated techniques may provide a higher degree of anonymity, they simultaneously require specialised technical expertise and meticulous planning. Consequently, there is a trade-off between the level of privacy attainable and the operational complexity inherent to each method. By systematically comparing these strategies, this analysis aims to contribute to a deeper understanding of cryptocurrency-based money laundering techniques, providing insight for more effective prevention and mitigation measures for both regulatory authorities and the financial sector.

Nancy Michail, Niloufer Selvadurai, Doron Goldbarsht

Purpose The purpose of this paper is to analyse privacy and national surveillance laws in Australia, including but not limited to the federal Privacy Act 1988 and the federal Anti-Money Laundering and Counter-Terrorism Financing Act 2006 (AML/CTF), to determine whether a tension exists between these two statutes within the context of the deployment of central bank digital currency (CBDC) in Australia. Design/methodology/approach The paper adopts doctrinal and normative approaches for analysis of the relevant legislations. Furthermore, the paper adopts a functionalist theoretical perspective to interrogate the interrelationship between regulation and society. Findings The paper suggests that the relevant legislations contain undefined terms, such as “reasonable grounds”, which may give the appearance of a balanced approach to the interactions between privacy and national surveillance laws within the context of deployment of CBDCs. The paper argues that lack of definition of these terms, however, renders the terms ineffective, leading to a potential regulatory overreach. The paper recommends administrators need internal policies and procedures that would give clear guidance on the possible meanings of those legislative terms to achieve the balance desired between privacy and national security requirements in Australia when deploying CBDCs. It is suggested that internal policies are to base suspicion on factual basis for decision-makers to have reasonable grounds for contravening privacy legislations. This may be achieved through requiring decision-makers to justify their decisions and consider alternative options before infringing on privacy, thus enhancing accountability. Furthermore, it is suggested that the transparency and traceability provided by distributed ledger technologies will compel decision-makers to assess the benefits of privacy violations against their costs, promoting a balanced approach to surveillance and personal data disclosure. Originality The originality of the paper lies in its seminal analysis of the interaction of privacy an anti-money laundering laws in the context of the introduction of a central bank digital currency.