Regulatory technology (RegTech) is transforming financial compliance by integrating advanced information technologies to strengthen anti money laundering and countering the financing of terrorism (AML CFT) frameworks. Recent literature suggests that such technologies represent more than just an efficiency tool; they mark a paradigm shift in regulation and the evolution of financial oversight (Kurum, 2023). This paper aims to provide a narrative review of recent RegTech applications in financial crime prevention, with a focus on key compliance domains. A structured literature review was conducted to examine publications between 2020 and 2024 with a thematic synthesis of findings related to customer due diligence (CDD) and know your customer (KYC), transaction monitoring, regulatory reporting and compliance automation, information sharing and cross border cooperation, as well as cost efficiency. Findings reveal that RegTech solutions give financial institutions more responsibility for detecting and managing financial crime risks, making them more active players in compliance processes traditionally overseen by regulators. The combined use of technologies such as artificial intelligence (AI), blockchain, and big data also generates synergistic effects that improve compliance outcomes beyond what these technologies achieve individually. This demonstrates the strategic relevance of integrated RegTech approaches.
Modern high-throughput BFT consensus protocols use streamlined push-pull mechanisms to disseminate blocks and keep happy-path performance optimal. Yet state-of-the-art designs lack a principled and efficient way to exchange blocks, which leaves them open to targeted attacks and performance collapse under network asynchrony. This work introduces the concept of a block synchronizer, a simple abstraction that drives incremental block retrieval and enforces resource-aware exchange. Its interface and role fit cleanly inside a modern BFT consensus stack. We also uncover a new attack, where an adversary steers honest validators into redundant, uncoordinated pulls that exhaust bandwidth and stall progress. Beluga is a modular and scarcity-aware instantiation of the block synchronizer. It achieves optimal common-case latency while bounding the cost of recovery under faults and adversarial behavior. We integrate Beluga into Mysticeti, the consensus core of the Sui blockchain, and show on a geo-distributed AWS deployment that Beluga sustains optimal performance in the optimistic path and, under attack, delivers up to 3x higher throughput and 25x lower latency than prior designs. The Sui blockchain adopted Beluga in production.
Ethan Hicks, Joseph Oglio, Mikhail Nesterenko, Gokarna Sharma
We study financial transaction confirmation finality in Bitcoin as a function of transaction amount and user risk tolerance. A transaction is recorded in a block on a blockchain. However, a transaction may be revoked due to a fork in the blockchain, the odds of which decrease over time but never reach zero. Therefore, a transaction is considered confirmed if its block is sufficiently deep in the blockchain. This depth is usually set empirically at some fixed number such as six blocks. We analyze forks under varying network delays in simulation and actual Bitcoin data. Based on this analysis, we establish a relationship between block depth and the probability of confirmation revocation due to a fork. We use prospect theory to relate transaction confirmation probability to transaction amount and user risk tolerance.
Blockchain technologies underpin an expanding ecosystem of decentralized applications, financial systems, and infrastructure. However, the fundamental networking layer that sustains these systems, the peer-to-peer (P2P) layer, of all but the top few ecosystems remains largely opaque. In this paper, we present the first longitudinal, cross-network measurement study of 36 public blockchain networks. Over 9 months (since late 2024), we deployed 15 active crawlers, sourced data from two additional community crawlers, and conducted hourly connectivity probes (e.g., pings and protocol-level handshakes) to observe the evolving state of these networks. Furthermore, by leveraging Ethereum's discovery protocols, we inferred metadata for an additional 19 auxiliary networks that utilize the Ethereum peer discovery protocol. We also explored Internet-wide scans, which only require probing each protocol's default ports with a simple, network-specific payload. This approach allows us to rapidly identify responsive peers across the entire address space without having to implement custom discovery and handshake logic for every blockchain. We validated this method on Bitcoin and similar networks with known ground truth, then applied it to Cardano, which we could not crawl directly. Our study uncovers dramatic variation in network size from under 10 to more than 10,000 active nodes. We quantify trends in IPv4 versus IPv6 usage, analyze autonomous systems and geographic concentration, and characterize churn, diurnal behavior, and the coverage and redundancy of discovery protocols. These findings expose critical differences in network resilience, decentralization, and observability. Beyond characterizing each network, our methodology demonstrates a general framework for measuring decentralized networks at scale. This opens the door for continued monitoring, benchmarking, and more transparent assessments of blockchain infrastructure across diverse ecosystems.
Preston Vander Vos, Alberto Sonnino, Giorgos Tsimos, Philipp Jovanovic · 5 authors
Blockchain consensus faces a trilemma of security, latency, and decentralization. High-throughput systems often require a reduction in decentralization or robustness against strong adversaries, while highly decentralized and secure systems tend to have lower performance. We present BlueBottle, a two-layer consensus architecture. The core layer, BB-Core, is an n=5f+1 protocol that trades some fault tolerance for a much lower finality latency with a medium-sized core validator set. Our experiments show that BB-Core reduces latency by 20-25% in comparison to Mysticeti. The guard layer, BB-Guard, provides decentralized timestamping, proactive misbehavior detection in BB-Core, and a synchronous recovery path. When it observes equivocations or liveness failures in the core -- while tolerating up to f<3n/5 faulty nodes in the primary layer -- guard validators disseminate evidence, agree on misbehaving parties for exclusion or slashing, and either restart the core protocol (for liveness violations) or select a canonical fork (for safety violations). Together, these layers enable optimistic sub-second finality at high throughput while maintaining strong safety and liveness under a mild synchrony assumption.
Chuanlei Li, Zhicheng Sun, Jing Xin Yuu, Xuechao Wang
Cross-chain interoperability is a core component of modern blockchain infrastructure, enabling seamless asset transfers and composable applications across multiple blockchain ecosystems. However, the transparency of cross-chain messages can inadvertently expose sensitive transaction information, creating opportunities for adversaries to exploit value through manipulation or front-running strategies. In this work, we investigate cross-chain sandwich attacks targeting liquidity pool-based cross-chain bridge protocols. We uncover a critical vulnerability where attackers can exploit events emitted on the source chain to learn transaction details on the destination chain before they appear in the destination chain mempool. This information advantage allows attackers to strategically place front-running and back-running transactions, ensuring that their front-running transactions always precede those of existing MEV bots monitoring the mempool of the destination chain. Moreover, current sandwich-attack defenses are ineffective against this new cross-chain variant. To quantify this threat, we conduct an empirical study using two months (August 10 to October 10, 2025) of cross-chain transaction data from the Symbiosis protocol and a tailored heuristic detection model. Our analysis identifies attacks that collectively garnered over \(5.27\) million USD in profit, equivalent to 1.28\% of the total bridged volume.
Crypto enthusiasts claim that buying and holding crypto assets yields high returns, often citing Bitcoin's past performance to promote other tokens and fuel fear of missing out. However, understanding the real risk-return trade-off and what factors affect future crypto returns is crucial as crypto becomes increasingly accessible to retail investors through major brokerages. We examine the HODL strategy through two independent analyses. First, we implement 480 million Monte Carlo simulations across 378 non-stablecoin crypto assets, net of trading fees and the opportunity cost of 1-month Treasury bills, and find strong evidence of survivorship bias and extreme downside concentration. At the 2-3 year horizon, the median excess return is -28.4 percent, the 1 percent conditional value at risk indicates that tail scenarios wipe out principal after all costs, and only the top quartile achieves very large gains, with a mean excess return of 1,326.7 percent. These results challenge the HODL narrative: across a broad set of assets, simple buy-and-hold loads extreme downside risk onto most investors, and the miracles mostly belong to the luckiest quarter. Second, using a Bayesian multi-horizon local projection framework, we find that endogenous predictors based on realized risk-return metrics have economically negligible and unstable effects, while macro-finance factors, especially the 24-week exponential moving average of the Fear and Greed Index, display persistent long-horizon impacts and high cross-basket stability. Where significant, a one-standard-deviation sentiment shock reduces forward top-quartile mean excess returns by 15-22 percentage points and median returns by 6-10 percentage points over 1-3 year horizons, suggesting that macro-sentiment conditions, rather than realized return histories, are the dominant indicators for future outcomes.
Abstract: Identity theft has emerged as a psychologically consequential form of cybercrime enabled by the proliferation of digital platforms, the expansion of datafication, and the collapse of traditional criminal–victim proximity. As personal identity becomes increasingly externalized through financial accounts, medical records, biometric templates, and algorithmically curated social profiles, offenders exploit cognitive biases, disclosure fatigue, and habituated oversharing to acquire and weaponize personal information. Criminal psychology research demonstrates that social engineering, authority mimicry, and emotional urgency manipulate victims into bypassing rational scrutiny, while cyberpsychology highlights the affective attachment individuals form with their digital representations. Unlike conventional theft, in which tangible objects are removed, identity theft appropriates informational components of the self, enabling prolonged impersonation, reputational distortion, and chronic anxiety that cannot be readily restored. Geographic detachment, encrypted communication channels, and anonymizing technologies reduce offenders’ perceived accountability, encouraged moral disengagement and facilitating mass victimization at minimal personal risk. Victims, confronted with unauthorized transactions or corrupted medical histories, report hypervigilance, loss of digital agency, and destabilization of narrative coherence. Emerging technologies, including Internet of Things devices, deepfake media, decentralized finance, and eventually quantum computing, further expand the attack surface and amplify criminogenic opportunity structures. Meanwhile, jurisdictional fragmentation complicates forensic attribution and legal recourse. Collectively, these developments reveal that traditional, place-based models of personal security are insufficient in networked environments. Safeguarding informational sovereignty requires interdisciplinary approaches that integrate behavioral criminology, cognitive vulnerability assessment, cyberpsychological resilience, and international policy coordination. Understanding identity theft as an ontological, relational, and psychologically persistent violation offers critical insight for prevention, victim support, and regulatory design in the digital epoch. Keywords: Identity Theft; Cyberpsychology; Criminal Psychology; Datafication; Digital Proximity Collapse; Social Engineering; Informational Sovereignty; Biometric Fraud; Cognitive Vulnerability; Cybercrime Scalability
Open access
2 source records
Cybercrime and Law Enforcement Studies
Crime Patterns and Interventions
Psychopathy, Forensic Psychiatry, Sexual Offending
The theses examine the legal and financial aspects of cryptocurrencies, taxation specifics, and transaction monitoring.The advantages of cryptocurrencies in financial inclusion and blockchain implementation are outlined.Challenges of legal regulation and prospects for aligning Ukrainian legislation with EU MiCA standards are highlighted.
Remote Procedure Call (RPC) services have become a primary gateway for users to access public blockchains. While they offer significant convenience, RPC services also introduce critical privacy challenges that remain insufficiently examined. Existing deanonymization attacks either do not apply to blockchain RPC users or incur costs like transaction fees assuming an active network eavesdropper. In this paper, we propose a novel deanonymization attack that can link an IP address of a RPC user to this user's blockchain pseudonym. Our analysis reveals a temporal correlation between the timestamps of transaction confirmations recorded on the public ledger and those of TCP packets sent by the victim when querying transaction status. We assume a strong passive adversary with access to network infrastructure, capable of monitoring traffic at network border routers or Internet exchange points. By monitoring network traffic and analyzing public ledgers, the attacker can link the IP address of the TCP packet to the pseudonym of the transaction initiator by exploiting the temporal correlation. This deanonymization attack incurs zero transaction fee. We mathematically model and analyze the attack method, perform large-scale measurements of blockchain ledgers, and conduct real-world attacks to validate the attack. Our attack achieves a high success rate of over 95% against normal RPC users on various blockchain networks, including Ethereum, Bitcoin and Solana.
This paper discusses the obstacles to the capitalization of data elements, such as the difficulties in confirming data ownership, trust deficit, privacy breaches, and inefficiency of transactions, through a distributed solution based on blockchain technology. First, a data ownership confirmation mechanism based on a consortium blockchain is established by using the Merkle tree and PBFT (Practical Byzantine Fault Tolerance) consensus algorithm to achieve transparency and traceability of data ownership. Second, a multi-dimensional data value evaluation and RF-BP (Random Forest-Back Propagation) dynamic pricing mechanism are established by using machine learning algorithms to evaluate the value of data assets in a scientific manner. Third, a smart contract is established for pricing and payment, in order to achieve transaction automation and clearing and settlement. Finally, ZKP (Zero-Knowledge Proof) technology is applied to develop a mechanism for verifying compliance and privacy of data under the proposition of public review and "visible, invisible". Experimental results show that the proposed method reduces the average leakage risk and defense success rate under various attacks to 8.57 % and 97.1%, respectively. In terms of transaction efficiency, the proposed method achieves a throughput of 1250 TPS (Transactions Per Second) with a latency of 120 milliseconds at a 50-node scale. Overall performance is demonstrated with a confirmation and transaction success rate of 99.2% and 97.8%, respectively. The suggested framework provides reliable confirmation of data elements, scientific pricing, efficient trading and transaction processes, and privacy protection. It can support the vision of developing a secure, transparent and efficient market for data element circulation with technical feasibility and performance.
The increasing use of deep learning (DL) models has given rise to significant privacy concerns regarding training and inference data. To address these concerns, the community has increasingly adopted crypto-based privacy-enhancing technologies (CPET) like homomorphic encryption (HE), secure multi-party computation (MPC), and zero-knowledge proofs (ZKP). The integration of CPET with DL, often referred to as CPET-DL, is commonly facilitated by specialized frameworks like CrypTen, TenSEAL, and EZKL. These frameworks offer configurable parameters to balance model accuracy and computational efficiency during privacy-preserving operations. However, these configurations, while seemingly harmless, can introduce subtle vulnerabilities. The stealthy attacks induced by misconfigurations are hard to detect because 1) the plaintext models remain vulnerability-free, and 2) existing auditing tools are hardly applicable to CPET-hardened models. This creates a paradox: tools intended to protect privacy can be undermined through configuration manipulation.
Open access
Cryptography and Data Security
Physical Unclonable Functions (PUFs) and Hardware Security
Over the past decade, the surge in popularity of cryptocurrency investment has garnered a lot of attention in the financial marketplace. This study aims to open the “black box” of cryptocurrency investors by understanding the past cryptocurrency investors’ characteristics. Specifically, we investigated the relationship between cryptocurrency investment investors and their characteristics, such as levels of financial literacy, as proxied by both subjective and objective financial knowledge measures, investment experience, and investment knowledge. We apply the logistic regression model to the data from the most recent cohort of the National Financial Capability Study 2021 (NFCS21). We find that investment experience and investment knowledge are the important factors related to investing in cryptocurrency among the characteristics examined. Surprisingly, we also find that individuals with higher education are less likely to become cryptocurrency investors. Finally, the present study reveals an interaction effect between age and investment knowledge, showing that the linkage between cryptocurrency investment and investment knowledge is stronger for younger investors with an age between 18 and 34 than for those above 44. This paper contributes to the existing literature on cryptocurrency investment by revealing the personal characteristics that categorize a cryptocurrency investor. This paper also extends the existing conceptual framework about cryptocurrency by showing that sociodemographic factors, such as age could moderate the linkage between investment knowledge and cryptocurrency investment.
We introduce the notion of committed vector oblivious linear evaluation (C-VOLE), which allows a party holding a pre-committed vector to generate VOLE correlations with multiple parties on the committed value. It is a unifying tool that can be found useful in zero-knowledge proofs (ZKPs) of committed values, actively secure multi-party computation, private set intersection (PSI), etc.
This preprint introduces Proof-of-Being (PoB) and ontological cryptography — the first cryptographic paradigm explicitly designed for the post-AGI era. As frontier language models, autonomous agents, and embodied robots increasingly generate outputs indistinguishable from human actions, classical authentication mechanisms (public-key cryptography, biometrics, CAPTCHAs, proof-of-personhood systems) no longer answer the central security question of the 2025 digital environment: “Was this action performed by a conscious human being?” Proof-of-Being addresses this foundational problem through HISPU (Human Intention Semantic Proof Unit) — a probabilistically unforgeable, fully anonymous attestation of human presence based on ontological randomness and multi-layered semantic–physiological–contextual proofs. HISPU verifies being rather than identity, enabling anonymous but provably human actions across digital systems. Key contributions of this work: · Introduction of ontological randomness as a fourth fundamental source of cryptographic unpredictability (beyond mathematical, physical, and hybrid entropy sources). · Formal definition of the HISPU primitive and seven foundational axioms of ontological cryptography. · Demonstration that no computational system — including superintelligent AGI — can forge a valid HISPU under the Ontological Security Assumption. · Clear conceptual separation between proving human being (ontological presence) and proving identity (social personhood). Applications include: · AGI safety and human-in-the-loop supervisory gates · Sybil-resistant DAO voting and decentralized governance · Intention-based economic systems · Bot-resistant democratic systems, legal smart contracts, and high-stakes authentication · Verifiable human authorship in generative AI ecosystems · Neurotechnology consent verification and BCI safety · Web4 / Noospheric Web intention-layer protocols This work positions Proof-of-Being as a foundational infrastructure for safe human–AI coexistence and represents the first major shift in digital trust since Diffie–Hellman (1976) and zero-knowledge proofs (1985). Keywords: proof-of-being, ontological cryptography, HISPU, proof of intention, ontological randomness, human verification, AGI safety, human-in-the-loop verification, post-AGI trust, intention-based economy, sybil resistance, digital ontology, privacy-preserving verification, human-AI coexistence, consciousness proof, non-simulatable proofs, machine unforgeability.
Artificial Intelligence as a Service (AIaaS) enables users to query a model hosted by a service provider and receive inference results from a pre-trained model. Although AIaaS makes artificial intelligence more accessible, particularly for resource-limited users, it also raises verifiability and privacy concerns for the client and server, respectively. While zero-knowledge proof techniques can address these concerns simultaneously, they incur high proving costs due to the non-linear operations involved in AI inference and suffer from precision loss because they rely on fixed-point representations to model real numbers.
James Hsin-yu Chiang, Ivan Damgård, William R. Duro, Sunniva Engan · 6 authors
We propose efficient, post-quantum threshold ring signatures constructed from one-wayness of AES encryption and the VOLE-in-the-Head zero-knowledge proof system. Our scheme scales efficiently to large rings and extends the linkable ring signatures paradigm. We define and construct key-binding deterministic tags to achieve linkability. We then extend our threshold ring signatures to realize post-quantum anonymous ledger transactions in the spirit of Monero. Finally, our deterministic tags also enable succinct aggregation using approximate lower bound arguments of knowledge; this allows us to achieve succinct (approximate) multi-signatures without SNARKs. Our constructions assume symmetric key primitives only.
In an era of fast-pace technological change, the internet is evolving from Web 1.0 (static, one-way communication) and Web 2.0 (interactive, collaborative platforms) to Web 3.0, characterized by decentralization, artificial intelligence, blockchain, and a focus on authentic values and meaningful connections. Web 3.0 empowers consumers and transforms the internet into a decentralized platform where users control their personal data, intermediaries are replaced by smart contracts and blockchain, but it also introduces challenges such as technological complexity, security risks, regulatory difficulties, and interoperability with Web 2.0. Web 3.0 marketing emphasizes an approach that includes emotional, cultural, and spiritual dimensions, enabling brands to gain a profound and lasting relevance. In this paper we analyse the multifacets of Web 3.0 marketing in the fashion industry, a sector intensely transformed by social, cultural, and technological dynamics. We investigate how marketing principles and Web 3.0 technologies, such as non-fungible tokens (NFTs), the metaverse, and digital identity, are being incorporated into fashion brand strategies, highlighting the benefits and challenges of building authentic relationships with consumers. Fashion brands are embracing emerging technologies to create immersive experiences and loyalty through NFTs, augmented reality, and virtual spaces in the metaverse.
Open access
Fashion and Cultural Textiles
Impact of AI and Big Data on Business and Society
Consumer Behavior in Brand Consumption and Identification
Muhammad Guhya Thesar Afani, Farhan Ferdiansyah, Eraneo Ihza P
The usage of blockchain has increased around the world over the years. It has become widely used in various sectors that need transparency, such as accounting, business, and auditing. Blockchains are gaining more popularity after being applied as a system for digital asset ownership, such as cryptocurrency and NFTs (Non-Fungible Tokens). This growing trend of blockchain is followed by the increasing trend of research regarding it in the last decade. Blockchain has the potential to revolutionize the auditing sector and enhance economic accountability due to its decentralized system. Therefore, research regarding blockchain applications in auditing is becoming an important topic. This study adopts a qualitative approach by using datasets retrieved from the Scopus website, from the search result of blockchain auditing, with a total of 1228 articles that were published in the last decade (2015-2025). Furthermore, this study also uses several software programs as data processing tools, such as R Studio, VOSViewer, and Publish or Perish. This study aims to understand the research trend regarding blockchain auditing in the last decade and highlight its implications for the auditing and economic sectors.
In timing-sensitive blockchain applications, such as decentralized finance (DeFi), achieving first-come-first-served (FCFS) transaction ordering among decentralized nodes is critical to prevent frontrunning attacks. Themis [CCS'23], a state-of-the-art decentralized FCFS ordering system, has become a key reference point for high-throughput fair ordering systems for real-world blockchain applications, such as rollup chains and decentralized sequencing, and has influenced the design of several subsequent proposals. In this paper, we critically analyze its core system property of practical batch-order fairness and evaluate the frontrunning resistance claim of Themis. We present the Ambush attack, a new frontrunning technique that achieves nearly 100% success against the practical batch-order fair system with only a single malicious node and negligible attack costs. This attack causes a subtle temporary information asymmetry among nodes, which is allowed due to the heavily optimized communication model of the system. A fundamental trade-off we identify is a challenge in balancing security and performance in these systems; namely, enforcing timely dissemination of transaction information among nodes (to mitigate frontrunning) can easily lead to non-negligible network overheads (thus, degrading overall throughput performance). We show that it is yet possible to balance these two by delaying transaction dissemination to a certain tolerable level for frontrunning mitigation while maintaining high throughput. Our evaluation demonstrates that the proposed delayed gossiping mechanism can be seamlessly integrated into existing systems with only minimal changes.
As Decentralized Finance (DeFi) develops, understanding user intent behind DeFi transactions is crucial yet challenging due to complex smart contract interactions, multifaceted on-/off-chain factors, and opaque hex logs. Existing methods lack deep semantic insight. To address this, we propose the Transaction Intent Mining (TIM) framework. TIM leverages a DeFi intent taxonomy built on grounded theory and a multi-agent Large Language Model (LLM) system to robustly infer user intents. A Meta-Level Planner dynamically coordinates domain experts to decompose multiple perspective-specific intent analyses into solvable subtasks. Question Solvers handle the tasks with multi-modal on/off-chain data. While a Cognitive Evaluator mitigates LLM hallucinations and ensures verifiability. Experiments show that TIM significantly outperforms machine learning models, single LLMs, and single Agent baselines. We also analyze core challenges in intent inference. This work helps provide a more reliable understanding of user motivations in DeFi, offering context-aware explanations for complex blockchain activity.
This poster introduces HarvETHter, a smart contract decompiler for EVM-based platforms such as Ethereum, Binance, and Polygon. We present the corpus completeness hypothesis, which we investigate through HarvETHter. Relying on our hypothesis, HarvETHter sources knowledge of the Ethereum blockchain and leverages it to decompile smart contracts to Solidity source code.
Committees are extensively used in the designs of various Proof-of-Stake (PoS) blockchains. A committee is simply a randomly selected subset of the parties/nodes in the system. Ideally, the committee should i) be as small as possible, and ii) properly represent the entire system, in terms of the corruption ratio. Existing committee selection schemes all follow the principle of proportionality, which says that a committee member should neither over-represent nor under-represent the stake it holds.
Ashwin Karthikeyan, Hengyu Liu, Kuldeep S. Meel, Ning Luo
Efficient zero-knowledge proofs (ZKPs) have been restricted to NP statements so far, whereas they exist for all statements in PSPACE. This work presents the first practical zero-knowledge (ZK) protocols for PSPACE-complete statements by enabling ZK proofs of QBF (Quantified Boolean Formula) evaluation. The core idea is to validate quantified resolution proofs (Q-Res) in ZK. We develop an efficient polynomial encoding of Q-Res proofs, enabling proof validation through low-overhead arithmetic checks. We also design a ZK protocol to prove knowledge of a winning strategy related to the QBF, which is often equally important in practice. We implement our protocols and evaluate them on QBFEVAL. The results show that our protocols can verify 72% of QBF evaluations via Q-Res proof and 82% of instances' winning strategies within 100 seconds, for instances where such proofs or strategies can be obtained.