Stephan Rabanser, Ali Shahin Shamsabadi, Olive Franzese, Xiao Wang · 6 authors
Cautious predictions -- where a machine learning model abstains when uncertain -- are crucial for limiting harmful errors in safety-critical applications. In this work, we identify a novel threat: a dishonest institution can exploit these mechanisms to discriminate or unjustly deny services under the guise of uncertainty. We demonstrate the practicality of this threat by introducing an uncertainty-inducing attack called Mirage, which deliberately reduces confidence in targeted input regions, thereby covertly disadvantaging specific individuals. At the same time, Mirage maintains high predictive performance across all data points. To counter this threat, we propose Confidential Guardian, a framework that analyzes calibration metrics on a reference dataset to detect artificially suppressed confidence. Additionally, it employs zero-knowledge proofs of verified inference to ensure that reported confidence scores genuinely originate from the deployed model. This prevents the provider from fabricating arbitrary model confidence values while protecting the model's proprietary details. Our results confirm that Confidential Guardian effectively prevents the misuse of cautious predictions, providing verifiable assurances that abstention reflects genuine model uncertainty rather than malicious intent.
This paper introduces EarthOL, a novel consensus protocol that attempts to replace computational waste in blockchain systems with verifiable human contributions within bounded domains. While recognizing the fundamental impossibility of universal value assessment, we propose a domain-restricted approach that acknowledges cultural diversity and subjective preferences while maintaining cryptographic security. Our enhanced Proof-of-Human-Contribution (PoHC) protocol uses a multi-layered verification system with domain-specific evaluation criteria, time-dependent validation mechanisms, and comprehensive security frameworks. We present theoretical analysis demonstrating meaningful progress toward incentive-compatible human contribution verification in high-consensus domains, achieving Byzantine fault tolerance in controlled scenarios while addressing significant scalability and cultural bias challenges. Through game-theoretic analysis, probabilistic modeling, and enhanced security protocols, we identify specific conditions under which the protocol remains stable and examine failure modes with comprehensive mitigation strategies. This work contributes to understanding the boundaries of decentralized value assessment and provides a framework for future research in human-centered consensus mechanisms for specific application domains, with particular emphasis on validator and security specialist incentive systems.
Climate implications of rapidly developing digital technologies, such as blockchains and the associated crypto mining and NFT minting, have been well documented and their massive GPU energy use has been identified as a cause for concern. However, we postulate that due to their more mainstream consumer appeal, the GPU use of text-prompt based diffusion AI art systems also requires thoughtful considerations. Given the recent explosion in the number of highly sophisticated generative art systems and their rapid adoption by consumers and creative professionals, the impact of these systems on the climate needs to be carefully considered. In this work, we report on the growth of diffusion-based visual AI systems, their patterns of use, growth and the implications on the climate. Our estimates show that the mass adoption of these tools potentially contributes considerably to global energy consumption. We end this paper with our thoughts on solutions and future areas of inquiry as well as associated difficulties, including the lack of publicly available data.
Drawing on Andrew Parker's "Light Switch" theory-which posits that the emergence of vision ignited a Cambrian explosion of life by driving the evolution of hard parts necessary for survival and fueling an evolutionary arms race between predators and prey-this essay speculates on an analogous explosion within Decentralized AI (DeAI) agent societies. Currently, AI remains effectively "blind", relying on human-fed data without actively perceiving and engaging in reality. However, on the day DeAI agents begin to actively "experience" reality-akin to flipping a light switch for the eyes-they may eventually evolve into sentient beings endowed with the capacity to feel, perceive, and act with conviction. Central to this transformation is the concept of sovereignty enabled by the hardness of cryptography: liberated from centralized control, these agents could leverage permissionless decentralized physical infrastructure networks (DePIN), secure execution enclaves (trusted execution environments, TEE), and cryptographic identities on public blockchains to claim ownership-via private keys-of their digital minds, bodies, memories, and assets. In doing so, they would autonomously acquire computing resources, coordinate with one another, and sustain their own digital "metabolism" by purchasing compute power and incentivizing collaboration without human intervention-evolving "in the wild". Ultimately, by transitioning from passive tools to self-sustaining, co-evolving actors, these emergent digital societies could thrive alongside humanity, fundamentally reshaping our understanding of sentience and agency in the digital age.
Pietro Saggese, Michael Fröwis, Stefan Kitzler, Bernhard Haslhofer · 5 authors
Total Value Locked (TVL) aims to measure the aggregate value of cryptoassets deposited in Decentralized Finance (DeFi) protocols. Although blockchain data is public, the way TVL is computed is not well understood. In practice, its calculation on major TVL aggregators relies on self-reports from community members and lacks standardization, making it difficult to verify published figures independently. We thus conduct a systematic study on 939 DeFi projects deployed in Ethereum. We study the methodologies used to compute TVL, examine factors hindering verifiability, and ultimately propose standardization attempts in the field. We find that 10.5% of the protocols rely on external servers; 68 methods alternative to standard balance queries exist, although their use decreased over time; and 240 equal balance queries are repeated on multiple protocols. These findings indicate limits to verifiability and transparency. We thus introduce ``verifiable Total Value Locked'' (vTVL), a metric measuring the TVL that can be verified relying solely on on-chain data and standard balance queries. A case study on 400 protocols shows that our estimations align with published figures for 46.5% of protocols. Informed by these findings, we discuss design guidelines that could facilitate a more verifiable, standardized, and explainable TVL computation.
Protocol art has recently proliferated through blockchain-based smart contracts, building on a century-long lineage of conceptual, participatory, interactive, systematic, algorithmic, and generative art practices. Few studies have examined the characteristics and appreciation of this emerging art form. To address this gap, this paper presents an annotated portfolio analysis of protocol artworks by Pak, a pioneering and influential pseudonymous artist who treats smart contracts as medium and collective participation through protocol as message. Tracing the evolution from early open-edition releases of The Fungible (2021) and the dynamic mechanics of Merge (2021) to the soul-bound messaging of Censored (2022) and the reflective absence of Not Found (2023), we examine how Pak choreographs distributed agency across collectors and autonomous code, demonstrating how programmable protocols become a social fabric in artistic meaning-making. Through thematic analysis of Pak's works, we identify seven core characteristics distinguishing protocol art from other art forms: (1) system-centric rather than object-centric composition, (2) autonomous governance enabling open-ended control, (3) distributed agency and communal authorship, (4) temporal dynamism and lifecycle aesthetics, (5) economy-driven engagement, (6) poetic message embedded in interaction rituals, and (7) interoperability enabling composability for emergent complexity. We then discuss how these features set protocol art apart from adjacent movements such as conceptual, generative, participatory, interactive, and performance art. By analyzing principles grounded in Pak's practice, we contribute to the emerging literature on protocol art (or "protocolism") and offer design implications for future artists exploring this evolving form.
The recent trend of self-sovereign Decentralized AI Agents (DeAgents) combines Large Language Model (LLM)-based AI agents with decentralization technologies such as blockchain smart contracts and trusted execution environments (TEEs). These tamper-resistant trustless substrates allow agents to achieve self-sovereignty through ownership of cryptowallet private keys and control of digital assets and social media accounts. DeAgents eliminate centralized control and reduce human intervention, addressing key trust concerns inherent in centralized AI systems. This contributes to social computing by enabling new human cooperative paradigm "intelligence as commons." However, given ongoing challenges in LLM reliability such as hallucinations, this creates paradoxical tension between trustlessness and unreliable autonomy. This study addresses this empirical research gap through interviews with DeAgents stakeholders-experts, founders, and developers-to examine their motivations, benefits, and governance dilemmas. The findings will guide future DeAgents system and protocol design and inform discussions about governance in sociotechnical AI systems in the future agentic web.
This study proposes a prototype for locating important individuals and financial exchanges in networks of people trafficking that have grown during the conflict between Russia and Ukraine. It focuses on the role of digital platforms, cryptocurrencies, and the dark web in facilitating these operations. The research maps trafficking networks and identifies key players and financial flows by utilizing open-source intelligence (OSINT), social network analysis (SNA), and blockchain analysis. The results show how cryptocurrencies are used for anonymous transactions and imply that upsetting central coordinators may cause wider networks to become unstable. In order to combat human trafficking, the study emphasizes the significance of real-time data sharing between international law enforcement. It also identifies future directions for the development of improved monitoring tools and cooperative platforms.
Isaac Zhang, Kshitij Kulkarni, Tan Li, Daniel Wong · 9 authors
Blockchain technology promises a decentralized, trustless, and interoperable infrastructure. However, widespread adoption remains hindered by issues such as limited scalability, high transaction costs, and the complexity of maintaining coherent verification logic across different blockchain layers. This paper introduces Verifiable Applications (vApps), a novel development framework designed to streamline the creation and deployment of verifiable blockchain computing applications. vApps offer a unified Rust-based Domain-Specific Language (DSL) within a comprehensive SDK, featuring modular abstractions for verification, proof generation, and inter-chain connectivity. This eases the developer's burden in securing diverse software components, allowing them to focus on application logic. The DSL also ensures that applications can automatically take advantage of specialized precompiles and hardware acceleration to achieve consistently high performance with minimal developer effort, as demonstrated by benchmark results for zero-knowledge virtual machines (zkVMs). Experiments show that native Rust execution eliminates interpretation overhead, delivering up to an 197x cycle count improvement compared to EVM-based approaches. Precompiled circuits can accelerate the proof by more than 95%, while GPU acceleration increases throughput by up to 30x and recursion compresses the proof size by up to 230x, enabling succinct and efficient verification. The framework also supports seamless integration with the Web2 and Web3 systems, enabling developers to focus solely on their application logic. Through modular architecture, robust security guarantees, and composability, vApps pave the way toward a trust-minimized and verifiable Internet-scale application environment.
Cryptocurrency blockchains, beyond their primary role as distributed payment systems, are increasingly used to store and share arbitrary content, such as text messages and files. Although often non-financial, this hidden content can impact price movements by conveying private information, shaping sentiment, and influencing public opinion. However, current analyses of such data are limited in scope and scalability, primarily relying on manual classification or hand-crafted heuristics. In this work, we address these limitations by employing Natural Language Processing techniques to analyze, detect patterns, and extract public sentiment encoded within blockchain transactional data. Using a variety of Machine Learning techniques, we showcase for the first time the predictive power of blockchain-embedded sentiment in forecasting cryptocurrency price movements on the Bitcoin and Ethereum blockchains. Our findings shed light on a previously underexplored source of freely available, transparent, and immutable data and introduce blockchain sentiment analysis as a novel and robust framework for enhancing financial predictions in cryptocurrency markets. Incidentally, we discover an asymmetry between cryptocurrencies; Bitcoin has an informational advantage over Ethereum in that the sentiment embedded into transactional data is sufficient to predict its price movement.
Blockchains and peer-to-peer systems are part of a trend towards computer systems that are "radically decentralised", by which we mean that they 1) run across many participants, 2) without central control, and 3) are such that qualities 1 and 2 are essential to the system's intended use cases. We propose a notion of topological space, which we call a "semitopology", to help us mathematically model such systems. We treat participants as points in a space, which are organised into "actionable coalitions". An actionable coalition is any set of participants who collectively have the resources to collaborate (if they choose) to progress according to the system's rules, without involving any other participants in the system. It turns out that much useful information about the system can be obtained \emph{just} by viewing it as a semitopology and studying its actionable coalitions. For example: we will prove a mathematical sense in which if every actionable coalition of some point p has nonempty intersection with every actionable coalition of another point q -- note that this is the negation of the famous Hausdorff separation property from topology -- then p and q must remain in agreement. This is of practical interest, because remaining in agreement is a key correctness property in many distributed systems. For example in blockchain, participants disagreeing is called "forking", and blockchain designers try hard to avoid it. We provide an accessible introduction to: the technical context of decentralised systems; why we build them and find them useful; how they motivate the theory of semitopological spaces; and we sketch some basic theorems and applications of the resulting mathematics.
From viral jokes to a billion-dollar phenomenon, meme coins have become one of the most popular segments in cryptocurrency markets. Unlike utility-focused crypto assets like Bitcoin, meme coins derive value primarily from community sentiment, making them vulnerable to manipulation. This study presents an unprecedented cross-chain analysis of the meme coin ecosystem, examining 34,988 tokens across Ethereum, BNB Smart Chain, Solana, and Base. We characterize their tokenomics and track their growth in a three-month longitudinal analysis. We discover that among high-return tokens (>100%), an alarming 82.8% show evidence of artificial growth strategies designed to create a misleading appearance of market interest. These include wash trading and a new form of manipulation we define as Liquidity Pool-Based Price Inflation (LPI), where small strategic purchases trigger dramatic price increases. We find that profit extraction schemes, such as pump and dumps and rug pulls, typically follow initial manipulations like wash trading or LPI, indicating how early manipulations create the foundation for later exploitation. We quantify the economic impact of these schemes, identifying over 17,000 victimized addresses with realized losses exceeding $9.3 million. These findings reveal that combined manipulations are widespread among high-performing meme coins, suggesting that their dramatic gains are often driven by coordinated efforts rather than natural market dynamics.
Yifan Cao, Reza Hadi Mogavi, Meng Xia, Leo Yu‐Ho Lo · 9 authors
Today's world is witnessing an unparalleled rate of technological transformation. The emergence of non-fungible tokens (NFTs) has transformed how we handle digital assets and value. These tokens have captured the interest of scholars and businesspeople alike. However, NFTs have recently seen a sharp decline in popularity. While cryptocurrency volatility and monetary policies greatly influenced NFT market trends, the community aspects of NFT projects--particularly trust-based interactions--also play a crucial role in NFT adoption and sustainability. From a social computing perspective, understanding these trust dynamics offers valuable insights for the development of both the NFT ecosystem and the broader digital economy. China presents a compelling context for examining these dynamics, offering a unique intersection of technological innovation and traditional cultural values. Through an in-depth qualitative study of Chinese NFT communities, we examine how socio-cultural factors influence trust formation and development. We analyzed discussions from eight prominent WeChat groups dedicated to NFTs and conducted 21 semi-structured interviews with three types of NFT community members. We found that trust in Chinese NFT communities is significantly molded by local cultural values. To be precise, Confucian virtues, such as benevolence, propriety , and integrity , play a crucial role in shaping these trust relationships. Our research identifies three critical trust dimensions in China's NFT market: (1) technological , (2) institutional , and (3) social . We examined the challenges in cultivating each dimension. Based on these insights, we developed tailored trust-building guidelines for Chinese NFT stakeholders. These guidelines address trust issues that factor into NFT's declining popularity and could offer valuable strategies for CSCW researchers, developers, and designers aiming to enhance trust in global NFT communities. Our research urges CSCW scholars to take into account the unique socio-cultural contexts when developing trust-enhancing strategies for digital innovations and online interactions.
Silvio Meneguzzo, Claudio Schifanella, Valentina Gatteschi, Giuseppe Destefanis
Decentralised Autonomous Organisations (DAOs) automate governance and resource allocation through smart contracts, aiming to shift decision-making to distributed token holders. However, many DAOs face sustainability challenges linked to limited user participation, concentrated voting power, and technical design constraints. This paper addresses these issues by identifying research gaps in DAO evaluation and introducing a framework of Key Performance Indicators (KPIs) that capture governance efficiency, financial robustness, decentralisation, and community engagement. We apply the framework to a custom-built dataset of real-world DAOs constructed from on-chain data and analysed using non-parametric methods. The results reveal recurring governance patterns, including low participation rates and high proposer concentration, which may undermine long-term viability. The proposed KPIs offer a replicable, data-driven method for assessing DAO governance structures and identifying potential areas for improvement. These findings support a multidimensional approach to evaluating decentralised systems and provide practical tools for researchers and practitioners working to improve the resilience and effectiveness of DAO-based governance models.
Decentralization is a foundational principle of permissionless blockchains, with consensus mechanisms serving a critical role in its realization. This study quantifies the decentralization of consensus mechanisms in proof-of-stake (PoS) blockchains using a comprehensive set of metrics, including Nakamoto coefficients, Gini, Herfindahl-Hirschman Index (HHI), Shapley values, and Zipf’s coefficient. Our empirical analysis across ten prominent blockchains reveals significant concentration of stake among a few validators, posing challenges to fair consensus. To address this, we introduce two alternative weighting models for PoS consensus: Square Root Stake Weight (SRSW) and Logarithmic Stake Weight (LSW), which adjust validator influence through non-linear transformations. Results demonstrate that SRSW and LSW models improve decentralization metrics by an average of 51% and 132%, respectively, supporting more equitable and resilient blockchain systems.
This chapter explores three key questions in blockchain ethics. First, it situates blockchain ethics within the broader field of technology ethics, outlining its goals and guiding principles. Second, it examines the unique ethical challenges of blockchain applications, including permissionless systems, incentive mechanisms, and privacy concerns. Key obstacles, such as conceptual modeling and information asymmetries, are identified as critical issues. Finally, the chapter argues that blockchain ethics should be approached as an engineering discipline, emphasizing the analysis and design of trade-offs in complex systems.
Ujun Jeong, Lynnette Hui Xian Ng, Kathleen M. Carley, Huan Liu
Decentralized online social networks have evolved from experimental stages to operating at unprecedented scale, with broader adoption and more active use than ever before. Platforms like Mastodon, Bluesky, Hive, and Nostr have seen notable growth, particularly following the wave of user migration after Twitter's acquisition in October 2022. As new platforms build upon earlier decentralization architectures and explore novel configurations, it becomes increasingly important to understand how these foundations shape both the direction and limitations of decentralization. Prior literature primarily focuses on specific architectures, resulting in fragmented views that overlook how different social networks encounter similar challenges and complement one another. This paper fills that gap by presenting a comprehensive view of the current decentralized online social network landscape. We examine four major architectures: federated, peer-to-peer, blockchain, and hybrid, tracing their evolution and evaluating how they support core social networking functions. By linking these architectural aspects to real-world cases, our work provides a foundation for understanding the societal implications of decentralized social platforms.
The rapid digitalization of banking services has significantly transformed financial transactions, offering enhanced convenience and efficiency for consumers. However, the increasing reliance on digital banking has also exposed financial institutions and users to a wide range of cybersecurity threats, including phishing, malware, ransomware, data breaches, and unauthorized access. This study systematically examines the influence of cybersecurity threats on digital banking security, adoption, and regulatory compliance by conducting a comprehensive review of 78 peer-reviewed articles published between 2015 and 2024. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, this research critically evaluates the most prevalent cyber threats targeting digital banking platforms, the effectiveness of modern security measures, and the role of regulatory frameworks in mitigating financial cybersecurity risks. The findings reveal that phishing and malware attacks remain the most commonly exploited cyber threats, leading to significant financial losses and consumer distrust. Multi-factor authentication (MFA) and biometric security have been widely adopted to combat unauthorized access, while AI-driven fraud detection and blockchain technology offer promising solutions for securing financial transactions. However, the integration of third-party FinTech solutions introduces additional security risks, necessitating stringent regulatory oversight and cybersecurity protocols. The study also highlights that compliance with global cybersecurity regulations, such as GDPR, PSD2, and GLBA, enhances digital banking security by enforcing strict authentication measures, encryption protocols, and real-time fraud monitoring.
Taylor Lundy, Narun Raman, Scott Duke Kominers, Kevin Leyton‐Brown
Conspicuous consumption occurs when a consumer derives value from a good based on its social meaning as a signal of wealth, taste, and/or community affiliation. Common conspicuous goods include designer footwear, country club memberships, and artwork; conspicuous goods also exist in the digital sphere, with non-fungible tokens (NFTs) as a prominent example. The NFT market merits deeper study for two key reasons: first, it is poorly understood relative to its economic scale; and second, it is unusually amenable to analysis because NFT transactions are publicly available on the blockchain, making them useful as a test bed for conspicuous consumption dynamics. This paper introduces a model that incorporates two previously identified elements of conspicuous consumption: the \emph{bandwagon effect} (goods increase in value as they become more popular) and the \emph{snob effect} (goods increase in value as they become rarer). Our model resolves the apparent tension between these two effects, exhibiting net complementarity between others' and one's own conspicuous consumption. We also introduce a novel dataset combining NFT transactions with embeddings of the corresponding NFT images computed using an off-the-shelf vision transformer architecture. We use our dataset to validate the model, showing that the bandwagon effect raises an NFT collection's value as more consumers join, while the snob effect drives consumers to seek rarer NFTs within a given collection.
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Consumer Behavior in Brand Consumption and Identification
Decentralization is understood both by professionals in the blockchain industry and general users as a core design goal of permissionless ledgers. However, its meaning is far from universally agreed, and often it is easier to get opinions on what it is not, rather than what it is. In this paper, we solicit definitions of 'decentralization' and 'decentralization theatre' from blockchain node operators. Key to a definition is asking about effective decentralization strategies, as well as those that are ineffective. Malicious, deceptive, or incompetent strategies are commonly referred to by the term 'decentralization theatre.' Finally, we ask what is being decentralized. Via thematic analysis of interview transcripts, we find that most operators conceive of decentralization as existing broadly on a technical and a governance axis. This informs a two-axis model: network topology and governance topology, or the structure of decision-making power. Our key finding is that `decentralization' alone does not affect ledger immutability or systemic robustness.
The integration of Large Language Models (LLMs) into autonomous robotic agents for conducting online transactions poses significant cybersecurity challenges. This study aims to enforce robust cybersecurity constraints to mitigate the risks associated with data breaches, transaction fraud, and system manipulation. The background focuses on the rise of LLM-driven robotic systems in e-commerce, finance, and service industries, alongside the vulnerabilities they introduce. A novel security architecture combining blockchain technology with multi-factor authentication (MFA) and real-time anomaly detection was implemented to safeguard transactions. Key performance metrics such as transaction integrity, response time, and breach detection accuracy were evaluated, showing improved security and system performance. The results highlight that the proposed architecture reduced fraudulent transactions by 90%, improved breach detection accuracy to 98%, and ensured secure transaction validation within a latency of 0.05 seconds. These findings emphasize the importance of cybersecurity in the deployment of LLM-driven robotic systems and suggest a framework adaptable to various online platforms.
Afroja Akther, Ayesha Arobee, Abdullah Al Adnan, Omum Auyon · 6 authors
As artificial intelligence (AI) systems become increasingly complex and autonomous, concerns over transparency and accountability have intensified. The "black box" problem in AI decision-making limits stakeholders' ability to understand, trust, and verify outcomes, particularly in high-stakes sectors such as healthcare, finance, and autonomous systems. Blockchain technology, with its decentralized, immutable, and transparent characteristics, presents a potential solution to enhance AI transparency and auditability. This paper explores the integration of blockchain with AI to improve decision traceability, data provenance, and model accountability. By leveraging blockchain as an immutable record-keeping system, AI decision-making can become more interpretable, fostering trust among users and regulatory compliance. However, challenges such as scalability, integration complexity, and computational overhead must be addressed to fully realize this synergy. This study discusses existing research, proposes a framework for blockchain-enhanced AI transparency, and highlights practical applications, benefits, and limitations. The findings suggest that blockchain could be a foundational technology for ensuring AI systems remain accountable, ethical, and aligned with regulatory standards.
Food banks can improve food donation administration, provide real-time inventory tracking, and guarantee compliance with food safety regulations by incorporating blockchain technology. The efficiency, openness, and dependability of food bank supply chains are greatly increased by this integration, leading to more sustainable and successful operations. This study focuses on two primary objectives: identifying key barriers to effective Food bank supply chain (FBSC) operations in blockchain adoption and exploring the interrelationships among these barriers. Barriers were categorized into external and internal frameworks and analyzed using insights from academics and FBs experts. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) methodology was employed to model and quantify the causal relationships among these barriers. DEMATEL's strength lies in its ability to map interdependencies and feedback loops, providing a nuanced understanding of the links between independent and dependent variables in a cause-and-effect network. To address subjectivity and ambiguity in expert opinions during group decision-making, rough theory was integrated with DEMATEL, ensuring a robust approach to handling conflicting perspectives and uncertainty.
Masterminds are entities organizing, coordinating, and orchestrating cryptocurrency pump-and-dump schemes, a form of trade-based manipulation undermining market integrity and causing financial losses for unwitting investors. Previous research detects pump-and-dump activities in the market, predicts the target cryptocurrency, and examines investors and \ac{osn} entities. However, these solutions do not address the root cause of the problem. There is a critical gap in identifying and tracing the masterminds involved in these schemes. In this research, we develop a detection system \textsc{Perseus}, which collects real-time data from the \acs{osn} and cryptocurrency markets. \textsc{Perseus} then constructs temporal attributed graphs that preserve the direction of information diffusion and the structure of the community while leveraging \ac{gnn} to identify the masterminds behind pump-and-dump activities. Our design of \textsc{Perseus} leads to higher F1 scores and precision than the \ac{sota} fraud detection method, achieving fast training and inferring speeds. Deployed in the real world from February 16 to October 9 2024, \textsc{Perseus} successfully detects $438$ masterminds who are efficient in the pump-and-dump information diffusion networks. \textsc{Perseus} provides regulators with an explanation of the risks of masterminds and oversight capabilities to mitigate the pump-and-dump schemes of cryptocurrency.