Andrew Isaak, Baris Istipliler, Suleika Bort, Michael Woywode
Decentralized autonomous organizations (DAOs) represent a novel organizational form enabling self-governed coordination based on blockchain technology. This study examines the prototypical Bitcoin DAO from an institutional perspective, focusing on how its core features—decentralization and autonomy—interact with the broader institutional framework in which it operates. Specifically, we study how regulative institutional environments (i.e., (il)legalization) shape the growth and development of DAOs while theorizing about the role of both petty and grand corruption (i.e., by higher-level officials) in influencing the effectiveness of these regulative institutions. Our empirical analysis focuses on the global rise of Bitcoin trading and platform establishment across 49 national contexts from 2011 to 2023. Utilizing a unique data set, we find that, although the number of Bitcoin exchange platforms in a country is positively associated with Bitcoin legalization, Bitcoin trading volume is positively associated with Bitcoin illegalization. In countries with higher levels of grand corruption, Bitcoin illegalization becomes even more strongly associated with trading. In contrast, grand corruption dampens the positive association between legalization and the number of Bitcoin exchange platforms. Further, the presence of petty corruption reduces the impact of grand corruption. Our study reveals that it is critical to distinguish between petty and grand corruption as an important factor that influences the interplay between the regulative environment and growth and development of the Bitcoin DAO and the related ecosystem of Bitcoin trading and platform founding.
This article critically examines the adoption of Bitcoin as legal tender in El Salvador, contextualising it within the legacy of official dollarisation after 2001. First, we empirically assess the benefits and costs of dollarisation, finding that, despite some theoretical claims, the benefits remain questionable in hindsight, while the costs for the country were relatively low. Second, we explore Bitcoin's role as legal tender, proposing its understanding as a form of International Money and its potential in facilitating remittances. Building on this, we show that the existing dollarisation and a ‘soft adoption’ of Bitcoin contributed to a comparatively low risk and low associated costs of introducing Bitcoin as a second legal tender. Third, we situate these developments within the broader geopolitical context, where the global monetary and financial system and the hegemony of the USD (the current World Money) are increasingly being repoliticised. In this light, the adoption of Bitcoin can be seen as a trial-and-error, unsuccessful at best, attempt by the Salvadoran government to enhance its leverage, improve remittance flows, and provide a low-cost escape valve in an evolving global landscape.
Felipe Mello Fonseca, M.F.S.F. de Moura, Pedro Henrique González, Diogo Mendonça
A Blockchain é uma tecnologia inovadora aplicada em diversas áreas como finanças, gestão de registros, votação eletrônica e jogos. As transações em blockchain são frequentemente executadas por smart contracts, código considerado crítico em termos de segurança. Existem diversas ferramentas que se propõe a identificar vulnerabilidades de forma automatizada em smart contracts, contudo, conforme estudos anteriores mostram, a eficácia nesta tarefa é normalmente baixa. Desse modo, identificar vulnerabilidades de forma automatizada em smart contracts continua sendo um grande desafio. Neste estudo investigamos a evolução de duas importantes ferramentas para detecção de vulnerabilidades em smart contracts: Mythril e Slither, avaliando sua eficácia na identificação de vulnerabilidades e se evoluíram comparadas a uma versão anterior. Para isto, executamos as ferramentas com versões mais rescentes em um conjunto de 69 smart contracts previamente analisados em um estudo anterior. Os resultados foram comparados com as vulnerabilidades já classificadas e submetidos a uma validação manual para aferir sua precisão. Os experimentos demonstram que Mythril apresentou melhorias na redução de falsos positivos, enquanto Slither aprimorou a detecção de falhas relacionadas a Access Control. Contudo, as ferramentas apresentaram limitações na sua evolução para alguns tipos de vulnerabilidades. Esses achados reforçam a necessidade contínua de aprimoramento e avaliação das ferramentas de análise automatizada de segurança em smart contracts.
Decentralized Finance (DeFi) has revolutionized financial transactions by enabling open, permissionless access to financial services. However, its lack of centralized oversight and pseudonymous architecture have also brought by fraudulent activities. This study presents a novel framework for fraud detection in DeFi that integrates graph neural networks (GNNs) with multi-agent reinforcement learning (MARL). Leveraging a directed transaction graph comprising 50,000 Ethereum addresses and over 120,000 token transfers, this paper evaluates four detection pipelines: extreme gradient-boosted decision trees (XGBoost), a GNN-only model (GCN), a standalone reinforcement learning agent (PPO), and a proposed GNN+RL hybrid model. The hybrid system combines graph-based embeddings with adversarial policy learning, where a fraudster and a detector co-evolve through a multi-agent PPO setup using PettingZoo’s ParallelEnv. Synthetic fraud strategies are generated using a GAN and projected into the GCN embedding space to simulate adaptive threats. Experimental results show that while GCNs outperform flat-feature models, the GNN+RL hybrid achieves superior balance across accuracy (84.58%), AUC (0.8176), and F1 score (0.7493), capturing both structural and behavioral fraud signals. Reward convergence curves further illustrate emergent adversarial dynamics. The proposed framework demonstrates the effectiveness of combining relational inductive biases, dynamic decision-making, and adversarial augmentation for resilient fraud detection. Future work includes extending to cross-chain analytics and enriching contextual understanding through integration with large language models.
In an increasingly digitalized and hyperconnected financial landscape, the complexity and frequency of cyber threats have grown exponentially, exposing financial institutions to real-time risks that conventional defense mechanisms struggle to mitigate.Traditional security frameworks, often reactive and siloed, lack the speed and contextual awareness required to protect dynamic finance ecosystems driven by automated trading, open banking, and decentralized financial services.This paper explores the emerging paradigm of Integrative Analytics for Autonomous Threat Response (IAATR)-a strategic synthesis of artificial intelligence (AI), behavioral modeling, and real-time analytics to secure business processes within finance ecosystems.From a broad perspective, the integration of AI into cybersecurity presents transformative possibilities.Machine learning models trained on network telemetry, user behavior, and transaction anomalies can detect threats proactively, adapt to novel attack patterns, and initiate countermeasures with minimal human intervention.The paper discusses how autonomous systemsrooted in deep reinforcement learning and explainable AI-enhance threat triage, isolate compromised processes, and orchestrate secure workflow rerouting to minimize systemic disruption.Narrowing the focus to finance-specific applications, the paper examines use cases including algorithmic fraud detection, insider threat mitigation in payment systems, and AI-enabled compliance monitoring.Emphasis is placed on the design of feedback loops between security intelligence layers and business process management (BPM) engines, ensuring that threat responses remain aligned with regulatory standards and operational continuity.The study concludes with a discussion on governance, ethical risks, and the role of digital trust in advancing AI-secured business environments.IAATR represents not just a technological leap, but a foundational shift toward anticipatory, resilient financial security architectures.
Money laundering with Cryptocurrency in Indonesia in the use of digital currencies provides a loophole for criminals to hide the results of criminal acts through money laundering practices. This article reviews various strategies used in money laundering using crypto assets, with the aim of providing a deeper understanding of this issue. This research applies the normative study method by analyzing legal aspects based on literature as well as the latest developments related to money laundering and cryptocurrencies. Money laundering through crypto assets is carried out in order to disguise the source of illegal funds. Some of the commonly used methods include transactions over the dark network as well as the use of unlicensed mixing services. This crime has been regulated in various laws and regulations that aim to prevent and eradicate the practice of money laundering through cryptocurrencies.
Jiaxin Wang, Qian’ang Mao, Hongliang Sun, Jiaqi Yan
With the development of blockchain technology, crypto gambling has gained popularity due to its high level of anonymity. However, similar to traditional casinos, crypto casinos are controlled by a few internal Delegatees, making it impossible for them to achieve complete transparency and fairness. These delegatees are hidden among gamblers and are difficult to identify and distinguish in anonymous and large-scale blockchain transaction networks. This paper proposes an unsupervised dual-stage role identification method to adaptively identify key roles and hidden delegatees in label-sparse crypto casinos. Specifically, inspired by voting-style transaction patterns, we propose a novel voting influence metric for key node identification. This metric is based on one-dimensional structural entropy to capture global dissemination capability. Subsequently, we develop a multi-view graph neural network framework enhanced with two-dimensional global structural entropy minimization and self-supervised contrastive learning to improve the robustness and interpretability of hidden role partitioning. Experiments on real-world cases of the most mainstream blockchains-Ethereum, TRON, and Arbitrum-demonstrate that our proposed method effectively reveals distinct role compositions and collusion patterns, distinguishing between gamblers and delegatees. Our results achieve a higher match with identities confirmed by judicial authorities than existing methods, indicating the effectiveness and generalizability of our approach in enhancing security and regulation oversight.
Muhammad Muzammil, Abisheka Pitumpe, Xigao Li, Amir Rahmati · 5 authors
Governments and regulatory bodies have recognized investment scams as a prevalent form of cryptocurrency fraud. These scams typically use professional-looking websites to lure unsuspecting victims with promises of unrealistically high returns. In this paper, we introduce Crimson, a distributed system designed to continuously detect cryptocurrency investment scam websites as they are created in the wild. During the first 8 months of 2024, Crimson processed approximately 6 billion domain names and classified 43,572 unique cryptocurrency investment scam websites in real-time. Beyond detection, we provide insights into the design and infrastructure of these websites that can help users recognize scam patterns and assist hosting providers in detecting and blocking such sites. Furthermore, we investigate the inclusion of our detected scam websites in block-lists used by popular web browsers and applications, finding that the vast majority of these websites were absent. On the financial side, by analyzing the transactions incoming to scammer wallets on 6.7% of the sites detected by Crimson, we observe an estimated lower bound of 2.04M USD in losses due to cryptocurrency investment scams.
Noor Ul Ain Afzal, Muhammad Kamran Abid, Muhammad Fuzail, Naeem Aslam · 5 authors
Ponzi schemes have surfaced on the Ethereum platform as blockchain technology continues to gain traction. Using smart contracts, these schemes, also referred to as smart Ponzi schemes, have caused significant financial losses and adverse effects. Byte code features, op code characteristics, account qualities, and smart contract transaction behavior are the main focus areas for current Ethereum smart Ponzi scheme detection techniques. However, these methods often do not record the behavioral features of the Ponzi scheme, resulting in high false alarm rates and poor identification accuracy. In this study, we provide the source P. Source P is a unique way of knowing intelligent Ponzi schemes on the Ethereum platform, passed by dataflow. Using the intelligent contract's source code as a function eliminates the difficulty of collecting data and extracting functions from available identification methods. In particular, we convert the code into statistical flow diagrams, apply educated models, and use code representations to create classification models for the detection of Ponzi schemes. Experimental results show that SourceP outperforms cutting-edge technology in terms of sustainability and effectiveness, achieving an F1 score of 92.4% and a recall of 90.1% in Ethereum's smart Ponzi schema detection. Ponzi, Blockchain, Source Code, Intelligent Contracts.
S.H. Anak Agung Alit Satya Prananda, Kadek Januarsa Adi Sudharma
This research examines the regulations and law enforcement efforts concerning the use of cryptocurrency as a money laundering tool in both Indonesia and the United States. Using normative legal research methods and a comparative approach, the study compares the legal frameworks of the two countries. In the United States, agencies such as FinCEN, IRS, and SEC play a critical role in enforcing laws against the use of cryptocurrencies for money laundering, with comprehensive laws and sophisticated enforcement mechanisms. Meanwhile, Indonesia relies on BAPPEBTI to oversee and regulate cryptocurrency activities. Although Indonesia’s legal framework may not be as extensive as the United States', the country has taken significant steps, such as adopting the "Travel Rule" to monitor cryptocurrency transactions. However, both countries face a common challenge: the anonymity offered by cryptocurrencies, which complicates investigations into money laundering. To address this challenge, both countries require more detailed regulations and enhanced international cooperation to effectively combat the misuse of cryptocurrencies for money laundering. The research suggests that strengthening legal measures and improving global collaboration are essential to mitigate the risks associated with cryptocurrency-based financial crimes.
Cryptocurrencies have revolutionized digital finance, offering decentralization, anonymity, and cross-border transactions. However, these very attributes have also facilitated money laundering, posing significant challenges for regulators. This paper examines the risks associated with cryptocurrency in relation to money laundering, emphasizing India’s legal and regulatory framework. It discusses the role of the Prevention of Money Laundering Act (PMLA), the Reserve Bank of India (RBI) directives, and recent policy developments concerning digital assets. Additionally, the paper explores international regulatory frameworks and suggests policy measures to strengthen anti-money laundering (AML) mechanisms in India.
Cryptocurrencies and blockchain technology have revolutionized the financial sector, offering decentralized, secure, and efficient transaction mechanisms. However, these innovations have also introduced new challenges, particularly in the realm of financial crimes such as money laundering, illicit trade, and fraud. This paper explores the dual-use nature of cryptocurrencies, examining their potential for both financial innovation and criminal exploitation, with over $20 billion in illicit transactions recorded in 2023 (Chainalysis, 2023). By reviewing case studies, regulatory responses, and technological solutions, this paper provides a comprehensive analysis of the risks and opportunities presented by cryptocurrencies and blockchain technology. Current regulatory frameworks, such as the EU’s MiCA Regulation (2023) and FATF recommendations and guidelines, have significantly influenced cryptocurrency adoption by balancing innovation with risk mitigation. The paper concludes with actionable recommendations for enhancing regulatory frameworks, fostering international cooperation, leveraging AI and other technological advancements, and creating educational initiatives to mitigate financial crimes in the digital age.
José Juan de León, Cenchuan Zhang, Christos - Spyridon Koulouris, Francesca Medda · 5 authors
The growing interest in decentralized finance (DeFi), driven by advancements in blockchain technologies such as Ethereum, highlights the crucial role of smart contracts. However, the inherent openness of blockchains creates an extensive attack surface, exposing participants’ funds to undetected security flaws. In this work we investigated the use of deep reinforcement learning techniques, specifically Deep Q-Network (DQN) and Proximal Policy Optimization (PPO), for detecting and classifying vulnerabilities in smart contracts. This approach utilizes control flow graphs (CFGs) generated through EtherSolve to capture the semantic features of contract bytecode, enabling the reinforcement learning models to recognize patterns and make more accurate predictions. Experimental results from extensive public datasets of smart contracts revealed that the PPO model performs better than DQN and demonstrates effectiveness in identifying unchecked-call vulnerability. The PPO model exhibits more stable and consistent learning patterns and achieves higher overall rewards. This research introduces a machine learning method for enhancing smart contract security, reducing financial risks for users, and contributing to future developments in reinforcement learning applications.
Chun-Wing Poon, James T. Kwok, Calvin Chow, Jieun Choi
Anti-money laundering (AML) systems are important for protecting the global economy. However, conventional rule-based methods rely on domain knowledge, leading to suboptimal accuracy and a lack of scalability. Graph neural networks (GNNs) for digraphs (directed graphs) can be applied to transaction graphs and capture suspicious transactions or accounts. However, most spectral GNNs do not naturally support multi-dimensional edge features, lack interpretability due to edge modifications, and have limited scalability owing to their spectral nature. Conversely, most spatial methods may not capture the money flow well. Therefore, in this work, we propose LineMVGNN (Line-Graph-Assisted Multi-View Graph Neural Network), a novel spatial method that considers payment and receipt transactions. Specifically, the LineMVGNN model extends a lightweight MVGNN module, which performs two-way message passing between nodes in a transaction graph. Additionally, LineMVGNN incorporates a line graph view of the original transaction graph to enhance the propagation of transaction information. We conduct experiments on two real-world account-based transaction datasets: the Ethereum phishing transaction network dataset and a financial payment transaction dataset from one of our industry partners. The results show that our proposed method outperforms state-of-the-art methods, reflecting the effectiveness of money laundering detection with line-graph-assisted multi-view graph learning. We also discuss scalability, adversarial robustness, and regulatory considerations of our proposed method.
This paper explores decentralized finance (DeFi), a fast-growing area powered by blockchain technology that offers a new alternative to traditional financial systems. DeFi removes the need for intermediaries like banks, making transactions more transparent, accessible, and often cheaper. This shift not only reduces costs but also helps improve financial access, particularly for people who are underserved by traditional banking systems. Key elements of DeFi, such as smart contracts and oracles, play a central role in automating processes and enabling peer-to-peer exchanges without needing middlemen. Despite its advantages, DeFi faces several challenges. Smart contracts can have security vulnerabilities, oracles may not always provide accurate data, and there is little consumer protection in place, which raises risks for users. Furthermore, DeFi's decentralized and often anonymous structure creates regulatory difficulties, especially when it comes to complying with anti-money laundering (AML) and know-your-customer (KYC) standards, which are crucial for ensuring financial safety and preventing illegal activities. This paper examines these issues and proposes potential solutions, such as decentralized oracle networks, regulatory tools embedded within DeFi platforms, and improved scalability techniques. These solutions aim to enhance DeFi's security while maintaining its core decentralized benefits. The paper concludes by discussing the future of DeFi, stressing the importance of balanced regulations that protect users without stifling innovation. Ultimately, DeFi holds the potential to reshape global finance, making it more inclusive, efficient, and accessible.
Abstract Anti-money laundering has been an issue in our society from the beginning of time. It simply refers to certain regulations and laws set by the government to uncover illegal money, which is passed as legal income. Now, with the emergence of cryptocurrency, it ensures pseudonymity for users. Cryptocurrency is a type of currency that is not authorized by the government and does not exist physically but only on paper. This provides a better platform for criminals for their illicit transactions. New algorithms have been proposed to detect illicit transactions. Machine learning and deep learning algorithms give us hope in identifying these anomalies in transactions. We have selected the Elliptic Bitcoin Dataset. This data set is a graph data set generated from an anonymous blockchain. Each transaction is mapped to real entities with two categories: licit and illicit. Some of them are not labeled. We have run different algorithms for predicting illicit transactions like Logistic Regression, Long Short Term Memory, Support Vector Machine, Random Forest, and a variation of Graph Neural Networks, which is called Graph Convolution Network (GCN). GCN is of special interest in our case. Different evaluation parameters such as accuracy, ROC and F1 score are analyzed for different models. Our experimental results show that the proposed GCN model gives the accuracy $$98.5\%$$ , the AUC 0.9444 and the RMSE 0.1123, which concludes that our GCN is better than the existing models, in particular with the model proposed in Weber et al. (Anti-money laundering in bitcoin: experimenting with graph convolutional networks for financial forensics, 2019. http://arxiv.org/abs/1908.02591 ).
The modern global financial environment faces a complex combination of requirements associated with ensuring systemic solvency while preventing the use of banks as conduits for illegal financial transactions. The current paper focuses on evaluating the capacity of modern regulatory standards for addressing these interconnected challenges. While modern legislation and regulatory approaches have reached a new level of sophistication and standardization, the dynamic nature of innovations in the field of decentralized finance integrate specific examples of Explainable AI (XAI) tools like SHAP values or Grad-CAM that regulators are currently using to improve transparency in decentralized finance. A qualitative-comparative methodology is employed for exploring the impact of strict enforcement of financial standards on the sustainability of the banking sector. Using case studies drawn from some of the world's largest economies, such as the EU, the US, and India, the study finds that despite the positive impact of regulations on the core of the global economy (e.g., through enhancing the financial cushioning of banks), there is evidence that the displacement effect has occurred, which means that risks and illegal activities continue to be relocated to the shadow economy. From the policy implications, a shift from a response-oriented and rule-based approach to one that is proactive and intelligence-based, emphasizing globalization and integration, becomes evident. For future regulation, there is a need for the coverage to be extended to non-bank financial institutions as well as dealing with the paradox of compliance whereby escalating costs have not yet translated into less global money laundering.
Abstract Smart contracts and blockchain technology have revolutionized our transactions and interactions with digital systems, yet their vulnerabilities can lead to devastating consequences such as financial losses, data breaches, and compromised system integrity. Existing detection methods, including static analysis, dynamic analysis, and machine learning-based approaches, have their limitations, such as requiring large amounts of labeled data or being computationally expensive. To address these limitations, we propose a novel approach that leverages a One-Class Variational Autoencoder (VAE) with CodeBERT for data pre-processing to detect vulnerabilities in smart contracts. Our approach achieved a higher F1 score (88.93%) compared to the baselines evaluated, even when labeled data is limited. This paper contributes to the development of effective and efficient vulnerability detection methods, ultimately enhancing the security and reliability of smart contracts and blockchain-based systems. By demonstrating superior performance in imbalanced data scenarios, our method offers a practical solution for real-world applications in blockchain security.
We discover a novel flight-to-safety (FTS) effect from cryptocurrency markets to stock markets, triggered by a series of hacking attacks on cryptocurrency exchanges. This phenomenon is driven by heightened uncertainty, which increases investors’ risk awareness and prompts asset reallocation in favour of safer stock markets over riskier cryptocurrency markets. We conduct an extensive global examination of this effect across 39 countries and confirm this novelty. This effect is amplified by frequent attacks when investors’ risk awareness is strengthened. Notably, social media sentiment surrounding these attacks serves as both a timely warning indicator for upcoming hacking events and a measure of the FTS pressure following such attacks. We conclude that the collapsed investor confidence and increased risk aversion are the primary cause of such an effect. We further substantiate the FTS hypothesis by offering evidence of significant abnormal fund flows into US mutual funds following these hacking events. As such, through the lens of cyber attacks, we document how a shock in cryptocurrency markets is transmitted into stock markets via investors’ FTS behaviour. • We discover a flight-to-safety (FTS) effect from cryptocurrency to stock markets. • The FTS effect is amplified by more frequent cyberattacks. • Social media sentiment can warn upcoming hacking events and measure FTS pressure. • The FTS is driven by collapsing investor confidence and heightened risk aversion. • Evidence from US mutual fund supports our novel FTS effect.
Abstract Since its introduction as a decentralized digital currency for peer-to-peer transactions, Bitcoin’s role in financial markets has undergone significant evolution. We employ bibliometric analysis to explore research trends in Bitcoin, identifying two primary perspectives in the recent financial economic literature: Bitcoin as a speculative asset and as a safe-haven asset. The speculative nature of Bitcoin is evident through its high volatility and frequent price jumps, largely influenced by rapid shifts in investor sentiment and attention, which create both risks and opportunities for traders. Conversely, Bitcoin exhibits characteristics of a safe-haven asset due to its asymmetric tail dependence and negative correlation within certain asset classes.
The increasing adoption of blockchain technology has led to a surge in financial fraud, including money laundering, Ponzi schemes, and illicit fund transfers. Traditional fraud detection techniques, such as rule-based systems and supervised machine learning models, struggle to handle the high-volume, high-velocity, and dynamically evolving nature of blockchain transactions. These limitations necessitate a scalable and adaptive approach to detect fraudulent activities efficiently. This study introduces a Spatial-Temporal Graph Neural Network (STGNN)-based fraud detection framework, specifically designed for scalable anomaly detection in large-scale blockchain networks. By modeling blockchain transactions as a spatial-temporal graph, the proposed system captures structural dependencies between wallets and temporal patterns of fund movements. The STGNN model employs graph convolutional networks (GCN) or graph attention networks (GAT) for spatial feature extraction and gated recurrent units (GRU) or temporal convolutional networks (TCN) for sequential fraud pattern recognition. Additionally, to ensure scalability, the framework incorporates graph partitioning techniques, parallelized mini-batch training, and distributed processing, enabling real-time fraud detection across high-throughput blockchain networks. Extensive experiments conducted on Bitcoin and Ethereum transaction datasets demonstrate that the STGNN model achieves higher accuracy, lower false positive rates, and improved computational efficiency compared to rule-based fraud detection systems, supervised ML models, and static GNNs. Case studies further confirm the model’s effectiveness in detecting large-scale fraud schemes, such as DeFi exploits, cross-chain laundering, and coordinated illicit transactions. This research highlights the potential of graph-based deep learning techniques in blockchain security, providing a foundation for future advancements in scalable fraud detection, cross-chain anomaly detection, and decentralized financial security monitoring.
Blockchain technology, originating from the Bitcoin system, is a prominent notion in both practical applications and scholarly discourse. Numerous subtopics may be seen, including the definition of blockchain, its historical significance in the evolution of currency, its durability, and its magnitude of influence within the literature. In other words, sufficient study on blockchain exists in the literature. Likewise, several studies exist about auditing, particularly concerning accounting and taxation within the setting of the Turkish economy. An examination of official declarations and legislation in Turkey reveals that the state's view on the bitcoin industry lacks definiteness. The perspectives are transitioning from negative to positive. Nevertheless, contradicting remarks have also been seen. Upon assessing the existing circumstances, the strategic plans of nations with comparable developmental stages and active cryptocurrency markets are identified. The most appropriate stance for Turkey is neither entirely liberal nor entirely restrictive. The market requires active management and oversight. This control includes accounting and taxation. Turkey should transition from a passive observation approach to one that incorporates a definitive hybrid therapy. This hybrid encryption encompasses the fundamental components of the cryptocurrency system and the corresponding regulation of pertinent regulations.
Ben Berger, Edward W. Felten, Akaki Mamageishvili, Benny Sudakov
Optimistic rollups rely on fraud proofs -- interactive protocols executed on Ethereum to resolve conflicting claims about the rollup's state -- to scale Ethereum securely. To mitigate against potential censorship of protocol moves, fraud proofs grant participants a significant time window, known as the challenge period, to ensure their moves are processed on chain. Major optimistic rollups today set this period at roughly one week, mainly to guard against strong censorship that undermines Ethereum's own crypto-economic security. However, other forms of censorship are possible, and their implication on optimistic rollup security is not well understood. This paper considers economic censorship attacks, where an attacker censors the defender's transactions by bribing block proposers. At each step, the attacker can either censor the defender -- depleting the defender's time allowance at the cost of the bribe -- or allow the current transaction through while conserving funds for future censorship. We analyze three game theoretic models of these dynamics and determine the challenge period length required to ensure the defender's success, as a function of the number of required protocol moves and the players' available budgets.
In 2021, El Salvador declared bitcoin legal tender. According to President Nayib Bukele, the measure was intended to expand access to financial services in a country with a high proportion of unbanked people and to cheapen and ease remittance flows for migrants and their families. In this article, we inquire about the use of bitcoin as a tool for financial inclusion and contend that this policy needs to be seen in the broader context of democratic backsliding. We show that bitcoin has not translated into financial inclusion, but instead, the bitcoin law serves as a public relations tool to capture new support from like-minded constituencies, build closer relations with them, and empower international “crypto-bros.” On the other hand, this is a tool to benefit a close circle close to the president with the use of public funds, as part of a broader historical shift of elites in El Salvador.