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.
Ruonan Chen, Ye Dong, Yizhong Liu, Tingyu Fan · 8 authors
Federated Learning (FL) is a distributed machine learning paradigm that allows multiple clients to train models collaboratively without sharing local data. Numerous works have explored security and privacy protection in FL, as well as its integration with blockchain technology. However, existing FL works still face critical issues. i) It is difficult to achieving poisoning robustness and data privacy while ensuring high model accuracy. Malicious clients can launch poisoning attacks that degrade the global model. Besides, aggregators can infer private data from the gradients, causing privacy leakages. Existing privacy-preserving poisoning defense FL solutions suffer from decreased model accuracy and high computational overhead. ii) Blockchain-assisted FL records iterative gradient updates on-chain to prevent model tampering, yet existing schemes are not compatible with practical blockchains and incur high costs for maintaining the gradients on-chain. Besides, incentives are overlooked, where unfair reward distribution hinders the sustainable development of the FL community. In this work, we propose FLock, a robust and privacy-preserving FL scheme based on practical blockchain state channels. First, we propose a lightweight secure Multi-party Computation (MPC)-friendly robust aggregation method through quantization, median, and Hamming distance, which could resist poisoning attacks against up to <50% malicious clients. Besides, we propose communication-efficient Shamir's secret sharing-based MPC protocols to protect data privacy with high model accuracy. Second, we utilize blockchain off-chain state channels to achieve immutable model records and incentive distribution. FLock achieves cost-effective compatibility with practical cryptocurrency platforms, e.g. Ethereum, along with fair incentives, by merging the secure aggregation into a multi-party state channel. In addition, a pipelined Byzantine Fault-Tolerant (BFT) consensus is integrated where each aggregator can reconstruct the final aggregated results. Lastly, we implement FLock and the evaluation results demonstrate that FLock enhances robustness and privacy, while maintaining efficiency and high model accuracy. Even with 25 aggregators and 100 clients, FLock can complete one secure aggregation for ResNet in 2 minutes over a WAN. FLock successfully implements secure aggregation with such a large number of aggregators, thereby enhancing the fault tolerance of the aggregation.
Sharding blockchain networks face significant scalability challenges due to high frequencies of cross-shard transactions and uneven workload distributions among shards. To address these scalability issues, account migration offers a promising solution. However, existing migration solutions struggle with the high computational overhead and insufficient capture of complex transaction patterns. We propose AERO, a deep reinforcement learning framework to facilitate efficient account migration in sharding blockchains. AERO employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts. We also implement a sharding blockchain system called AEROChain, which integrates AERO and aligns with the blockchain decentralization principle. Extensive evaluation with real Ethereum transaction data demonstrates that AERO improves the system throughput by 31.77% compared to existing solutions, effectively reducing cross-shard transactions and balancing shard workloads.
In recent years, the Ethereum Name Service (ENS) has garnered significant attention within the community for enabling the use of Unicode in domain names, thereby facilitating the inclusion of a wide array of character sets such as Greek, Cyrillic, Arabic, and Chinese. While this feature enhances the versatility and global accessibility of domain names, it concurrently introduces a substantial security vulnerability due to the presence of homoglyphs-characters that are visually similar to others across Unicode and ASCII sets. These similarities can be exploited in homoglyph attacks, posing a distinct threat to domain name integrity. Despite community efforts to counteract this issue through a normalization process prior to domain resolution, our analysis uncovers significant discrepancies in how the normalization processes are applied across various applications. This inconsistency could result in the same domain name being resolved to different addresses in different applications, underscoring a critical vulnerability. We also discovered the new attack scenario in ENS which may cause legitimate domains resolved into malicious addresses even when they are verified by authorities. To systematically evaluate this inconsistency, we designed a tool for detecting application-level discrepancies in domain normalization process without requiring access to the application's source code. Our evaluation on hundreds of real-world Web3 applications identifies widespread deviations from established homoglyph mitigation practices, with more than 60% digital wallets and 80% dApps (decentralized applications) not able to produce consistent ENS resolving results, potentially impacting millions of users. This analysis underscores the urgent need for a standardized implementation of normalization processes to safeguard the integrity and security of ENS domains.
The rise of digital healthcare has intensified concerns over data privacy, particularly in cross-institutional medical data exchanges. This study introduces a blockchain-based protocol leveraging Zero-Knowledge Proofs (ZKP), specifically zk-SNARK, to enable verifiable yet privacy-preserving health data sharing. Built on a permissioned Ethereum blockchain, the protocol ensures that medical data validity can be confirmed without disclosing sensitive content. System implementation involves Python-based zk-circuits, smart contracts in Solidity, and RESTful APIs supporting HL7 FHIR formats for interoperability. Performance evaluations show promising results: proof verification times remained under 100 ms, with average proof sizes below 2 KB, even under complex transaction scenarios. Gas consumption analysis indicates a trade-off—ZKP-enabled transactions consumed approximately 93,000 gas units, compared to 52,800 in baseline cases. Interoperability testing across 10 FHIR-based scenarios resulted in 100% parsing success and an average data integration time of 1.7 seconds. Security assessments under white-box threat models confirmed that sensitive information remains unreconstructable, preserving patient confidentiality. Compared to previous implementations using zk-STARK, this protocol offers a 30% improvement in verification efficiency and a 45% reduction in proof size. The novelty lies in combining lightweight ZKP mechanisms with an interoperability-focused design, tailored for realistic hospital infrastructures. This research delivers a scalable, standards-compliant architecture poised to advance secure digital healthcare ecosystems while complying with regulations like GDPR
Xinyao Xu, Ziyu Mao, Jianzhong Su, Xingwei Lin · 7 authors
The rapid growth of decentralized applications, while revolutionizing financial transactions, has created an attractive target for malicious attacks.Existing approaches to detecting attacks often rely on predefined rules or simplistic and overly-specialized models, which lack the flexibility to handle the wide spectrum of diverse and dynamically changing attack types.To address this challenge, we present a general and extensible framework, MoE (Monitoring Ethereum), that leverages runtime verification to detect a wide range of attacks on Ethereum.MoE features an expressive attack modeling language, based on Metric First-order Temporal Logic (MFOTL), that can formalize a wide range of attacks.We integrate a novel semantic lifting approach that extracts system behaviors relevant for various attacks, which can be analyzed using the monitoring tool MonPoly.Furthermore, we also equip MoE with quantitative capabilities to evaluate the similarity between a transaction and an attack formula to enhance its performance in identifying attacks, including near-miss attacks.We carry out extensive experiments with MoE on a labeled benchmark and a large-scale dataset containing over one million transactions.On the labeled benchmark, MoE successfully detects 92.0% attacks and achieves a 45.0% higher recall rate than competing state-of-the-art tool.MoE finds 3,319 attacks with 95.4% precision on the large dataset.Furthermore, MoE uses quantitative analysis to uncover 8% additional attacks.Finally, the average time for * Xinyao Xu and Ziyu Mao contributed equally.
M. K. Ghosh, Swapnil Srivastava, Apoorva Upadhyaya, Raju Halder · 5 authors
Phishing scams on Ethereum have expanded with the surge of the platform, posing substantial challenges due to the sheer similarity in user behaviours and sparse temporal instances. Current methods often fail to tackle these concerns and overlook the temporal sequence of transactions, resulting in suboptimal performance. In this paper, we aim to address these gaps by focusing on the alignment of two aspects: (1) User-specific local temporal behavior, and (2) Divergences from global activity patterns of the network. Hence, we introduce CATALOG (CApturing joint TemporAl dependencies from LOcal and Global user behaviour), a novel representation learning model that jointly captures the local and global user behviours and their correlations by leveraging a dual cross-attention mechanism paired with a bi-directional Masked Language Modelling (MLM) transformer. Our proposed model simultaneously learns from local behavioral shifts, global market trends, and contextually enriched embeddings, effectively distinguishing phishing from non-phishing users while addressing existing research gaps. Extensive experiments on real-world Ethereum transaction data show that our framework improves phishing detection by 7-8% in the F1-Score along with demonstrating the generalization to Ethereum versions 1.0 and 2.0.
Recently, with the gradual development of machine learning technology, more and more people are trying to apply machine learning technology in various fields, and finance is one of the important fields. This work investigates the optimization of cryptocurrency portfolios by combining Long Short-Term Memory (LSTM) time series forecasting with traditional portfolio optimization methods. The focus of the paper is on using the historical price data from the past six years of Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC) to train LSTM models, which are then used to predict the prices of these cryptocurrencies for the period from January to June 2024. These predictions are subsequently incorporated into an extended Markowitz framework to optimize the portfolio on a monthly basis. The results indicate that the LSTM-enhanced portfolio optimization method yields higher returns and better risk management compared to traditional methods. This finding could prove that it is feasible and effective to apply machine learning methods, especially time series forecasting methods, to cryptocurrency portfolios.
Ethereum’s scalability has been a major concern due to its limited transaction throughput and high fees. To address these limitations, Polygon has emerged as a sidechain solution that facilitates asset transfers between Ethereum and Polygon, thereby improving scalability and reducing costs. However, current cross-chain transactions, particularly those between Ethereum and Polygon, lack transparency and traceability. This paper proposes a method to track cross-chain transactions across EVM-compatible blockchains. It leverages the unique feature that user addresses are consistent across EVM-compatible blockchains. We develop a matching heuristic algorithm that links transactions between the source and target chains by combining transaction time, value, and token identification. Applying our methodology to over 2 million cross-chain transactions (August 2020–August 2023) between Ethereum and Polygon, we achieve matching rates of up to 99.65% for deposits and 92.78% for withdrawals, across different asset types including Ether, ERC-20 tokens, and NFTs. In addition, we provide a comprehensive analysis of various properties and characteristics of cross-chain transactions. Our methodology and findings contribute to a better understanding of cross-chain transaction dynamics and bridge performance, with implications for improving bridge efficiency and security in cross-chain operations.
Traditional electronic voting systems face sig-nificant challenges, including susceptibility to tam-pering, lack of transparency, and vulnerabilities in voter authentication.To address these issues, this paper proposes a decentralized e-voting archi-tecture that integrates Aadhaar-based identity val-idation, biometric authentication (fingerprint and facial recognition), and Ethereum blockchain tech-nology for secure and immutable vote recording [1].The system leverages multi-factor authentica-tion to ensure only eligible voters can participate, while blockchain's distributed ledger guarantees tamper-proof storage and real-time auditability of votes.Experimental evaluations demonstrate that the proposed framework achieves a throughput of over 10,000 transactions per second with 99.99% uptime, making it scalable for large-scale elections.By eliminating centralized points of failure and enabling remote voting, this approach signif-icantly enhances electoral integrity, accessibility, and public trust.Future work will explore inte-gration with postquantum cryptography to further strengthen long-term security.
Open access
Internet Traffic Analysis and Secure E-voting
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Hendri Dewarto Silitonga, Ratna Artha Windari, Si Ngurah Ardhya
Penelitian ini bertujuan untuk (1) menganalisis dan memahami pengaturan hukum mengenai transaksi aset digital cryptocurrency di indonesia dalam Undang- Undang Informasi dan Transaksi Elektronik sebagai acuan dalam memberikan kepastian hukum terhadap penggunaan aset digital cryptocurrency di Indonesia, (2) mengidentifikasi keabsahan smart contract pada platform Ethereum berdasarkan sistem hukum kontrak di Indonesia berdasarkan perspektif syarat sahnya perjanjian yang dimuat di dalam pasal 1320 KUH Perdata. Jenis penelitian yang digunakan dalam penelitian ini adalah yuridis normatif , yakni melalui pendekatan perundang-undangan (statute approach) dan pendekatan perbandingan (comparative approach) . Sumber bahan hukum yang digunakan yaitu KUH Perdata, UU ITE, Bappebti, PP, dan artikel ilmiah yang relevan. Hasil penelitian menunjukkan bahwa (1) Undang-Undang Informasi dan Transaksi Elektronik tidak mencamtumkan secara eksplisit mengenai transaksi aset digital, istilah dokumen elektronik dan informasi elektronik, menkategorikan aset digital yang menjadikan aset digital memiliki kedudukan yang sah dan diakui secara hukum. (2) Keabsahan smart contract pada platform Ethereum jelas tidak memenuhi syarat subjektif dalam 1320 KUH Perdata yang membuat smart contract batal atau tidak sah sebagai suatu perjanjian yang mengikat para pihak.
Ethereum, the leading platform for decentralized applications, faces challenges in maintaining decentralization due to the significant hardware requirements for validators to store Ethereum's entire state. To address this, the concept of stateless clients is under exploration, enabling validators to verify transactions using cryptographic witnesses rather than the full state. This paper compares two approaches currently being discussed for achieving statelessness: Verkle trees utilizing vector commitments and binary Merkle trees combined with SNARKs. Benchmarks are performed to evaluate proving time, witness size, and verification time. The results reveal that the Verkle tree implementation used for benchmarking offers proving and verification times on the order of seconds and proof sizes on the order of one MB. The SNARK-based Merkle trees exhibit slow proof generation times, while offering constant and fast verification time. Overall, the results indicate for Verkle trees to provide a more practical solution for Ethereum's stateless future, but both methods offer valuable insights into reducing the state burden on Ethereum nodes. We make the code used for benchmarking available on GitHub.
Dankrad Feist, Gottfried Herold, Mark Simkin, Benedikt Wagner
Data Availability Sampling (DAS), a central component of Ethereum's roadmap, enables clients to verify data availability without requiring any single client to download the entire dataset. DAS operates by having clients randomly retrieve individual symbols of erasure-encoded data from a peer-to-peer network. While the cryptographic and encoding aspects of DAS have recently undergone formal analysis, the peer-to-peer networking layer remains underexplored, with a lack of security definitions and efficient, provably secure constructions. In this work, we address this gap by introducing a novel distributed data structure that can serve as the networking layer for DAS, which we call robust distributed arrays. That is, we rigorously define a robustness property of a distributed data structure in an open permissionless network, that mimics a collection of arrays. Then, we give a simple and efficient construction and formally prove its robustness. Notably, every individual node is required to store only small portions of the data, and accessing array positions incurs minimal latency. The robustness of our construction relies solely on the presence of a minimal absolute number of honest nodes in the network. In particular, we avoid any honest majority assumption. Beyond DAS, we anticipate that robust distributed arrays can have wider applications in distributed systems.
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.
Muhammad Kamran, Maaz Rehan, Muhammad Maaz Rehan, Wasif Nisar · 6 authors
Blockchain technology is increasingly being adopted across critical domains, such as healthcare and finance, yet it remains susceptible to anomalies and malicious attacks. Hence, robust anomaly detection is essential in these decentralized systems to maintain integrity, trust, and reliability. However, anomaly detection is still challenging due to data imbalances, adversarial resilience, and the lack of explanation in existing approaches. This work presents ARCADE, a novel approach for adversarially resilient anomaly detection in blockchain networks that leverages an optimized cost-sensitive stacking ensemble learning combined with explainable artificial intelligence (XAI) techniques. Firstly, the proposed approach uses cost-sensitive learning to address the data imbalance problem by optimizing class weights that are integrated with stacking ensemble learning to enhance detection accuracy. Secondly, along with this, newly engineered features are employed to strengthen the resilience of the model against malicious perturbations. Lastly, XAI techniques are applied to provide comprehensive insights and explanations for model prediction. To evaluate ARCADE, the Ethereum network transactions dataset is utilized to ensure a realistic case study. The experimental results show the superiority of the ARCADE in several aspects, achieving a high accuracy of 99.65%; strong resilience against adversarial perturbations, achieving an accuracy of 99.38% for low-intensity attacks, 91.04% for moderate attacks, and over 78% for extreme attacks; and surpassing existing techniques while also providing explainability for domain users.
The rapid expansion of 5G networks and edge computing has amplified security challenges in Internet of Things (IoT) environments, including unauthorized access, data tampering, and DDoS attacks. This paper introduces EdgeChainGuard, a hybrid blockchain-based authentication framework designed to secure 5G-enabled IoT systems through decentralized identity management, smart contract-based access control, and AI-driven anomaly detection. By combining permissioned and permissionless blockchain layers with Layer-2 scaling solutions and adaptive consensus mechanisms, the framework enhances both security and scalability while maintaining computational efficiency. Using synthetic datasets that simulate real-world adversarial behaviour, our evaluation shows an average authentication latency of 172.50 s and a 50% reduction in gas fees compared to traditional Ethereum-based implementations. The results demonstrate that EdgeChainGuard effectively enforces tamper-resistant authentication, reduces unauthorized access, and adapts to dynamic network conditions. Future research will focus on integrating zero-knowledge proofs (ZKPs) for privacy preservation, federated learning for decentralized AI retraining, and lightweight anomaly detection models to enable secure, low-latency authentication in resource-constrained IoT deployments.
Open access
Blockchain Technology Applications and Security
IoT and Edge/Fog Computing
Advanced Steganography and Watermarking Techniques
Abstract Decentralized and transparent nature of cryptocurrencies have lately increased investors interest in them. Forecasting cryptocurrency’s price accurately is crucial to come up with a good investment strategy, and such a forecast requires one to consider its unique attributes as well as high volatility. Even though many existing studies have focused on analyzing the cryptocurrency transaction graph topology, studies on the analysis of transaction graph’s impact on prices are quite limited. In this paper, we explore the forecasting ability of blockchain transaction graph-based attributes on Bitcoin’s and Ethereum’s future price via deep learning methods. More specifically, we came up with motif convolution module (MCM), a motif-based graph representation learning approach to take local structural knowledge into account more strongly in node and edge-attributed transaction graphs encoding substantial structural knowledge. Our proposed MCM constructs a motif dictionary without supervision, and employs a new motif convolution operation while extracting the vertices local structural context. Afterwards, we learn high-level vertex embeddings by using such structural context via multilayer perceptron and graph neural network. Overall, we extract the attributed transaction graphs temporally-evolving low-dimensional representations, and use such embedding data together with historical prices within self-attention-based LSTM to predict the future prices accurately. Our proposed approach outperforms all considered baselines in terms of both price and price direction prediction, showing the promise of efficient integration of transaction data into cryptocurrency price prediction.
This paper introduces a novel multi-objective optimization framework for the portfolio rebalancing problem, incorporating return, risk, and liquidity as the central financial objectives. Unlike static models, our approach captures market dynamics by allowing periodic reallocation of assets and explicitly modeling transaction costs. To address uncertainty in key financial parameters such as expected returns, volatility, and asset liquidity, we employ interval arithmetic, offering a flexible representation without requiring distributional assumptions. The framework models risk using semi-absolute deviation, which better reflects downside exposure compared to traditional variance. A distinctive feature of the model is the integration of nonlinear transaction costs, ensuring higher realism in trading scenarios. The optimization problem is formulated with interval coefficients and solved under multiple decision-making strategies: pessimistic, optimistic, and mixed (via convex combination). To validate the model, we conduct a case study on a cryptocurrency portfolio consisting of Bitcoin, Ethereum, Solana, and Binance Coin, covering the period January–March 2025. The numerical simulations demonstrate the adaptability of the proposed methodology under different investor attitudes and market conditions. Our findings show that the interval-based, multi-objective framework provides robust, diversified portfolio allocations and valuable strategic insights for decision-makers operating under uncertainty.
Abstract: This paper describes a project-specific electronic voting (e-voting) system that integrates blockchain technology with face recognition for robust voter authentication. The goal is to design a decentralized platform in which every vote is recorded immutably on an Ethereum-based blockchain, while face recognition ensures that only a uniquely verified individual can cast a ballot. We detail the system architecture, methodology, and implementation steps, and we compare our approach to other blockchain-based e-voting systems worldwide, including Voatz, Follow My Vote, Zug e-Voting, and Moscow Blockchain Voting. Finally, we reference the open-source repository on which our project is based, demonstrating its real-world applicability and transparency.
The Ethereum blockchain, plagued by network congestion and exorbitant transaction fees, faces significant scalability challenges. While Layer 2 solutions offer a promising avenue to address these concerns, their potential remains largely unexplored on blockchain applications. The research proposes a novel Layer 2 architecture specifically designed for the academic certificate system on the Ethereum network. The method commences with a comprehensive survey of existing literature, followed by an analysis of solutions within the business domain. Subsequently, the most suitable and comprehensive solutions are identified for integration into the proposed academic certificate system architecture. In the selection process, the research analyzes 20 studies to determine the frequency of solutions employed in each investigation. The results indicate the InterPlanetary File System (IPFS) exhibiting the highest frequency, while Oracle, Decentralized Identifiers (DIDs), and Application Programming Interfaces (APIs) have comparable frequencies. Furthermore, an analysis of rankings from 10 websites evaluating Layer 2 Ethereum solutions and their performance across various aspects reveals Arbitrum as the top-ranked solution, followed by Polygon and Optimism, respectively. The research demonstrates the implementation of this system architecture within the proposed system's process. The culmination of this effort is a valuable blueprint for developers seeking to build and deploy similar systems efficiently. Notably, the inherent adaptability of the architecture extends beyond the educational domain, paving the way for its application across diverse contexts. The system architecture presented constitutes an initial exploration into developing Decentralized Applications (DApps) on the Ethereum Layer 2 network because prior research has not specifically focused on its application.
Damodar Bihani, Bright Chibunna Ubamadu, Andrew Ifesinachi Daraojimba
The evolution of Web3 has ushered in a paradigm shift from centralized control to decentralized, user-centric ecosystems powered by blockchain technology. At the core of these ecosystems lies tokenomics—the strategic design and management of token economies—which plays a crucial role in ensuring long-term sustainability, scalability, and user engagement. This paper presents a strategic framework for understanding and optimizing tokenomics within Web3 ecosystems, integrating insights from game theory, behavioral economics, and blockchain governance. It identifies key components of effective tokenomic models, including token utility, supply mechanisms, distribution strategies, and incentive alignment. The proposed framework emphasizes the importance of balancing inflationary and deflationary forces, designing value accrual mechanisms that benefit both users and network developers, and embedding governance protocols that enhance transparency and resilience. Additionally, this study explores the interplay between token utility and network effects, underscoring how strategic token design can accelerate ecosystem growth while maintaining decentralization. By analyzing successful Web3 projects such as Ethereum, Polkadot, and Cosmos, the paper extracts best practices and highlights potential pitfalls that hinder ecosystem scalability and trust. Furthermore, it evaluates regulatory implications, sustainability challenges, and market volatility, proposing adaptive policy mechanisms to future-proof token economies. The framework provides a roadmap for developers, investors, and policymakers aiming to build or assess blockchain ecosystems that are not only technologically sound but also economically viable. In doing so, this research bridges the gap between technical blockchain design and economic sustainability, offering actionable insights for fostering inclusive, community-driven, and robust Web3 infrastructures. As blockchain adoption accelerates globally, strategic tokenomics will be pivotal in shaping the next generation of digital economies, ensuring equitable value creation and distribution in decentralized environments.
Andrew Lewis-Pye, Joachim Neu, Tim Roughgarden, Luca Zanolini
Safety and liveness are the two classical security properties of consensus protocols. Recent works have strengthened safety with accountability: should any safety violation occur, a sizable fraction of adversary nodes can be proven to be protocol violators. This paper studies to what extent analogous accountability guarantees are achievable for liveness. To reveal the full complexity of this question, we introduce an interpolation between the classical synchronous and partially-synchronous models that we call the $x$-partially-synchronous network model in which, intuitively, at most an $x$ fraction of the time steps in any sufficiently long interval are asynchronous (and, as with a partially-synchronous network, all time steps are synchronous following the passage of an unknown "global stablization time"). We prove a precise characterization of the parameter regime in which accountable liveness is achievable: if and only if $x < 1/2$ and $f < n/2$, where $n$ denotes the number of nodes and $f$ the number of nodes controlled by an adversary. We further refine the problem statement and our analysis by parameterizing by the number of violating nodes identified following a liveness violation, and provide evidence that the guarantees achieved by our protocol are near-optimal (as a function of $x$ and $f$). Our results provide rigorous foundations for liveness-accountability heuristics such as the "inactivity leaks" employed in Ethereum.
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.
As Ethereum continues to thrive, the Ethereum Virtual Machine (EVM) has become the cornerstone powering tens of millions of active smart contracts. Intuitively, security issues in EVMs could lead to inconsistent behaviors among smart contracts or even denial-of-service of the entire blockchain network. However, to the best of our knowledge, only a limited number of studies focus on the security of EVMs. Moreover, they suffer from 1) insufficient test input diversity and invalid semantics; and 2) the inability to automatically identify bugs and locate root causes. To bridge this gap, we propose OpDiffer, a differential testing framework for EVM, which takes advantage of LLMs and static analysis methods to address the above two limitations. We conducted the largest-scale evaluation, covering nine EVMs and uncovering 26 previously unknown bugs, 22 of which have been confirmed by developers and three have been assigned CNVD IDs. Compared to state-of-the-art baselines, OpDiffer can improve code coverage by at most 71.06%, 148.40% and 655.56%, respectively. Through an analysis of real-world deployed Ethereum contracts, we estimate that 7.21% of the contracts could trigger our identified EVM bugs under certain environmental settings, potentially resulting in severe negative impact on the Ethereum ecosystem.