Blockchain Papers

Divesh Sarkar, Manish Sarkar

No abstract is available for this record.

Hans Gersbach, Akaki Mamageishvili, Manvir Schneider

On several proof-of-stake blockchains, agents engaged in validating transactions can open a pool to which others can delegate their stake in order to earn higher returns. We develop a model of staking pool formation in the presence of malicious agents and establish existence and uniqueness of equilibria. We then identify the potential and risk of staking pools. First, allowing for staking pools lowers blockchain security. Yet, honest stakeholders obtain higher returns. Second, by choosing welfare-optimal distribution rewards, staking pools prevent malicious agents from receiving large rewards. Third, when pool owners can freely distribute the returns from validation to delegators, staking pools can disrupt blockchain operations, since malicious agents attract most delegators by offering generous returns.

Andry Alamsyah, Said Fikri Naufal Ramdhani

• Blockchain-based system enables policy-grade traceability for plastic credits. • Smart contracts enforce automated compliance with EPR and ESG frameworks. • DApp supports decentralized oversight, reducing audit burden on regulators • System logs immutable offset records aligned with circular economy targets. • Architecture offers scalable digital infrastructure for waste policy integration. Plastic credit schemes are increasingly adopted to mitigate plastic pollution, yet existing systems remain centralized, opaque, and prone to double counting and fraud. This study proposes and validates a plastic credit system that leverages blockchain technology aimed at enhancing transparency, traceability, and accountability in plastic recovery efforts. A modular three-layer architecture was implemented, comprising a user interaction layer, a blockchain execution layer, and a utility layer for metadata and analytics integration. The system employs two smart contracts on the Polygon Proof-of-Stake (PoS) mainnet using Ethereum standards: ERC-20 for fungible tokenization of plastic credits and ERC-721 for non-fungible certificate issuance. Functional testing confirmed successful execution of token lifecycle operations. Stress testing across 5000 sequential transactions yielded stable performance, with average confirmation times of 5.29 s for fungible token operations and 5.59 s for non-fungible processes. A decentralized application (DApp) was developed to support role-based interaction, credit traceability, and certificate validation. User evaluation returned a high usability score (86.4%), while benchmarking against existing platforms demonstrated improved auditability, automation, and stakeholder control. These findings indicate that blockchain infrastructure can enable decentralized, tamper-resistant plastic credit systems. The proposed model provides a scalable foundation for Extended Producer Responsibility (EPR) compliance and plastic waste traceability, which could potentially support the credibility of Environmental, Social, and Governance (ESG) reporting and supporting circular economy transitions across diverse policy and economic contexts.

Ziwei Wang, Cong Wu, Paolo Tasca

Sharding has emerged as a critical solution to address the scalability challenges faced by blockchain networks, enabling them to achieve higher transaction throughput, reduced latency, and optimized resource usage. This paper investigates the advancements, methodologies, and adoption potential of sharding in the context of Hedera, a distributed ledger technology known for its unique Gossip about Gossip protocol and asynchronous Byzantine Fault Tolerance (ABFT). We explore various academic and industrial sharding techniques, emphasizing their benefits and trade-offs. Building on these insights, we propose a hybrid sharding solution for Hedera that partitions the network into local and global committees, facilitating efficient cross-shard transactions and ensuring robust security through dynamic reconfiguration. Our analysis highlights significant reductions in storage and communication overhead, improved scalability, and enhanced fault tolerance, demonstrating the feasibility and advantages of integrating sharding into Hedera's architecture.

Tianyu Zhaolu, Huaqun Wang

No abstract is available for this record.

Ayan Sar, Sampurna Roy, Tanupriya Choudhury, Ajith Abraham

Generative adversarial networks (GANs) and diffusion models have dramatically advanced deepfake technology, and its threats to digital security, media integrity, and public trust have increased rapidly. This research explored zero-shot deepfake detection, an emerging method even when the models have never seen a particular deepfake variation. In this work, we studied self-supervised learning, transformer-based zero-shot classifier, generative model fingerprinting, and meta-learning techniques that better adapt to the ever-evolving deepfake threat. In addition, we suggested AI-driven prevention strategies that mitigated the underlying generation pipeline of the deepfakes before they occurred. They consisted of adversarial perturbations for creating deepfake generators, digital watermarking for content authenticity verification, real-time AI monitoring for content creation pipelines, and blockchain-based content verification frameworks. Despite these advancements, zero-shot detection and prevention faced critical challenges such as adversarial attacks, scalability constraints, ethical dilemmas, and the absence of standardized evaluation benchmarks. These limitations were addressed by discussing future research directions on explainable AI for deepfake detection, multimodal fusion based on image, audio, and text analysis, quantum AI for enhanced security, and federated learning for privacy-preserving deepfake detection. This further highlighted the need for an integrated defense framework for digital authenticity that utilized zero-shot learning in combination with preventive deepfake mechanisms. Finally, we highlighted the important role of interdisciplinary collaboration between AI researchers, cybersecurity experts, and policymakers to create resilient defenses against the rising tide of deepfake attacks.

Kaiji Yang, Jingjing Zhang, Junyao Zheng, Qiwen Liu · 6 authors

Pipelined Byzantine Fault Tolerant (BFT) consensus is fundamental to permissioned blockchains. However, many existing protocols are limited by the requirement for view-consecutive quorum certificates (QCs). This constraint impairs performance and creates liveness vulnerabilities under adverse network conditions. Achieving "certificate decoupling"-committing blocks without this requirement-is therefore a key research goal. While the recent BeeGees algorithm achieves this, our work reveals that it suffers from security and liveness issues. To address this problem, this paper makes two primary contributions. First, we formally define these flaws as the Invalid Block Problem and the Hollow Chain Problem. Second, we propose pBeeGees, a new algorithm that addresses these issues while preserving certificate decoupling with no additional computational overhead. To achieve this, pBeeGees integrates traceback and pre-commit validation to solve the Invalid Block Problem.Further, to mitigate the Hollow Chain Problem, we introduce a prudent validation mechanism, which prevents unverified branches from growing excessively. To summarize, pBeeGees is the first protocol to simultaneously achieve safety, liveness, and certificate decoupling in a pipelined BFT framework. Experiments confirm that our design significantly reduces block commit latency compared to classic algorithms, particularly under frequent stopping faults.

Jitendra Sharma, Jigyasu Dubey

Transaction-Ordering Dependence (TOD) is a potential vulnerability of blockchain-based smart contracts, which allows malicious actors to exploit the order of transactions to obtain financial profit through front-running and back-running strategies.The purpose of this paper is to present ZkDelay, a new framework that jointly uses commitment schemes and Verifiable Delay Functions (VDFs) to counter TOD in decentralized applications.ZkDelay introduces a two-step transaction scheme: a user makes a cryptographic commitment to a transaction without announcing its purpose, and then, upon completing a verifiable delay with a VDF, the intended transaction can be revealed and carried out.This temporal discontinuity, combined with cryptographic acknowledgments, prevents adversaries from interfering with actionable knowledge in real-time, thereby eliminating any orderingbased attack possibilities.Moreover, ZkDelay is transparent and trustless, as it can be used to verify both commitments and delay execution through zero-knowledge proofs, without leaking sensitive data.Additional sections dedicated to rigorous security analysis and performance analysis in Ethereum-like environments are provided in the paper, demonstrating that ZkDelay incurs only a low amount of computational overhead and that it exponentially improves resistance to TOD attacks.The solution can be deployed in existing smart contract systems and adapted to DeFi protocols, order-sensitive auctions, and other mechanisms.ZkDelay addresses the challenge of integrating privacy-preserving mechanisms with the fairness of execution by providing a scalable and practical solution to one of the most prevalent security issues in smart contract environments.

Badal Bhushan, Prassanna R Rajgopal, K. C. Sharma

Zero Trust is now the de facto standard to secure cloud-native, distributed, and AI-driven enterprise infrastructures. It's not only crucial to address human identities but also to secure non-human entities such as APIs, software agents, RPA bots, and smart city workloads. As hybrid infrastructures become the new normal and agentic AI systems (e.g., self-driving cars) grow more autonomous, identity remains the most stable and trustworthy security control plane. This document proposes an intent-aware Zero Trust Identity Architecture designed to consolidate governance, authentication, and access control for human and non-human entities. The architecture consists of decentralized identity provisioning, policy-as-code enforcement, real-time telemetry ingestion, trust scoring, and AI-powered intent detection to provide inputs for continuous verification and least privilege enforcement. Compliant with standards such as NIST SP 800-207, NIST SP 800-63, CISA Zero Trust Maturity Model, and DoD's Zero Trust Strategy, the architecture also aligns with industry developments from Microsoft Entra ID, AWS IAM Identity Center, Google BeyondCorp, SPIFFE/SPIRE, and W3C DIDs. The whitepaper explores use cases in healthcare, finance, retail, and industrial IoT spaces that are struggling with unique challenges like OT/IT convergence, multi-user devices, and governance of sensitive data access. High-profile attacks such as SolarWinds, MOVEit, and Log4Shell are broken down to highlight weaknesses in legacy IAM architectures and underscore the need for intent-based security. By intersecting behavior, purpose, and identity, this architecture remakes trust in hybrid, edge, and cloud-native settings with a conclusion of actionable paths of mitigation and a vision for intent-based Zero Trust governance

Yuankui Wang, Mohd Fahmi Ghazali, Ruzanna Ab Razak, Mohd Azlan Shah Zaidi

This study applies Phase Space Reconstruction and Phase Space LSTM to analyze Bitcoin’s interactions with Gold, S&P 500, U.S. Bonds, EUR/USD, and Crude Oil, revealing hidden dependencies and chaotic structures in financial markets. Study implement a multi-method validation framework combining the Rosenstein algorithm for Lyapunov exponent estimation, 0 − 1 test for chaos and BDS test to provide robust evidence for deterministic chaos. Results indicate that most assets exhibit deterministic chaos, with price evolution highly sensitive to liquidity conditions and macroeconomic forces. Phase space analysis conducted in optimal four-dimensional embeddings uncovers stronger predictive linkages between Bitcoin and U.S. Bonds, reinforcing its growing dependence on global financial conditions. The application of PS-LSTM significantly enhances forecasting accuracy, demonstrated through rigorous validation including statistical significance testing and economic significance evaluation using risk-adjusted performance metrics. These findings suggest that cryptocurrencies are not isolated assets but deeply entangled with systemic financial fluctuations, necessitating a reassessment of market stability and risk propagation through the lens of statistical mechanics and econophysics. • PSR reveals hidden dependencies across Bitcoin, gold, stocks, bonds, exchange rate and commodities. • Phase space analysis reveals that Bitcoin-bond linkages indicate macroeconomic integration. • Phase Space LSTM (PS-LSTM) enhances forecasting accuracy, reducing overfitting and improving predictive stability across all assets. • PS-LSTM reduces overfitting and improves forecasting across all asset classes. • Chaos detection confirms the presence of nonlinear dynamics in cryptocurrency and commodity markets. • Higher-dimensional embeddings enhance the detection of causality between financial assets.

Jing Zhao

As global economic integration deepens, supply chain finance plays a crucial role in optimizing corporate cash flow and promoting the coordinated development of industrial chains. However, issues such as information asymmetry and credit assessment difficulties in traditional models have hindered its growth. Blockchain technology, with its decentralized nature, data immutability, and automated smart contracts, offers innovative solutions for credit risk management in supply chain finance. This article systematically analyzes the application logic, typical scenarios, and implementation effects of blockchain technology in credit risk management within supply chain finance. It also explores the technical bottlenecks, regulatory challenges, and coordination issues faced by the practical implementation of these technologies, and propose targeted optimization strategies. The aim is to provide theoretical support and practical references for the deep integration of blockchain technology with supply chain finance.

Mohammed M. Alenazi, Fawwad Hassan Jaskani

The volatile nature of cryptocurrency markets demands real-time analytical capabilities that traditional centralized computing architectures struggle to provide. This paper presents a novel hybrid cloud–edge computing framework for cryptocurrency market forecasting, leveraging distributed systems to enable low-latency prediction models. Our approach integrates machine learning algorithms across a distributed network: edge nodes perform real-time data preprocessing and feature extraction, while the cloud infrastructure handles deep learning model training and global pattern recognition. The proposed architecture uses a three-tier system comprising edge nodes for immediate data capture, fog layers for intermediate processing and local inference, and cloud servers for comprehensive model training on historical blockchain data. A federated learning mechanism allows edge nodes to contribute to a global prediction model while preserving data locality and reducing network latency. The experimental results show a 40% reduction in prediction latency compared to cloud-only solutions while maintaining comparable accuracy in forecasting Bitcoin and Ethereum price movements. The system processes over 10,000 transactions per second and delivers real-time insights with sub-second response times. Integration with blockchain ensures data integrity and provides transparent audit trails for all predictions.

Nkanyiso Msweli, Gideon Ude Nnachi, Coneth Graham Richards

Electricity access deficits remain acute in Sub-Saharan Africa (SSA), where more than 600 million people lack reliable supply. Green hydrogen, produced through renewable-powered electrolysis, is increasingly recognized as a transformative energy carrier for decentralized systems due to its capacity for long-duration storage, sector coupling, and near-zero carbon emissions. This review adheres strictly to the PRISMA 2020 methodology, examining 190 records and synthesizing 80 peer-reviewed articles and industry reports released from 2010 to 2025. The review covers hydrogen production processes, hybrid renewable integration, techno-economic analysis, environmental compromises, global feasibility, and enabling policy incentives. The findings show that Alkaline (AEL) and PEM electrolyzers are immediately suitable for off-grid scenarios, whereas Solid Oxide (SOEC) and Anion Exchange Membrane (AEM) electrolyzers present high potential for future deployment. For Sub-Saharan Africa (SSA), the levelized costs of hydrogen (LCOH) are in the range of EUR5.0–7.7/kg. Nonetheless, estimates from the learning curve indicate that these costs could fall to between EUR1.0 and EUR1.5 per kg by 2050, assuming there is (i) continued public support for the technology innovation, (ii) appropriate, flexible, and predictable regulation, (iii) increased demand for hydrogen, and (iv) a stable and long-term policy framework. Environmental life-cycle assessments indicate that emissions are nearly zero, but they also highlight serious concerns regarding freshwater usage, land occupation, and dependence on platinum group metals. Namibia, South Africa, and Kenya exhibit considerable promise in the early stages of development, while Niger demonstrates the feasibility of deploying modular, community-scale systems in challenging conditions. The study concludes that green hydrogen cannot be treated as an integrated solution but needs to be regarded as part of blended off-grid systems. To improve its role, targeted material innovation, blended finance, and policies bridging export-oriented applications to community-scale access must be established. It will then be feasible to ensure that hydrogen contributes meaningfully to the attainment of Sustainable Development Goal 7 in SSA.

Akash Shinde, K. S. Radha, Eesh Pratap Singh, Amresh Kushwaha

Event ticketing systems have long faced challenges such as counterfeiting, scalping, and lack of transparency in resale markets. To address these issues, this research presents the design and development of a blockchain-based ticketing platform that leverages Non-Fungible Tokens (NFTs) to ensure secure, transparent, and verifiable ticket distribution. The primary aim of the study is to explore how blockchain technology can enhance trust, eliminate fraud, and provide users with full ownership of their tickets. The proposed system employs smart contracts to automate ticket creation, distribution, and resale, thereby minimizing the need for intermediaries. Each ticket is represented as a unique NFT, guaranteeing authenticity and enabling traceability throughout its lifecycle. The methodology involves implementing a decentralized application where event organizers can mint NFT tickets, and users can securely purchase, transfer, or resell them using blockchain infrastructure. The results demonstrate that NFT-based tickets effectively prevent duplication and unauthorized sales while providing an immutable record of ownership. Additionally, organizers gain better control over pricing policies, while buyers benefit from secure transfers and enhanced transparency. In conclusion, this platform contributes to solving long-standing issues in the ticketing industry by combining blockchain’s immutability with NFTs’ uniqueness. The study highlights the potential of decentralized technologies to revolutionize digital ticketing, improve user trust, and create a more efficient event management ecosystem.

Roberta Mauro Medina Maia

O presente estudo busca analisar os non fungible tokens em uma perspectiva jurídica, analisando, prioritariamente, suas possíveis interações com o direito de propriedade. Para tanto, a blockchain, na qual os NFTs são emitidos, será exposta como resposta tecnológica à escassez em ambiente virtual, o que tornava questionável a necessidade de tutela proprietária em tal contexto. Na sequência, será proposta, aos NFTs, a natureza de título atributivo multipropósito, expondo-se, também suas possíveis aplicações no mercado imobiliário.

UmaMaheswari Gurusamy, V Sangeetha, G PraveenKumar, A. Meenakshi

Purpose: This review paper examines the transformative potential of blockchain technology in addressing critical challenges within agricultural supply chains, including traceability, transparency, fraud prevention, and equitable value distribution. It aims to synthesize existing research and real-world applications to highlight how blockchain can create more efficient, sustainable, and fair food systems. Methodology/Approach: The study adopts a systematic literature review methodology, analyzing peer-reviewed articles, industry reports, and case studies from 2008 to 2024. Key frameworks and implementations, such as IBM Food Trust, AgriDigital, and TE-FOOD, are evaluated to identify patterns, benefits, and limitations of blockchain adoption in agriculture. Originality/Relevance: This paper contributes to the growing body of research on blockchain in agriculture by integrating insights from decentralized finance (DeFi), IoT, and AI, offering a holistic view of next-generation supply chains. It addresses gaps in scalability, regulatory challenges, and adoption barriers while proposing future trends like tokenization and predictive analytics. Key Findings: Blockchain enhances traceability, reducing food fraud and enabling rapid contamination tracking (e.g., Walmart’s mango traceability in 2.2 seconds). Smart contracts automate payments and compliance, empowering smallholder farmers with timely compensation. Integration with IoT and AI improves real-time monitoring and demand-supply matching, fostering sustainability. Challenges include scalability limitations, regulatory ambiguities, and the digital divide in rural areas. Theoretical/Methodological Contributions: The paper advances theoretical understanding by linking blockchain to ethical sourcing, sustainability, and farmer empowerment. Methodologically, it provides a framework for evaluating blockchain’s role in multi-stakeholder supply chains, emphasizing hybrid architectures and decentralized verification. The findings underscore blockchain’s potential as a foundational technology for equitable and resilient food systems.

Marcin Wątorek, Marija Bezbradica, Martin Crane, Jarosław Kwapień · 5 authors

Based on the cryptocurrency market dynamics, this study presents a general methodology for analyzing evolving correlation structures in complex systems using the $q$-dependent detrended cross-correlation coefficient ρ(q,s). By extending traditional metrics, this approach captures correlations at varying fluctuation amplitudes and time scales. The method employs $q$-dependent minimum spanning trees ($q$MSTs) to visualize evolving network structures. Using minute-by-minute exchange rate data for 140 cryptocurrencies on Binance (Jan 2021-Oct 2024), a rolling window analysis reveals significant shifts in $q$MSTs, notably around April 2022 during the Terra/Luna crash. Initially centralized around Bitcoin (BTC), the network later decentralized, with Ethereum (ETH) and others gaining prominence. Spectral analysis confirms BTC's declining dominance and increased diversification among assets. A key finding is that medium-scale fluctuations exhibit stronger correlations than large-scale ones, with $q$MSTs based on the latter being more decentralized. Properly exploiting such facts may offer the possibility of a more flexible optimal portfolio construction. Distance metrics highlight that major disruptions amplify correlation differences, leading to fully decentralized structures during crashes. These results demonstrate $q$MSTs' effectiveness in uncovering fluctuation-dependent correlations, with potential applications beyond finance, including biology, social and other complex systems.

Samuel Breckenridge, Dani Vilardell, Andrés Fábrega, Amy Zhao · 7 authors

In traditional, one-vote-per-person voting systems, privacy equates with ballot secrecy: voting tallies are published, but individual voters' choices are concealed. Voting systems that weight votes in proportion to token holdings, though, are now prevalent in cryptocurrency and web3 systems. We show that these weighted-voting systems overturn existing notions of voter privacy. Our experiments demonstrate that even with secret ballots, publishing raw tallies often reveals voters' choices. Weighted voting thus requires a new framework for privacy. We introduce a notion called B-privacy whose basis is bribery, a key problem in voting systems today. B-privacy captures the economic cost to an adversary of bribing voters based on revealed voting tallies. We propose a mechanism to boost B-privacy by noising voting tallies. We prove bounds on its tradeoff between B-privacy and transparency, meaning reported-tally accuracy. Analyzing 3,582 proposals across 30 Decentralized Autonomous Organizations (DAOs), we find that the prevalence of large voters ("whales") limits the effectiveness of any B-Privacy-enhancing technique. However, our mechanism proves to be effective in cases without extreme voting weight concentration: among proposals requiring coalitions of $\geq5$ voters to flip outcomes, our mechanism raises B-privacy by a geometric mean factor of $4.1\times$. Our work offers the first principled guidance on transparency-privacy tradeoffs in weighted-voting systems, complementing existing approaches that focus on ballot secrecy and revealing fundamental constraints that voting weight concentration imposes on privacy mechanisms.

Chidananda Ningthoujam, Basanta Thoudam, Mutum Bıdyaranı Devi

Blockchain oracle networks serve as critical bridges between on-chain smart contracts and off-chain data sources, enabling decentralized applications to access real-world information. However, existing oracle systems suffer from significant vulnerabilities including data manipulation attacks, lack of quality assurance mechanisms, and absence of robust validation frameworks. This research proposes a novel reputation-based data quality assurance system for blockchain oracle networks that combines machine learning-based reputation scoring with stake-weighted validation mechanisms. We employ a multi-tier validation process to check data sources for historical accuracy, metric consistency and behavioural patterns. Through extensive simulated and experimental studies with 1,000 oracle nodes under different data categories, we show a reduction of 82.3% in false data injection attack exposure as well as an improvement by 76.8% in our overall data quality metrics compared to conventional oracle systems. The proposed system achieves an accuracy of 94.7% on data without sacrificing decentralization and resistant to collusion attacks in the network. Our results contribute to the design of reliable blockchain oracle infrastructure that decentralized finance (DeFi) and Web3 applications will rely on.

Tanya Wadhwani

Taxation of Non-Fungible Tokens (NFTs) is a topical challenge in the emerging global digital economy. NFTs, being individual blockchain assets symbolizing ownership of art, music, gaming collectibles, or virtual property, are difficult to fit into traditional categories of property, securities, or commodity law. India's Finance Act 2022 brought a framework for taxing Virtual Digital Assets (VDAs) including NFTs, by levying a flat 30 per cent tax on income arising on their transfer and a 1 per cent Tax Deducted at Source (TDS). This legislative move, making NFTs a part of the official economy, has at the same time created ambiguity around valuation, fairness, and the larger digital innovation implications. The present paper embarks on an in-depth analysis of NFT taxation in India by putting the Finance Act 2022 within the overall policy context of economic formalisation. It analyzes the complexities of valuation, wherein subjective determination of price and unstable market conditions restrict even-handed assessment; risks of double taxation, especially where royalties, resale profits, and GST overlap; complexities of cross-border enforcement in decentralized blockchain transactions; and compliance burden on investors, creators, and exchanges through compulsory TDS deductions. These bring out the inflexibility and shortfalls in the current framework. As a backdrop to understand India's strategy, the paper contrasts global practices. The United States has released Internal Revenue Service (IRS) guidance that indicates NFTs can be considered collectibles and taxed with a premium capital gains rate. The United Kingdom uses principles of capital gains tax to the transfer of NFTs, with the aim of equity with other assets. Singapore has exempted some NFT transactions from Goods and Services Tax (GST), which is a forward-thinking move, while the European Union is still considering a harmonized digital tax code under its proposed reforms of the digital economy. These cross-country views expose that India's flat-rate policy is an extreme outlier in its harshness, threatening to drive creators and investors to more innovation-friendly places. The paper also addresses wider policy issues, such as whether NFTs are subject to capital gains rules or a general rate of tax, whether authentic creators should be distinguished from speculative traders, and how international cooperation, especially under the OECD's digital tax efforts, could reduce cross-border revenue losses. Pursuant to these analyses, the report provides in-depth recommendations: the introduction of transparent and standardized valuation guidelines for NFTs; differentiated treatment for creators to foster innovation; relief from compliance burdens by amending the 1 per cent TDS; consideration of bilateral tax treaties to cover cross-border NFT transactions; and a phase-wise movement towards a capital gains tax model. By plugging a major research lacuna in Indian juristic literature, this article contends that though India's taxation of NFTs is an important starting point, the existing architecture is incomplete and unbending. What is needed is a remodeled approach one that scales the state's interests with the imperative to promote a dynamic digital economy.

Md. Zaki Muzahid

In the Ethereum blockchain network, high transaction fees due to limited block space and high demand necessitate scalable solutions.Layer 2 (L2) scaling solutions, particularly rollups, offer a promising approach by processing transactions offchain and posting compressed data to the main chain (Layer 1).However, current L2 rollups rely heavily on centralized sequencer nodes, which introduces centralization risks and single points of failure.Thus, to address these concerns, this paper explores the existing issues associated with centralized sequencers exemplified by real-life incidents.Consequently, reviews the existing decentralized sequencer models by describing their operations.In addition, this study proposes a novel approach of decentralizing sequencers leveraging the Delegated Proof of Stake (DPoS) consensus mechanism depicting its' components and step by step procedures.Finally, providing comparison among the novel approach and the existing decentralized sequencer frameworks along with their limitations.

Stanton Heister, Felix Kin Peng Hui, David I. Wilson, Yaakov Anker

The 2015 Paris Agreement paved the way for the carbon trade economy, which has since evolved but has not attained a substantial magnitude. While carbon credit exchange is a critical mechanism for achieving global climate targets, it faces persistent challenges related to transparency, double-counting, and verification. This paper examines how Distributed Ledger Technology (DLT) can address these limitations by providing immutable transaction records, automated verification through digitally encoded smart contracts, and increased market efficiency. To assess DLT’s strategic potential for leveraging the carbon markets and, more explicitly, whether its implementation can reduce transaction costs and enhance market integrity, three alternative approaches that apply DLT for carbon trading were taken as case studies. By comparing key elements in these DLT-based carbon credit platforms, it is elucidated that these proposed frameworks may be developed for a scalable global platform. The integration of existing compliance markets in the EU (case study 1), Australia (case study 2), and China (case study 3) can act as a standard for a global carbon trade establishment. The findings from these case studies suggest that while DLT offers a promising path toward more sustainable carbon markets, regulatory harmonization, standardization, and data transfer across platforms remain significant challenges.

Jinghui Chen, Edward Furman, Stephano Ricci, Judeto Shanthirajah

The limitations of the traditional mean-variance (MV) efficient frontier, as introduced by Markowitz (1952), have been extensively documented in the literature. Specifically, the assumptions of normally distributed returns or quadratic investor preferences are often unrealistic in practice. Moreover, variance is not always an appropriate risk measure, particularly for heavy-tailed and highly volatile distributions, such as those observed in insurance claims and cryptocurrency markets, which may exhibit infinite variance. To address these issues, Shalit and Yitzhaki (2005) proposed a mean-Gini (MG) framework for portfolio selection, which requires only finite first moments and accommodates non-normal return distributions. However, downside risk measures - such as tail variance - are generally considered more appropriate for capturing risk managers' risk preference than symmetric measures like variance or Gini. In response, we introduce a novel portfolio optimization framework based on a downside risk metric: the tail Gini. In the first part of the paper, we develop the mean-tail Gini (MTG) efficient frontier. Under the assumption of left-tail exchangeability, we derive closed-form solutions for the optimal portfolio weights corresponding to given expected returns. In the second part, we conduct an empirical study of the mean-tail variance (MTV) and MTG frontiers using data from equity and cryptocurrency markets. By fitting the empirical data to a generalized Pareto distribution, the estimated tail indices provide evidence of infinite-variance distributions in the cryptocurrency market. Additionally, the MTG approach demonstrates superior performance over MTV strategy by mitigating the amplification distortions induced by $\mathrm{L}^2$-norm risk measures. The MTG framework helps avoid overly aggressive investment strategies, thereby reducing exposure to unforeseen losses.

Bence Soóki-Tóth, István András Seres, Kamilla Kara, Ábel Nagy · 6 authors

The long-term success of cryptocurrencies largely depends on the incentive compatibility provided to the validators. Bribery attacks, facilitated trustlessly via smart contracts, threaten this foundation. This work introduces, implements, and evaluates three novel and efficient bribery contracts targeting Ethereum validators. The first bribery contract enables a briber to fork the blockchain by buying votes on their proposed blocks. The second contract incentivizes validators to voluntarily exit the consensus protocol, thus increasing the adversary's relative staking power. The third contract builds a trustless bribery market that enables the briber to auction off their manipulative power over the RANDAO, Ethereum's distributed randomness beacon. Finally, we provide an initial game-theoretical analysis of one of the described bribery markets.