Anton Paramonov, Yann Vonlanthen, Quentin Kniep, Jakub Sliwinski · 5 authors
Mangrove is a novel scaling approach to building blockchains with parallel smart contract support. Unlike in monolithic blockchains, where a single consensus mechanism determines a strict total order over all transactions, Mangrove uses separate consensus instances per smart contract, without a global order. To allow multiple instances to run in parallel while ensuring that no conflicting transactions are committed, we propose a mechanism called Parallel Optimistic Agreement. Additionally, for simple transactions, we leverage a lightweight Byzantine Reliable Broadcast primitive to reduce latency. Mangrove is optimized for performance under optimistic conditions, where there is no misbehavior and the network is synchronous. Under these conditions, our protocol can achieve a latency of 2 communication steps between creating and executing a transaction.
Margarita Capretto, Martín Ceresa, Antonio Fernández Anta, Pedro Moreno-Sanchez · 5 authors
Blockchains face a scalability limitation, partly due to the throughput limitations of consensus protocols, especially when aiming to obtain a high degree of decentralization. Layer 2 Rollups (L2s) are a faster alternative to conventional blockchains. L2s perform most computations offchain using minimally blockchains (L1) under-the-hood to guarantee correctness. A sequencer is a service that receives offchain L2 transaction requests, batches these transactions, and commits compressed or hashed batches to L1. Using hashing needs less L1 space, which is beneficial for gas cost, but requires a data availability committee (DAC) service to translate hashes into their corresponding batches of transaction requests. The behavior of sequencers and DACs influence the evolution of the L2 blockchain, presenting a potential security threat and delaying L2 adoption. We propose in this paper fraud-proof mechanisms, arbitrated by L1 contracts, to detect and generate evidence of dishonest behavior of the sequencer and DAC. We study how these fraud-proofs limit the power of adversaries that control different number of sequencer and DACs members, and provide incentives for their honest behavior. We designed these fraud-proof mechanisms as two player games. Unlike the generic fraud-proofs in current L2s (designed to guarantee the correct execution of transactions), our fraud-proofs are over pred-etermined algorithms that verify the properties that determine the correctness of the DAC. Arbitrating over concrete algorithms makes our fraud-proofs more efficient, easier to understand, and simpler to prove correct. We provide as an artifact a mechanization in LEAN4 of our fraud-proof games, including (1) the verified strategies that honest players should play to win all games as well as (2) mechanisms to detect dishonest claims.
In recent years, blockchain consensus mechanisms have evolved significantly from the original proof-of-work design, transitioning towards more efficient and scalable alternatives. This paper presents a comprehensive review and analysis framework for blockchain consensus mechanisms based on a systematic examination of 200+ publications. We categorize consensus mechanisms into four performance-oriented groups: high throughput, strong security, low energy, and flexible scaling, each addressing specific trade-offs in the blockchain trilemma of decentralization, security, and scalability. Through quantitative metrics including transactions per second, energy consumption, fault tolerance, and communication complexity, we evaluate mainstream mechanisms. Our findings reveal that no single consensus mechanism optimally satisfies all performance requirements, with each design involving explicit trade-offs. This paper provides researchers and practitioners with a structured framework for understanding these trade-offs and selecting appropriate consensus mechanisms for specific application contexts. Finally, we discussed future development trends, as well as regulatory and ethical considerations.
This paper introduces a pioneering multi-layered cybersecurity framework for Decentralized Finance (DeFi) platforms, fundamentally transforming traditional financial infrastructure by operating without centralized intermediaries through blockchain-based smart contracts, creating unprecedented accessibility while simultaneously introducing complex security challenges requiring specialized defense mechanisms. The immutable nature of blockchain technology necessitates comprehensive proactive security measures, as deployed smart contracts cannot be easily modified to address discovered vulnerabilities. Multi-layered security frameworks encompass pre-deployment foundations, including rigorous smart contract auditing by multiple independent firms, formal verification processes that mathematically prove contract behavior alignment with intended specifications, and transparent code documentation enabling thorough community security reviews. Runtime protection mechanisms incorporate time-locks enforcing mandatory delays before implementing critical protocol changes, circuit breakers serving as emergency stops when suspicious activity is detected, and rate limiting controls preventing flash loan attacks through transaction volume restrictions. Access control systems utilize multi-signature wallets requiring multiple authorized parties for transaction approval, while progressive decentralization strategies enable structured transitions from centralized development teams to distributed community governance. Financial protection frameworks integrate insurance protocols providing coverage against successful exploits, treasury management systems maintaining reserve funds through secure multi-signature mechanisms, and comprehensive risk management frameworks enabling continuous monitoring and threat identification. Community-driven security initiatives leverage distributed expertise through bug bounty programs offering competitive rewards for responsible vulnerability disclosure, collaborative security reviews identifying issues missed by formal auditing, and educational programs enhancing user awareness of security best practices and threat recognition capabilities.
The integration of blockchain technology into decision support systems (DSS) represents a paradigm shift in how organizations approach data integrity, transparency, and collaborative decision-making processes.This research explores the fundamental mechanisms through which blockchain technology enhances traditional DSS architectures, focusing on distributed ledger capabilities, consensus mechanisms, and cryptographic security features.The study examines various implementation frameworks, analyzing their effectiveness in real-world applications across multiple industrial sectors including healthcare, supply chain management, and financial services.Through comprehensive analysis of existing blockchain-based DSS implementations, this paper identifies key advantages such as immutable data records, enhanced transparency, reduced intermediary costs, and improved stakeholder trust.The research methodology encompasses both theoretical framework development and empirical evaluation of blockchain-DSS integration models.Critical challenges including scalability limitations, energy consumption concerns, and regulatory compliance issues are thoroughly investigated.The findings reveal that while blockchain technology significantly improves data reliability and system transparency in DSS environments, implementation requires careful consideration of technical constraints and organizational readiness.Performance metrics demonstrate measurable improvements in decision accuracy, audit trail completeness, and stakeholder confidence levels.The study concludes with recommendations for optimal blockchain-DSS integration strategies, highlighting the importance of hybrid approaches that combine traditional centralized processing with distributed ledger benefits.Future research directions include investigation of quantum-resistant blockchain protocols and artificial intelligence integration within blockchain-based decision support frameworks.This work contributes to the growing body of knowledge on distributed systems applications in organizational decision-making processes.
Open access
Blockchain Technology Applications and Security
Economic and Technological Systems Analysis
Advanced Research in Systems and Signal Processing
Harsh Kumar, Aijaz Ahmad Chopan, Makinder Kour, Zubair Fayaz
This paper explores the integration of Decentralized Finance (DeFi) into the Metaverse and its transformative impact on virtual economies. By examining platforms like Decentraland and Sandbox, the study highlights how DeFi protocols—such as lending, staking, and token swapping—enhance financial accessibility, user engagement, and asset liquidity. The paper also identifies key challenges including scalability, security vulnerabilities, regulatory uncertainty, and user adoption barriers. Opportunities such as decentralized ownership, financial inclusion, and cross-platform interoperability are discussed. Through a synthesis of academic and industry literature, this research underscores the potential of DeFi to reshape digital finance and virtual interactions. The study concludes by calling for further exploration of governance, behavioral trends, and regulatory frameworks to ensure a secure, inclusive, and sustainable decentralized Metaverse.
This paper provides a comprehensive analysis of the evolving security risks and regulatory compliance challenges facing Bitcoin in financial applications. While Bitcoin's cryptographic foundations and decentralized architecture offer strong protections, emerging threats—such as consensus attacks, network exploits, privacy leaks, and operational vulnerabilities—pose significant risks to financial institutions. Simultaneously, global regulatory frameworks are tightening, particularly around AML, KYC, and consumer protection, compelling institutions to implement robust compliance strategies. The study synthesizes insights from computer science, finance, and law to propose integrated approaches combining technical safeguards with regulatory adherence. Strategies such as multi-signature controls, blockchain analytics, secure custody solutions, and “compliance by design” are explored. The paper concludes by discussing future risks including quantum computing and privacy-compliance tensions, emphasizing the need for adaptive risk management in institutional Bitcoin adoption.
Decentralized blockchain payment infrastructures are rapidly emerging as transformative tools for reshaping financial transactions in the United States.Traditional payment systems remain heavily reliant on intermediaries such as banks, card networks, and clearinghouses, which impose significant transaction fees and introduce settlement delays.These inefficiencies disproportionately affect small businesses, underbanked populations, and cross-border remittances, where costs and time lags create barriers to broader participation in the financial ecosystem.Blockchain-based payment systems, by contrast, utilize distributed ledgers and smart contracts to enable peer-to-peer transactions with reduced reliance on intermediaries.This structural shift holds the potential to substantially lower transaction fees, streamline settlement processes to near-real-time, and enhance transparency through immutable record-keeping.From a broader economic perspective, decentralized payment solutions align with the growing demand for financial inclusion and democratized access to capital flows.They allow micro-entrepreneurs, gig workers, and rural communities to participate more effectively in economic activities by reducing entry costs and providing verifiable transaction histories.In the U.S. context, the integration of blockchain into mainstream finance could complement existing systems such as ACH, FedNow, and card-based networks, while offering alternatives that better serve marginalized groups.Narrowing the focus, evidence suggests that fintech innovators piloting blockchain platforms have already demonstrated measurable reductions in processing costs and settlement times for retail and institutional payments alike.Nonetheless, challenges persist in terms of regulatory clarity, interoperability with legacy infrastructures, and concerns over scalability and energy use.Addressing these issues through targeted policy reforms and publicprivate partnerships will be critical to ensuring that decentralized blockchain payment infrastructures can deliver on their promise of inclusive, efficient, and secure economic participation.
Nan Wang, Nan Wu, Xiangyu Hui, Jiafan Wang · 5 authors
As the demand for exercising the "right to be forgotten" grows, the need for verifiable machine unlearning has become increasingly evident to ensure both transparency and accountability. We present {\em zkUnlearner}, the first zero-knowledge framework for verifiable machine unlearning, specifically designed to support {\em multi-granularity} and {\em forgery-resistance}. First, we propose a general computational model that employs a {\em bit-masking} technique to enable the {\em selectivity} of existing zero-knowledge proofs of training for gradient descent algorithms. This innovation enables not only traditional {\em sample-level} unlearning but also more advanced {\em feature-level} and {\em class-level} unlearning. Our model can be translated to arithmetic circuits, ensuring compatibility with a broad range of zero-knowledge proof systems. Furthermore, our approach overcomes key limitations of existing methods in both efficiency and privacy. Second, forging attacks present a serious threat to the reliability of unlearning. Specifically, in Stochastic Gradient Descent optimization, gradients from unlearned data, or from minibatches containing it, can be forged using alternative data samples or minibatches that exclude it. We propose the first effective strategies to resist state-of-the-art forging attacks. Finally, we benchmark a zkSNARK-based instantiation of our framework and perform comprehensive performance evaluations to validate its practicality.
The paper briefly introduces the blockchain and distributed ledger technologies and the concept of non-fungible tokens. Next, the (re)conceptualization of business activities in heritage institutions like museums, libraries, and archives regarding introducing non-fungible tokens is discussed. The proposal for a non-fungible token application model is the central part of the paper. The eight-step model is thoroughly explained and separated into two sections: off-chain and on-chain. Finally, the discussion and conclusion are given, identifying new possibilities that the application of the model can bring to the heritage institutions and the possible challenges they may face.
В статье рассматриваются алгоритмы консенсуса как основа функционирования криптовалютных и блокчейн-систем. Раскрывается их экономическая и технологическая сущность, проводится сравнительный анализ основных моделей – Proof-of-Work (PoW), Proof-of-Stake (PoS), Delegated Proof-of-Stake (DPoS), Practical Byzantine Fault Tolerance (PBFT) и гибридных решений. Выделяются их преимущества и недостатки, определяется область применения и перспективы развития в условиях необходимости повышения масштабируемости, энергоэффективности и устойчивости к кибератакам. Особое внимание уделяется проблеме «триилеммы блокчейна» и поиску оптимального баланса между безопасностью, децентрализацией и производительностью. Сделан вывод о важности гибридных моделей и инновационных протоколов в формировании будущей архитектуры децентрализованных финансов.
Moisés Wladimir Henríquez-Moreira, Ariana Yomira Zamora-Párraga, Darwin Javier Zamora Mayorga
Participatory budgeting is presented as a tool for democratic governance that promotes citizen participation, transparency, and the equitable distribution of public resources through dialogue and consensus. The main objective of this research is to analyze the relationship between participatory budgeting and the management of the Decentralized Municipal Autonomous Government of the Ventanas canton during the year 2024, with two specific objectives: to determine how it strengthens local governance and to identify its impact on the canton's socioeconomic development. The methodology used was descriptive-explanatory with a qualitative approach, allowing for the characterization of the phases and actors involved in the process, as well as the examination of factors that influence its implementation. Documentary analysis of regulatory and bibliographic sources was used, along with interviews with officials from the GAD's Finance, Administration, and Planning departments, as well as with citizens, to learn about their perceptions and experiences. The participatory budget was developed in five phases: organization and dissemination, training, working groups, preparation and approval, and monitoring. Financial indicators revealed high efficiency in revenue (98%) and expenditure (97%), as well as a reduction in financial dependence. However, problems of socioeconomic disconnect and political prioritization were identified that limit the social impact of participatory budgeting, underscoring the need to strengthen planning and citizen participation.
Nicolò Romandini, Carlo Mazzocca, Kai Otsuki, Rebecca Montanari
Blockchain and smart contracts have garnered significant interest in recent years as the foundation of a decentralized, trustless digital ecosystem, thereby eliminating the need for traditional centralized authorities. Despite their central role in powering Web3, their complexity still presents significant barriers for non-expert users. To bridge this gap, Artificial Intelligence (AI)-based agents have emerged as valuable tools for interacting with blockchain environments, supporting a range of tasks, from analyzing on-chain data and optimizing transaction strategies to detecting vulnerabilities within smart contracts. While interest in applying AI to blockchain is growing, the literature still lacks a comprehensive survey that focuses specifically on the intersection with AI agents. Most of the related work only provides general considerations, without focusing on any specific domain. This paper addresses this gap by presenting the first Systematization of Knowledge dedicated to AI-driven systems for blockchain, with a special focus on their security and privacy dimensions, shedding light on their applications, limitations, and future research directions.
The InterPlanetary File System (IPFS) has been successfully established as the de facto standard for decentralized data storage in the emerging Web3. Despite its decentralized nature, IPFS nodes, as well as IPFS content providers, have converged to centralization in large public clouds. Centralization introduces BGP routing-based attacks, such as passive interception and BGP hijacking, as potential threats. Although this attack vector has been investigated for many other Web3 protocols, such as Bitcoin and Ethereum, to the best of our knowledge, it has not been analyzed for the IPFS network. In our work, we bridge this gap and demonstrate that BGP routing attacks can be effectively leveraged to censor content in IPFS. For the analysis, we collected 3,000 content blocks called CIDs and conducted a simulation of BGP hijacking and passive interception against them. We find that a single malicious AS can censor 75% of the IPFS content for more than 57% of all requester nodes. Furthermore, we show that even with a small set of only 62 hijacked prefixes, 70% of the full attack effectiveness can already be reached. We further propose and validate countermeasures based on global collaborative content replication among all nodes in the IPFS network, together with additional robust backup content provider nodes that are well-hardened against BGP hijacking. We hope this work raises awareness about the threat BGP routing-based attacks pose to IPFS and triggers further efforts to harden the live IPFS network against them.
The relevance of this article lies in the existence of over 13,000 decentralized autonomous organizations worldwide, with a total capitalization exceeding 23 billion USD. Numerous projects exploit this form to circumvent regulatory frameworks. At both the international and Ukrainian levels, a coherent understanding of the phenomenon of decentralized autonomous organizations, their objectives, genesis, and legal nature remains absent. The purpose of this article is to explore the genesis and legal nature of decentralized autonomous organizations – from the inception of the technical idea to their transformation into sui generis legal entities. Applying comparative and formal legal methods to examine the development of the legal understanding of these organizations, and employing case study methodology to assess their implementation in practice, the article investigates the main stages of the formation of the modern concept of decentralized autonomous organizations, their differentiation from adjacent constructs – decentralized applications, autonomous agents, and decentralized organizations – by highlighting criteria of autonomy and decentralization, along with case studies from Bitcoin to The DAO. On the basis of a comparative legal analysis of regulatory models in the United States, Europe, and offshore jurisdictions, a conceptual mismatch is identified between classical corporate forms and the ontology of decentralized autonomous organizations. A two-component qualification test is proposed, alongside a typology dividing them into genuine, hybrid, and quasi forms. The findings of the study, together with the identification of practical challenges faced by such projects, substantiate the possibility of recognizing decentralized autonomous organizations as legal persons under Ukrainian law by means of the doctrinal construct of the “personalized purpose” (Zweckvermögen) developed by A. von Brinz, potentially implemented in the form of a foundation. This approach permits the integration of algorithmic will with legal personality without undermining their decentralized nature. The article provides a foundation for further inquiries into specific legal characteristics of decentralized autonomous organizations, including the “sorites paradox” and the prospects for legislative regulation within the Ukrainian legal order based on the doctrine of personalized purpose.
Decentralized Finance (DeFi) is among the most revolutionary blockchain technology applications that changes how financial systems operate globally by eliminating the middlemen and allowing peer-to-peer transactions via smart contracts. DeFi platforms built on decentralized networks recreate core banking services (e.g., lending, borrowing, trading, and asset management) in a transparent, borderless, and programmable setting. This paper looks at the ways in which DeFi is disrupting conventional banking and the regulation issues that have emerged due to the phenomenon. It discusses the technical foundations of DeFi, its benefits of inclusiveness, efficiency, and innovation and its risks of volatility, security, and systemic vulnerability. Among the major regulatory issues identified in the paper are jurisdictional ambiguity, anti-money laundering (AML) and Know-Your-Customer (KYC) compliance, investor protection, and financial stability. Applications like Uniswap, Aave as well as MakerDAO example opportunities and threats. The same problem has dominated the United States, European Union and the emerging economies, as has been described in the comparison analysis of the response in regulation; tension of stimulation of innovation and protection of the financial structure. Research indicates that although DeFi has a revolutionary potential in relation to open finance, its decentralized form makes it difficult to regulate. It needs to be a middle ground between international coordination, hybrid sandboxes and technology neutral policy to not just promote resilience and consumer protection, but also creativity.
Recent years have seen many industrial implementations and much scholastic research, i.e., prototypes and theoretical frameworks, in Decentralized Identity Management Systems (DIDMS). It is safe to say that Attestation-Based Attribute-Based Decentralized IDM (ABABDIDM) has not received anywhere near the same level of attention in the literature as general Attribute-Based DIDMs (ABDIDM), i.e, decentralized Attribute-Based Access Control (ABAC). The use of decentralization, i.e., DIDM, is to improve upon the security and privacy-related issues of centralized Identity Management Systems (IDM) and Attribute-Based IDMs (ABIDM). And blockchain is the framework used for decentralization in all these schemes. Many DIDMs - even ABDIDMs - have been defined on popular blockchains such as Hyperledger, Ethereum, and Bitcoin. However, despite the characteristics of Ripple that makes it appealing for an ABIDM, there is a lack of research to develop an Identity Management System (IDMS) on Ripple in literature. We have attempted to conceptualize an ABABDIDM on Ripple.
As datasets become critical assets in modern machine learning systems, ensuring robust copyright protection has emerged as an urgent challenge. Traditional legal mechanisms often fail to address the technical complexities of digital data replication and unauthorized use, particularly in opaque or decentralized environments. This survey provides a comprehensive review of technical approaches for dataset copyright protection, systematically categorizing them into three main classes: non-intrusive methods, which detect unauthorized use without modifying data; minimally-intrusive methods, which embed lightweight, reversible changes to enable ownership verification; and maximally-intrusive methods, which apply aggressive data alterations, such as reversible adversarial examples, to enforce usage restrictions. We synthesize key techniques, analyze their strengths and limitations, and highlight open research challenges. This work offers an organized perspective on the current landscape and suggests future directions for developing unified, scalable, and ethically sound solutions to protect datasets in increasingly complex machine learning ecosystems.
ABSTRACT In the context of China–US trade friction, we use the TVP‐VAR, SHAP, DECO, and CDB model to test the spillover volatility, hedging, safe haven, and portfolio returns of cryptocurrencies based on daily data of Bitcoin, Ethereum, Litecoin, Ripple, CSI300 Index, Shanghai Composite Index, S&P500 Index, and Nasdaq Index. The results show a significant short‐term time‐varying asymmetric volatility spillover effect between cryptocurrencies and the US and Chinese stock markets. Cryptocurrencies can be used as short‐term hedging assets for the Chinese stock market. The evidence also shows no long‐term correlation between cryptocurrencies and the stock market. Therefore, in periods of volatility caused by trade friction between the two countries, such as the imposition of high tariffs, investors can regard cryptocurrencies as short‐term hedging assets and long‐term safe haven assets to mitigate losses caused by stock market fluctuations. In addition, adding cryptocurrencies to stock index portfolios can significantly diversify risks and increase returns.
Andrey Zaytsev, Nikolay Dmitriev, Evgenii Konnikov
A unified software-analytical suite is proposed. It implements a closed-loop control cycle for regional energy systems. The implementation combines event-driven modeling with two-stage stochastic optimization. The suite includes adaptive web parsers. The parsers extract and semantically verify telemetry data regardless of changes in web page structures and anti-bot mechanisms. The system employs the discrete-event simulator SimPy. That simulator reproduces equipment failures, load fluctuations and external disturbances. An analytical subsystem processes textual event logs. It applies TF-IDF and cosine similarity. It simulates quantum annealing to determine automatically the optimal cluster count. It evaluates cluster stability. A Pyomo-based optimization module solves a two-stage optimization program. Scenario generation employs Monte Carlo and Latin-Hypercube sampling. Subtasks distribute across computing resources in parallel. The system provides scalable configuration. It ensures high availability and fault tolerance. The solution offers extensible visualization. It supports flexible parameterization and API integration. Testing on real operational data for a regional energy system confirmed adaptability of the suite. It also demonstrated capacity to scale when the number of nodes and the volume of events increases. Future work will integrate machine learning algorithms for predictive analytics. The plan includes extending the model to multistage problems with distributed ledger technologies.
Modern vehicles accumulate fragmented lifecycle records across OEMs, owners, and service centers that are difficult to verify and prone to fraud. We propose VehiclePassport, a GAIA-X-aligned digital passport anchored on blockchain with zero-knowledge proofs (ZKPs) for privacy-preserving verification. VehiclePassport immutably commits to manufacturing, telemetry, and service events while enabling selective disclosure via short-lived JWTs and Groth16 proofs. Our open-source reference stack anchors hashes on Polygon zkEVM at <$0.02 per event, validates proofs in <10 ms, and scales to millions of vehicles. This architecture eliminates paper-based KYC, ensures GDPR-compliant traceability, and establishes a trustless foundation for insurance, resale, and regulatory applications in global mobility data markets.