Blockchain Papers

Sreena G. Nair, K. Rohini

No abstract is available for this record.

Steven Paul Nohr

<b>Maximal Extractable Value (MEV)</b> has evolved from a theoretical artifact of transparent transaction ordering into a dominant economic force shaping Proof-of-Stake (PoS) blockchain ecosystems. While early research framed MEV as an unavoidable but competitive phenomenon, recent infrastructure developments—particularly MEV relays, aggregators, and proposer-builder separation (PBS)—have enabled the consolidation of extractive power into coordinated intermediary groups. This paper introduces <b><i>MEV Aggregator Drift</i></b>, a structural phenomenon in which MEV extraction progressively centralizes into opaque, off-chain coordination clusters <b><i>(“collusion packs”)</i></b> that undermine validator neutrality, distort protocol incentives, and introduce cartel-like dynamics without explicit on-chain collusion. We analyze the economic drivers, execution mechanisms, and systemic risks of MEV collusion across PoS and DeFi systems, and argue that existing mitigations focus on efficiency while neglecting enforceable neutrality. Finally, we outline mitigation requirements centered on validator accountability, behavioral monitoring, and transaction ordering attestation.

Priya Saxena, Shyam Jaiswal, Vaishnavi Mishra, A. Syed Ismail

No abstract is available for this record.

K. V. Panduranga Rao, S. K. Yakoob, C Dastagiraiah, T. Veeranna

No abstract is available for this record.

Peyman Karimi, Gholamreza Askarzadeh Dareh, Alireza Rayati Shavazi, Seyed Yahya Abtahi

This study aimed to develop a conceptual model for pricing digital assets by integrating behavioral finance perspectives and identifying psychological and social factors influencing investors’ decision-making in decentralized markets. A qualitative grounded theory approach was adopted. The study involved 15 experts in digital currencies, blockchain, and behavioral finance selected through purposive sampling until theoretical saturation was achieved. Data were collected via semi-structured interviews and textual content analysis. Open, axial, and selective coding were applied to build the theoretical framework. Reliability was confirmed using quality control indices such as Krippendorff’s alpha, Holsti coefficient, Scott’s Pi, and Cohen’s Kappa, all indicating high inter-coder agreement. The resulting model captured multiple determinants of digital asset pricing. Causal factors included emotional and psychological behaviors (e.g., fear of missing out, fear and greed), the influence of news and media, and social association effects. Contextual factors encompassed uncertainty, ambiguity, and market volatility. Strategic factors such as market trust and credibility, investors’ knowledge and awareness, and reference points were identified. Core conditions included regulatory and legal environments, technological infrastructure, and macroeconomic conditions. Consequences involved enhanced market transparency, analysts’ and advisors’ influence, institutional and retail investor interactions, and the impact of past experiences on risk-taking. The proposed behavioral finance-driven model demonstrates that digital asset pricing extends beyond classical economic frameworks, heavily shaped by investor psychology and external information dynamics. The findings can guide investors toward more rational strategies and support policymakers in creating effective regulations and safer decentralized financial ecosystems.

Kristián Košťál, Lukas Mastilak, Dušan Morháč, Adam Valach

No abstract is available for this record.

João Pires da Cruz, Daniel Costa, Armando Teixeira, João B. Duarte · 6 authors

No abstract is available for this record.

Rujia Li, Mingfei Zhang, Xueqian Lu, Wenbo Xu · 6 authors

No abstract is available for this record.

Hideshi Itoh, Kimiyuki Morita

No abstract is available for this record.

Yuki YASUNO, Shusuke Chida, 永田光葉, Daiki Izumi · 5 authors

Recent advances in distributed system design have shifted from centralized client–server models to decentralized frameworks that enable broad inter-organizational collaboration. While Decentralized Autonomous Organizations (DAOs) leverage blockchain for governance and token-based transactions, existing platforms face two major limitations: the absence of decentralized authorization mechanisms and inadequate support for large-scale data management. This paper introduces Agentic DAO, a next-generation DAO architecture that integrates multi-agent authorization and scalable data governance into a blockchain-centric framework. The platform employs a Trust Scoring Agent using Graph Neural Networks (GNNs) to predict transactional reliability and a Data Management Agent to ensure consistency between blockchain and IPFS-based storage. Governance is reinforced through SoulBound Token (SBT)-based credentials to prevent Sybil attacks and enforce fair participation. A prototype implementation demonstrates the feasibility of trust-based access control and robust data synchronization. Experimental results confirm that Agentic DAO enhances transparency, scalability, and fairness compared to conventional DAO models, enabling secure and collaborative engagement in decentralized ecosystems.

Ming Sen Thong

No abstract is available for this record.

Kevin Werbach

Abstract As we look to the future, how might decentralized autonomous organizations (DAOs) evolve? And where, beyond corporate law, might we find guidance for the legal questions those evolved DAOs pose? DAOs are, and will increasingly become, instrumentalities of artificial intelligence (AI). DAOs are connected with AI in at least three ways: They are tools for decentralized governance of AI data and models; AI may be used to automate the management and operations of DAOs; and DAOs themselves may function as a form of AI. As such, DAOs inherit the major regulatory and ethical challenges that AI poses, most notably with regard to autonomy. Thus, to consider the future questions DAOs pose and how to address them, we must look to the raging debates over AI regulation, and connect them to the more established themes of corporate law.

Eduardo Miranda Ribera

Abstract Blockchain technology indubitably offers opportunities for the trading of carbon credits through the tokenization of Verified Carbon Credits (VCCs) via DAO (Decentralized Autonomous Organizations). These organizations aim to facilitate the trading of VCCs, improve market transparency, reduce transaction costs by eliminating intermediaries and facilitate market access for small entities interested in exploiting this opportunity. Thus, the purpose of this paper—in line with the content of the U nidroit Project on the legal nature of verified carbon credits—is to analyse the tokenization of VCCs through DAOs, given the development of new platforms created for this purpose. To carry out this analysis, it is necessary to study the concepts of blockchain technology, digital assets and DAO.

Alex Murray, Jen Rhymer, David Sirmon

No abstract is available for this record.

Yu Cui, Prakash J. Singh

No abstract is available for this record.

Dario Amoroso d'Aragona

Microservice architecture has emerged as a dominant paradigm for building largescale software systems, promising modularity, scalability, and independent evolution of services. A central premise of this paradigm is that architectural decomposition reduces inter-service dependencies and enables teams to develop and deploy services autonomously. Despite these expectations, empirical evidence on whether microservice systems actually achieve loose coupling in practice remains limited, particularly in open-source environments where development processes are highly decentralized. This thesis investigates coupling phenomena in microservice-based open-source systems from both technical and organizational perspectives. The research follows a progressive investigative design structured around three stages: <i>measurement, observation</i>, and <i>explanation</i>. First, the thesis introduces the <i>Microservice Logical Coupling</i> (MLC) metric, a repository-based approach for identifying evolutionary dependencies between microservices. By extending traditional logical coupling analysis from file-level artifacts to service-level entities, MLC enables the detection of cross-service evolutionary dependencies using version control data without requiring runtime instrumentation. Second, the thesis conducts a large-scale empirical analysis to assess the prevalence and evolution of logical coupling across microservice-based open-source projects. The results show that logical coupling is a widespread phenomenon and tends to increase over time, suggesting that architectural boundaries alone do not guarantee independent service evolution. Third, the study examines the socio-technical factors associated with coupling by analyzing developer contribution patterns and service ownership structures. The findings reveal a consistent association between cross-service developer activity and higher levels of logical coupling, indicating that organizational structures influence the emerqence of architectural dependencies. Overall, this work contributes a replicable measurement approach, an empirically grounded empirical analysis of coupling in microservices, and a socio-technical perspective on microservice evolution. The results suggest that service independence is not solely an architectural property but a socio-technical outcome shaped by both system design and development practices.

Hoong Hao Yap, Karel Kruger

No abstract is available for this record.

Winfred Ayanga, Róbert Magda

No abstract is available for this record.

Yi Kuang, Fuchun Lin, Chaoping Xing

No abstract is available for this record.

Mariana Reis

This paper presents Zero-Knowledge Federated Learning Guard (ZK-FLGuard), a privacy-preserving and verifiable federated learning framework for real-time anomaly detection in Fifth-Generation Mobile Network (5G)-enabled Internet of Things (IoT) environments. Building on the integration of zero-knowledge proofs (zk-SNARK—Zero-Knowledge Succinct Non-interactive Argument of Knowledge) and blockchain-based access control, ZK-FLGuard ensures the integrity of model updates without exposing private data. Using real-world intrusion detection datasets (CICIDS2017—Canadian Institute for Cybersecurity Intrusion Detection System 2017, TON_IoT—Telecommunications Organisation of the National Security—IoT) and a synthetic adversarial dataset, our evaluation shows that ZK-FLGuard achieves up to 0.96 F1-score (harmonic mean of precision and recall), improves recall in low-frequency attack detection, and introduces less than 10% additional latency overhead compared to standard Federated Learning (FL). Compared with centralized Long Short-Term Memory (LSTM) and FL without Zero-Knowledge Proof (ZKP), ZK-FLGuard provides competitive accuracy while ensuring verifiable computation and strong privacy guarantees. We address the critical challenge of securing federated anomaly detection in 5G-enabled IoT systems against data leakage, model poisoning, and unauthorized access. While FL preserves privacy by keeping raw data local, it remains vulnerable to gradient leakage and adversarial manipulation. Our hypothesis is that combining zero-knowledge proofs and blockchain with FL can deliver a scalable, tamper-resistant, and privacy-preserving detection pipeline suitable for resource-constrained edge environments.

John Okyere

Financial exclusion remains acute in Sub-Saharan Africa, where more than 350 million adults lack access to formal financial services. A defining barrier is the absence of verifable identity: in countries such as Mozambique, Tanzania, and Niger, over half of adults without mobile money accounts cite missing documentation as the primary obstacle. Existing remedies either centralise sensitive personal data, creating systemic privacy and security risks, or demand document-issuing infrastructure that does not yet exist in many communities. This paper proposes ZKP-IDFS (Zero-Knowledge Proof Identity for Financial Services), a decentralised, privacy preserving digital identity framework that lets individuals prove identity-related predicates to financial institutions without disclosing the underlying personal attributes. ZKP-IDFS combines Groth16 zk-SNARKs for succinct on-chain proof verification, Pedersen commitments for attribute hiding, a W3C-compliant Verifi-able Credential layer, and a lightweight USSD/SMS proof-relay channel designed for feature-phone users in low-connectivity environments. We formalise the cryptographic model, specify the system architecture, and present a simulated performance evaluation across four representative network conditions. Results from a controlled simulation study show that end-to-end proof generation and relay complete in under 4.2 seconds on entry-level Android handsets at 3G speeds, with on-chain verication costs below 0.003 USD on an EVM-compatible layer-2 chain; these results require validation in eld deployments. We further demonstrate compliance with FATF risk-based KYC guidance and with emerging African data-protection legislation.

Subhasis Thakur, Martín Serrano

No abstract is available for this record.

Sachdeva Ks

Digital identity is critical, yet centralized providers create single points of failure&#x2014;breaches have exposed billions of records&#x2014;and quantum computing threatens the classical public-key cryptography (RSA/ECC) on which these systems rely. We present a system-level integration of blockchain, zero-knowledge proofs (ZKPs), and post-quantum cryptography (PQC) for privacy-preserving digital identity. A blockchain-based decentralized identifier (DID) system removes central databases; all signing and key-encapsulation operations use lattice-based PQC (CRYSTALS-Dilithium and Kyber); and selective disclosure is provided by Groth16 zk-SNARKs, with revocation via on-chain Merkle non-membership accumulators. We specify the full credential lifecycle&#x2014;issuance, two-phase authentication, and revocation&#x2014;with an explicit trust boundary separating the in-circuit Groth16 relation from the off-circuit issuer-signature check. We report a measured evaluation on a reference prototype: under liboqs 0.15.0, Dilithium-II signs/verifies in 0.19/0.06 ms and Kyber-512 encapsulates/decapsulates in 0.018/0.022 ms; a single-authentication Groth16 proof over the 21,715-constraint BN254 credential circuit takes <inline-formula> <tex-math notation="LaTeX">$\approx 981$ </tex-math></inline-formula> ms (snarkJS) and <inline-formula> <tex-math notation="LaTeX">$\approx 177$ </tex-math></inline-formula> ms (native rapidsnark) on byte-identical inputs, with <inline-formula> <tex-math notation="LaTeX">$\approx 40$ </tex-math></inline-formula> ms verification, a 723-byte proof, and <inline-formula> <tex-math notation="LaTeX">$\approx 243$ </tex-math></inline-formula>,000 gas for on-chain verification on a local EVM. A lifecycle harness with a passing revoked-credential negative test validates correctness. The signing and key-encapsulation layers are quantum-safe under current lattice assumptions; the Groth16 proof layer is classically secure only, and its post-quantum migration is identified as future work. End-to-end credential unforgeability is conditioned on an honest holder wallet performing the off-circuit signature check (Assumption 5). Every quantitative claim is labelled measured [M], simulated [S], assumption [A], or future work [F].

Christoph H.-J. Braun, Jesse Wright, Tobias Käfer

No abstract is available for this record.