Phan The Duy, Nghi Hoang Khoa, Nguyen Tran Anh Quan, Luong Ha Tien ¡ 6 authors
This paper proposes PenTiDef, a fully decentralized, privacy-preserving, and poisoning-resilient framework for decentralized federated IDS (DFL-IDS). PenTiDef synergistically integrates three key components: (i) client-side Distributed Differential Privacy (DDP) with stochastic Gaussian noise to protect gradient leakage, (ii) a lightweight latent-space defense module that extracts and compresses penultimate-layer representations (PLRs) into stable Latent Semantic Representations (LSRs) via AutoEncoder, followed by Centered Kernel Alignment (CKA) and K-Means clustering for robust malicious update detection without auxiliary datasets, and (iii) a permissioned blockchain layer with smart contracts that orchestrates on-chain validation, secure FedAvg aggregation, and immutable auditability, eliminating any central server. Extensive experiments on CIC-IDS2018 and Edge-IIoTSet under both IID and realistic non-IID settings, with adversary ratios up to 40\%, demonstrate that PenTiDef consistently outperforms state-of-the-art baselines (FLARE and FedCC) in detection accuracy and F1-score while maintaining lower training overhead. By jointly addressing privacy, robustness, and decentralization in a unified secure aggregation protocol, PenTiDef provides a practical and scalable solution for trustworthy collaborative intrusion detection in heterogeneous, adversarial IIoT environments.
The sudden growth of cryptocurrencies has created a set of intricate regulatory and legal issues for the financial and governance system of India. The decentralized nature of digital currencies like Bitcoin and Ethereum challenges the conventional monetary system, giving rise to concerns about their legal status, protection of investors, taxation, and overall financial stability. This paper critically analyzes the regulatory environment in India, especially in the wake of the 2018 circular issued by the Reserve Bank of India and its subsequent strike-down in the case of Internet and Mobile Association of India v. Reserve Bank of India. It also discusses challenges with respect to money laundering under the Prevention of Money Laundering Act, 2002, taxation of virtual digital assets, and the lack of a comprehensive statutory regulatory framework for cryptocurrency exchanges. The paper contends that the current stance of India is one of regulatory ambivalence, vacillating between control and tolerance.
Advances in artificial intelligence (AI) and distributed ledger technology (DLT) are reshaping how biological research, data and materials are managed. At the same time, the mechanisms used to implement and demonstrate compliance with the biological weapons prohibition regime rely heavily on national oversight systems that face increasing administrative complexity and uneven capacity across states. Emerging technologies are often discussed as potential sources of risk in the life sciences, but they may also provide tools to strengthen key regime functions. AI and DLT could support more effective laboratory oversight, strengthen export controls on dual-use items, and facilitate national reporting and transparency mechanisms. Their impact will depend on governance choices, including how states manage data integrity, human oversight, interoperability and equitable access to digital capabilities. Used responsibly, these tools could improve record integrity, administrative efficiency and confidence in the peaceful use of biological research.
Stochastic Bit-Parallel Maximum Clique Solver (1024-bit Virtual Register) We introduce a stochastic bit-parallel solver for the Maximum Clique Problem (MCP) based on a 1024-bit virtual register architecture implemented as 16 contiguous uint64_t words in standard C++17, ensuring full portability across 64-bit platforms (x86-64, ARM, RISC-V). Core operationsâcandidate intersection, population count, and leading-zero detectionâexecute in exactly 16 instructions per 1024-bit operation. The solver integrates three key components: (i) a co-neighborhood heuristic that identifies high-coreness nodes via O(N²) pairwise popcount over 1024-bit adjacency rows; (ii) a stochastic swarm of independent worker threads; and (iii) greedy clique expansion through iterative bitwise intersection. Exact branch-and-bound solvers (MaxCliqueDyn, MCQ) become computationally intractable on dense random graphs such as G(1024, 0.5), where chromatic coloring bounds lose effectiveness and the search tree grows exponentially, requiring hours of computation on commodity hardware. The proposed method operates specifically within this hard regime, achieving 100% recovery of all 28 planted clique vertices in 153 millisecondsâa setting where exact state-of-the-art methods cannot remain competitive regardless of hardware scaling. Experimental validation was performed on a Qualcomm Snapdragon 8 Gen 2 (8-core ARM) and independently reproduced on Linux x86-64 server hardware. The solver requires no cloud infrastructure and no GPU acceleration. STATEMENT OF PRIOR ART AND LICENSE TERMS (PolyForm Noncommercial Framework) 1. Statement of Prior Art This document constitutes a public disclosure of the stochastic bit-parallel Maximum Clique methodology, including its virtual register architecture, heuristic structure, and execution model.The mathematical and algorithmic concepts are released solely to establish Prior Art and prevent third-party patent claims under 35 U.S.C. § 102 and international equivalents. 2. Software License While the conceptual methods are disclosed defensively, all source code, implementations, binaries, and hardware realizations are not in the public domain and are licensed under the PolyForm Noncommercial License 1.0.0. Permitted (Non-Commercial)⢠Academic research and experimentation⢠Peer review and independent verification⢠Educational and non-profit use⢠Non-commercial open-source research implementations Condition: Publications must cite the canonical DOI or primary reference. Prohibited (Commercial)⢠Integration into proprietary software or hardware⢠Deployment in commercial systems, services, or products⢠Use in paid tools, platforms, or consulting workflows⢠Sublicensing or sale of the code or derivatives 3. Commercial Licensing Any commercial use requires explicit written authorization from the author. 4. No Code-Size Threshold (No De Minimis) The PolyForm Noncommercial License imposes no exemptions based on code length, fragment size, or proportion of reuse. Any useâpartial or completeâremains fully subject to the license. 5. Anti-Snippet Laundering and Anti-Circumvention Extraction, paraphrasing, refactoring, translation, or reimplementation of any algorithmic componentâincluding bit-parallel structures, heuristics, or execution logicâshall be considered derivative use.Attempts to evade the license through minimal reuse, language changes, functional replication, or modular embedding do not limit its applicability.This interpretation aligns with international good-faith and anti-abuse principles. 6. Presumption of Derivation Any system exhibiting substantial functional or structural similarity, developed after exposure to this work, shall be presumed derivative.The burden of proof for independent creation rests on the alleged infringing party. 7. Knowledge Contamination Exposure to the code, documentation, or technical description constitutes knowledge contamination.Subsequent implementations by exposed parties are not considered clean-room unless supported by contemporaneous evidence of prior independent development. 8. Waiver of Jury Trial To the fullest extent permitted by law, all parties waive the right to a jury trial in disputes arising from this license or related use. 9. Severability and Survival If any provision is deemed unenforceable, the remaining provisions remain in effect.The following provisions survive termination: license scope, noncommercial restrictions, anti-circumvention, presumption of derivation, knowledge contamination, intellectual property ownership, waiver of jury trial, and remedies. 10. Academic Use and Research Freedom The author expressly encourages academic and scientific use of this work. The following activities are permitted on a non-commercial basis: ⢠Research, benchmarking, and experimental validation⢠Publication of scientific analyses, comparisons, or extensions⢠Use in university courses, laboratories, and academic projects⢠Inclusion in research solver portfolios⢠Independent theoretical or empirical study All academic use must include proper citation to the original work.
Exponentially growing data generated by networked devices in Industry 4.0 environments requires industrial analytics that are secure, scalable, and decentralized. This article proposes TADDA-4i, a new multi-layered architecture based on IOTA's Tangle-Directed Acyclic Graph (DAG)-based Distributed Ledger Technology (DLT)-combined with federated learning and edge computing to provide real-time, secure, reliable, and self-sovereign industrial analytics. The architecture minimizes centralized bottlenecks via feeless, asynchronous data validation and tamper-evident model update verification using the Tangle ledger. Adaptive Tip-Aware Data Prioritization (ATDP) and Tangle-Validated Federated Aggregation (TVFA) are two new algorithms proposed for improving responsiveness and securing federated learning integrity. Experimental evaluation in emulated industrial edge environments showed that transactions take 30 percent less time, almost all of the misbehaving updates are detected, the model is about 10 percent more accurate, and output is not reduced even if the number of devices reaches 50. These findings make TADDA-4i an executable solution for the future generations of decentralized industrial intelligence.
A.S. Kannan, E. Baraneetharan, R.Venkatasubramanian, S. Sasi ¡ 6 authors
India's ambitious renewable energy targets of 500 GW by 2030 and net-zero emissions by 2070 necessitate transformative energy trading solutions capable of harnessing distributed renewable sources. This paper introduces a blockchain-enabled peer-to-peer (P2P) energy trading platform designed for India's diverse energy landscape, which includes rooftop solar, wind plants, and microgrids in both urban and rural areas. Built on the Ethereum foundation, the platform employs smart contracts to automate energy transactions between prosumers, reducing dependence on the conventional grid and advancing India's energy security goals. The system integrates machine learning algorithms trained on specific Indian usage patterns and weather conditions to forecast optimal trading times, accounting for seasonal changes, festivals, and industrial demand cycles. Key model assumptions include: (i) prosumers have bidirectional smart meters with IoT connectivity; (ii) weather data availability from Indian Meteorological Department stations; (iii) baseline electricity tariffs following state-level regulatory frameworks; and (iv) participants operate within Karnataka Electricity Regulatory Commission's P2P trading guidelines. Core parameters include LSTM networks with 50 hidden units, learning rate of 0.001, and 24-hour prediction horizons; Random Forest models with 100 estimators and maximum depth of 10; smart contract gas limits of$3,000,000$units; and dynamic pricing coefficients$\alpha=0.15$and$\beta=0.08$calibrated against Tamil Nadu industrial tariffs. Through automated transactions, the platform allows small-scale generators to sell surplus energy directly to local consumers, mitigating the$18-20 \%$distribution losses typical of the Indian grid. Pilot studies in Tamil Nadu and Maharashtra showcased significant results, including a 35-45% cost saving for participating industries and transparent carbon credit accounting, aligning with emerging ESG compliance needs. The platform contributes to the Digital India initiative by fostering a decentralized energy infrastructure that supports both economic development and environmental sustainability.
DLT and several other technological elements such as smart contracts, digital wallets, oracles, and so on in the context of financial markets, are leading to the emergence of very different phenomena which require, first of all, to be understood and then, inevitably as their importance and volume grow, regulated and supervised, to ensure the stability of the market and the protection of its investors. At the international level, the Financial Stability Board is advancing a global regulatory framework grounded in the principle of âsame activity, same risk, same regulationâ, aiming to ensure consistent and comprehensive regulation of crypto-asset activities and stablecoins relative to the risks they present, while also fostering responsible innovation prompted by technological advancements. The European Union is actively addressing regulatory challenges in the crypto space, employing distinct approaches to different categories of cryptoassets, depending on whether DLT technology is used in the context of non-fully decentralized finance, rather than in DeFi itself, which currently lacks effective regulation within the European Union. Greater problems from a regulatory perspective, however, are posed by the phenomenon of DeFi, which entails a more significant disintermediation. For this reason, even at the European level, this is undoubtedly the area that poses the most significant problems for market and investor protection. Keywords: decentralized ledger technology, crypto-assets, regulation, DeFi, investor protection.
With distributed financial microservices, the DualWrite problem frequently results in data discrepancy between payment gateways and in-house ledgers. Conventional reconciliation schemes are based on high-latency batch reconciliation or hard-coded rules, and cannot identify Soft Drifts, small corruptions in the data (e.g. 3% deviation) that resemble normal variance. The paper suggests a real-time reconciliation model that combines an Apache Kafka streaming high-throughput system and an Unsupervised Isolation Forest anomaly detector. The experimental outcomes have shown that although the application of static rules resulted in a Recall rate of only 52.1% (it does not detect soft drifts), the offered AI model attained 100% Recall in all types of drifts. Moreover, the system had a consistent latency of 3.76 ms which was found to be viable in high-frequency trading settings where low-latency and data integrity are of utmost importance.
The rapid evolution of digital currency systems has consistently faced the fundamental challenge of achieving an optimal balance between transaction privacy, computational efficiency, and cryptographic security. This comprehensive research paper introduces the Elliptic Homomorphic Token (EHT), a groundbreaking cryptographic protocol that revolutionizes privacy-preserving peer-to-peer transactions through the innovative integration of elliptic curve-based partially homomorphic encryption mechanisms and advanced digital signature schemes. Unlike conventional zero-knowledge proof systems that have dominated the privacy-focused cryptocurrency landscape, EHT takes a fundamentally different approach by directly leveraging the underlying cryptographic primitives that form the mathematical foundation of these complex systems. The protocol implements a sophisticated pre-transaction mechanism followed by distributed block recording, achieving remarkable performance metrics of 1000 transactions per second (TPS) with consistently low latency ranging from 50 to 100 milliseconds. Our comprehensive approach systematically addresses the significant computational overhead challenges that were extensively documented during Central Bank Digital Currency (CBDC) implementation projects, while simultaneously providing a robust and practical framework for privacy-preserving digital transactions that maintains the highest standards of cryptographic security. The EHT protocol represents a paradigm shift in how we conceptualize and implement privacy-preserving digital currency systems, offering a more direct, efficient, and mathematically elegant solution compared to existing approaches. Through extensive theoretical analysis, rigorous security proofs, and comprehensive performance evaluations, this paper demonstrates that EHT not only meets but exceeds the requirements for next-generation digital currency systems in terms of privacy, efficiency, scalability, and security.
Sealed-bid auctions are a fundamental mechanism for pricing across a wide range of online applications, but their deployment in online and decentralized environments raises significant security and privacy challenges. This paper presents a comprehensive survey of secure online sealed-bid auction schemes, whose primary contribution lies in a unified taxonomy, a structured comparative analysis, and an explicit identification of design trade-offs and research gaps in secure sealed-bid auction systems. We organize existing work into a taxonomy covering trusted, verifiable, confidential, fully decentralized, and identity-privacyâpreserving auctions and systematically analyze their cryptographic foundations and trust assumptions. Through this comparative evaluation, we highlight fundamental trade-offs between privacy guarantees, public verifiability, and efficiency, particularly the computational cost imposed by zero-knowledge proofs and secure computation techniques. Finally, we identify key research gaps and outline future directions toward scalable, verifiable, and privacy-preserving auction designs for real-world online applications.
Contemporary digital currency systems face fundamental challenges in achieving optimal balance between transaction privacy, computational efficiency, and cryptographic security. While zero-knowledge proof systems have dominated privacy-preserving cryptocurrency research, their practical implementations often involve prohibitive computational overhead that limits real-world deployment. This paper presents a comprehensive analysis of the Elliptic Homomorphic Token (EHT) protocol, which leverages elliptic curve-based partially homomorphic encryption to enable privacy-preserving peer-to-peer transactions without the computational complexity of zero-knowledge constructions. Our theoretical analysis demonstrates strong privacy guarantees under standard cryptographic assumptions, while experimental evaluation shows that EHT achieves 1000 transactions per second with 50-100ms latency. The protocol eliminates the need for complex zero-knowledge proofs by directly utilizing elliptic curve cryptographic primitives, resulting in performance improvements exceeding 100Ă over existing privacy-focused systems while maintaining equivalent security properties.
The notion of decentralization has acquired a central position in contemporary debates on crypto-asset regulation, yet its legal meaning remains largely unresolved. MiCA is the first EU legislative act to attach explicit regulatory consequences to decentralization, most notably through Recital 22, which excludes from its scope services âprovided in a fully decentralized manner without any intermediaryâ. This chapter examines how such decentralization could be interpreted and demonstrated when invoked as a criterion of regulatory scope. Adopting a legal-dogmatic and functional approach, it argues that the âproof of decentralizationâ should be grounded in verifiable distributions of control, discretion and operational capacity. The chapter develops this argument across three analytically distinct layers of the cryptoasset ecosystem: node operators at the network layer, smart contract deployers at the protocol layer and front-end providers at the interface layer. It shows that each layer hosts actors capable of influencing transactional outcomes in ways that may reproduce, rather than eliminate, forms of financial intermediation and risks targeted by MiCA. The analysis illustrates why decentralization is a matter of degree rather than an absolute condition and why its evidentiary assessment must be structurally informed. The chapter concludes by outlining the implications of this approach for MiCAâs supervisory landscape and for the future treatment of decentralized systems within EU financial regulation. Keywords: decentralization, MiCA, crypto-asset services, regulatory scope, decentralized finance, DeFi, blockchain governance, node operators, protocol deployers, front-end providers, distributed ledger technology, intermediation, EU financial regulation.
This chapter explores the core challenge that blockchain-based decentralization presents to traditional legal frameworks. Legal systems rely on the identification of subjects â whether natural persons, legal entities, or State authorities â to whom rights, obligations, and consequences can be assigned. This principle of attribution is foundational to nearly every facet of law, including criminal responsibility, contractual obligations, property rights, and regulatory enforcement. However, blockchain technology and autonomous decentralized systems fundamentally undermine this assumption by operating without identifiable centres of accountable authority. Decentralized Autonomous Organisations and Decentralized Finance protocols function autonomously, transcending territorial boundaries and human intermediaries, yet they typically fall under State jurisdictional structures. The argument is made that the fundamental operations of law â determining applicable rules, adjudicating disputes, and enforcing judgments â are predicated on the existence of âcentres of attribution.â This situation creates a fundamental tension between the aspirations of decentralization and the operational requirements of law, raising the question of whether the challenges posed by Blockchain can be reconciled with the essential principles of legal order. Keywords: blockchain technology, decentralization, smart contracts, legal accountability, jurisdiction, lex cryptographia, DAO, constitutional law, regulatory frameworks, legal personality.
The blockchain technology has attracted more and more interest recently as a reliable and secure platform for a variety of applications. This study presents a comprehensive comparative analysis of monolithic and modular architectural patterns in smart contracts, which have become a revolutionary technology thanks to the integration of blockchain technology. A real-world vehicle purchase and sale system was used as a case study. Two contract structures were used that perform the same function: a modular architecture with five interacting contracts, and a monolithic architecture that combines all these functions in a single contract. An empirical analysis conducted for 100 vehicle sales revealed that while the modular architecture offers advantages such as independent upgradeability, testability, and maintainability, the monolithic approach outperforms it in many metrics, including a 36.7% reduction in transaction costs and a 75% faster deployment time. The findings provide evidence-based architectural guidance for blockchain and smart contract developers in selecting appropriate design patterns, particularly in real-world applications where gas costs are critical. Cite this article as: T. Timu.in and S. BiroÄul, "A comparative analysis of monolithic and modular smart contract architectures: A case study of vehicle trading systems," Electrica, 2026, 26, 0333, doi:10.5152/electrica.2026.25333.
Abstract This paper investigates the time-varying dynamics of the Bitcoin price by examining its relationship with key global factors, including the VIX, the interest rate, the US dollar index, the oil price, and the gold price. The empirical analysis employs a state-space model, the Kalman filter method, and a TVP-VAR-SV. The findings from the state-space model indicate a significant negative association between the Bitcoin price and the VIX, while identifying a positive relationship with the gold price. Further analysis using instantaneous time-varying impulse response functions reveals that the negative response of Bitcoin to the VIX intensified significantly during the pandemic period. A similar negative impact was observed regarding the US dollar index and the oil price. In contrast, the interest rate exhibited a positive connection with the Bitcoin price. Notably, the relationship between Bitcoin and gold, which was negative prior to the pandemic, became statistically insignificant as the crisis escalated. This underscores that Bitcoinâs hedging capabilities and safe haven characteristics are not intrinsic fundamental qualities, but rather conditional behaviors that evolve with shifting global economic landscapes. The evidence suggests that Bitcoinâs defensive properties are structural rather than fundamental, emerging primarily during specific volatility regimes. Additionally, the inverse relationship between the oil price and Bitcoin suggests that rising energy costs may dampen the cryptocurrencyâs appeal due to its substantial energy consumption. These results offer significant implications for scholars, investors, and portfolio managers regarding the management of digital assets during periods of systemic instability.
The proliferation of large language model (LLM) based AI agents has created an urgent need for robust orchestration mechanisms that can coordinate heterogeneous agents in complex, real-world environments. Existing approaches to multi-agent task allocation rely predominantly on centralized controllers, which introduce single points of failure, scalability bottlenecks, and rigid coupling between the orchestrator and the agents it manages. This paper introduces the Dynamic Task Orchestration (DTO) framework, a decentralized, capability-aware architecture for assigning tasks to AI agents in real time. The DTO framework models each agent as an autonomous economic actor that participates in a sealed-bid auction mechanism to compete for incoming tasks. Task allocation decisions are driven by three primary factors: the agent's declared capability profile, its current computational and cognitive load, and the estimated complexity of the task. The framework defines a formal task decomposition grammar, a standardized agent capability ontology, and a set of protocol-level contracts that govern bidding, delegation, execution, and result aggregation. We present the theoretical foundations of the framework, provide detailed implementation guidance, and propose a comprehensive evaluation methodology grounded in metrics for throughput, latency, fault tolerance, and resource utilization. Through analytical evaluation and scenario-based discussion, we demonstrate that the DTO framework achieves superior load balancing, resilience to agent failure, and adaptability to changing workloads compared to centralized orchestration baselines. The framework is entirely tool-agnostic and vendor-neutral, designed so that any organization can adopt it to build more robust, efficient, and scalable multi-agent systems.
This chapter explores the legal and economic dimensions of Non-Fungible Tokens as instruments of digital scarcity. It examines their technical architecture, legal qualification, and evolving regulatory approaches in the European Union and the United States through a comparative perspective. By contrasting derivative and specific forms of regulation, the analysis highlights the challenges of integrating Non-Fungible Tokens into existing legal frameworks. The chapter concludes by emphasising the need for regulatory frameworks that provide legal certainty while preserving the innovative potential of blockchain technologies, highlighting how the controlled creation of digital scarcity can redefine value and trust in the digital environment. Keywords: NFT, digital scarcity, digital assets, value, comparative law, EU Regulation, US Regulation.
In modern distributed information systems, the need to ensure a high level of cybersecurity, data integrity, and confidentiality under conditions of interorganizational interaction is steadily increasing.Blockchain technologies enhance transparency and trust among participants; however, traditional consensus mechanisms are accompanied by significant computational overhead, risks of centralization, and limited capabilities for protecting sensitive information.These issues are particularly acute in corporate environments of small and medium-sized enterprises, where the computational resources of network nodes are constrained while the requirements for business data confidentiality remain high.A promising direction is the integration of Zero-Knowledge Proof (ZKP) mechanisms, which enable verification of operation correctness without disclosing the underlying data.Nevertheless, their practical adoption is hindered by the high cost of proof construction for classical cryptographic primitives.In particular, for the SM3 hash function there are no efficient optimized implementations of preimage proofs, and its bit-oriented structure leads to a substantial increase in circuit size and proof generation time, making its use infeasible in resource-constrained environments.This paper proposes a dockerized private blockchain architecture oriented toward corporate environments with limited resources, combining the trust-oriented Proof of Friendship consensus with Zero-Knowledge Proof mechanisms.The key result is the development of an approach for optimizing SM3 hash preimage proofs in ZKP systems.The paper introduces principles of manual optimization of the SM3 circuit representation, including reduction of bitwise operations, aggregation of 1965 constraints, optimization of message expansion, and reduction of round depth.It is shown that these transformations significantly decrease the size of arithmetic circuits and proof generation time compared to naive algorithm translation, enabling practical use of SM3 in zero-knowledge systems and corporate blockchain solutions.The proposed approach provides a balance between blockchain transparency and business data confidentiality, forming a "trust but do not disclose" model.The obtained results establish a scientific and practical foundation for deploying privacypreserving computation in distributed information systems and for developing nextgeneration secure blockchain platforms.
Ethereum transaction fees exhibit substantial shortterm volatility driven by network congestion, making it difficult for users and applications to determine optimal transaction timing. This work investigates the temporal structure of Ethereum base fees and develops a scalable data-collection and forecasting pipeline for short-horizon, congestion-aware fee estimation. We propose a harvester engine framework based on a parallel blockprocessing mechanism to capture short-term market volatility and develop a parallelized harvester for efficient fee-history collection using the eth_feeHistory JSON-RPC interface. This interface provides the high-resolution, block-level data required for intraday analysis, despite protocol constraints such as the$\mathbf{1 0 2 4}$-block retrieval limit per request. Our fee-history engine incorporates bounded concurrency, latency-aware pacing, and retry stabilization, reducing 30-day data-acquisition time from hours to minutes. We analyze intra-day fee behavior and show that Ethereum base fees exhibit a stable$\mathbf{2 4}$-hour diurnal cycle. We also propose a normalized shape with a rolling-level calibration framework that preserves a stable daily rhythm while continuously adapting to month-level fee changes. Empirical evaluation shows that the adaptive approach improves accuracy and robustness. These findings demonstrate that Ethereum gas fees contain a predictable structure that can be leveraged for practical, short-term forecasting when combined with adaptive calibration.
ABSTRACT The digitization of healthcare insurance claims faces persistent challenges including data breaches, fraudulent submissions, and inefficiencies in verification and settlement. This paper presents a ZeroâKnowledge Succinct NonâInteractive Argument of Knowledge (ZkâSNARK) enabled blockchain framework deployed on the Polygon Proof of Stake (PoS) network for secure and privacyâpreserving health insurance processing. The proposed architecture integrates AttributeâBased Encryption (ABE) for data confidentiality and the Elliptic Curve Digital Signature Algorithm (ECDSA) for authentication, ensuring endâtoâend data integrity and access control. Experimental evaluation on the Polygon PoS testbed demonstrates a transaction cost of approximately $0.002, which is over 99% lower than Ethereum's 3â10 $ per transaction, while maintaining 100% resistance to data tampering, replay attacks, and transaction manipulation. Under the Polygon real network, the proposed framework supports a networkâlevel transaction capacity of up to 7000 transactions per second (TPS) under nominal operating conditions, with an approximately 9.3% reduction in effective capacity under stress scenarios, while maintaining 100% verification accuracy for all ZkâSNARK proofs. The average onâchain verification and settlement latency was measured at 4.7 s, confirming the system's suitability for realâtime healthcare claim settlement. These results validate that the proposed ZkâSNARK enabled Polygon PoS framework offers a scalable, costâefficient, and cryptographically robust solution for healthcare insurance automation, outperforming existing blockchain implementations across security, efficiency, and economic performance metrics.
This chapter analyses how the tokenization of decisional power â enabled by blockchainbased governance tokens â offers new governance models while simultaneously revealing structural, technological, and legal limits. After illustrating how electronic voting and tokenbased identification address challenges such as deepfakes, the chapter examines the functioning of DAOs, their governance procedures, and the informal community dynamics that shape decision-making. It then discusses the conceptual difficulties surrounding âdecentralization,â reviews existing metrics, and proposes alternative normative criteria, including minimum independent decision centres and the âmutualistic symbiosisâ between protocol, users, and developers. The final section evaluates legal obstacles under Italian law, showing the limited feasibility of tokenizing corporate participations and the centralizing effects introduced by the Fintech Decree. The chapter concludes that regulators must develop a coherent legal definition of decentralization and simplify regulatory frameworks for tokenized equity instruments. Keywords: Decentralized Autonomous Organizations (DAOs), tokenized governance, decentralization metrics, Blockchain-based voting, CorpTech regulation, Fintech Decree (Italy).
This paper introduces a novel decentralized autonomous organization (DAO) framework for microgrid governance, specifically targeting diverse stakeholder ownership. It integrates principles of decentralization, democratization, and digitization to streamline the just energy transition. The multifaceted model synthesizes key DAO mechanisms with microgrid elements by incorporating tokenomics, fund management, actor reputation, decision-making, tender selections and dispute resolutions. Preliminary conceptual validation in Python case studies demonstrates the feasibility of the approach. However, further research and validation are needed to pave the way from centralized structures to a more empowered, equitable and resilient future.
The real potential of an AI-enhanced and accelerated future lies not just in our collaborative capability but in our ability to go beyond simple collaboration and develop a symbiotic relationship with AI. Because AI is us, our role in this symbiosis is to ensure that we become better human beings that can make AI better. When we are better, more mature, and bring deeper thinking, we can ensure that the AI we develop evolves to become a better partner for us. An AI that reflects the best of us, not the worst. That means that everyone, especially the leadership teams of the leaner, smaller multinational companies and SMEs of the future will need to become deliberately developmental to get much better at collaboration and communication. So with the army of solopreneurs who exit companies to deliver part of the AI stack. In businesses of the future, as multiple external blockchains deliver new capabilities to the market, we will need a number of brilliant internal âquarterbacksâ calling the commercial, marketing, digital, operational, legal, and people âplaysâ and âthrowingâ out projects or tasks to their AI or human partners and solopreneurs across the decentralised web3 ecosystem. And the teams left inside those learner businesses need to develop too.