Blockchain Papers

Enhao Huang, Frank Li, Tony Lin, Lowes Yang

This paper introduces DMind-3, a sovereign Edge-Local-Cloud intelligence stack designed to secure irreversible financial execution in Web3 environments against adversarial risks and strict latency constraints. While existing cloud-centric assistants compromise privacy and fail under network congestion, and purely local solutions lack global ecosystem context, DMind-3 resolves these tensions by decomposing capability into three cooperating layers: a deterministic signing-time intent firewall at the edge, a private high-fidelity reasoning engine on user hardware, and a policy-governed global context synthesizer in the cloud. We propose policy-driven selective offloading to route computation based on privacy sensitivity and uncertainty, supported by two novel training objectives: Hierarchical Predictive Synthesis (HPS) for fusing time-varying macro signals, and Contrastive Chain-of-Correction Supervised Fine-Tuning (C$^3$-SFT) to enhance local verification reliability. Extensive evaluations demonstrate that DMind-3 achieves a 93.7% multi-turn success rate in protocol-constrained tasks and superior domain reasoning compared to general-purpose baselines, providing a scalable framework where safety is bound to the edge execution primitive while maintaining sovereignty over sensitive user intent.

Bikki Kumar, Dev Karan, Adarsh Kandu, Ashish Khari

The social impact of decentralised online communities, such as blockchain-based social networks, is complex because their decentralisation allows users to exercise greater freedom and independence. A novel Dynamic Graph Neural Network with Temporal Knowledge Distillation (DGNN-TKD) is proposed to model and predict influence patterns. DGNN-TKD differs from standard Graph Neural Networks (GNNs), which function under the assumption of static graphs. It tracks the temporal evolution of a graph, and introduces a knowledge distillation mechanism that enables the transfer of influence embeddings over time. We propose a novel multi-dimensional influence metric that captures agent reputation, engagement and trust, supplemented with robust attention-based temporal aggregation. In experiments on decentralized social network datasets, DGNN-TKD surpasses current dynamic GNNs in influence prediction, community detection, and misinformation detection in decentralized governance/Web3 applications. This framework connects graph-based learning and social dynamics and serves as a powerful tool to study decentralized phenomena.

Aravindh Sekar

The increasing deployment of agentic artificial intelligence (AI) systems and decentralized digital infrastructures has challenged traditional assumptions about organizational administration, control, and governance. While AI has advanced task-level optimization and decision support, administrative functions such as coordination, compliance, and accountability remain largely centralized and dependent on humans. This paper introduces Autonomous Administrative Intelligence (AAI), a governance-aware AI capability that enables autonomous agents to execute and adapt administrative decisions within strategically defined constraints and decentralized governance mechanisms. Building on the Strategic Decentralized Resilience–AI (SDRT-AI) framework, the study develops a layered architecture and operational flow integrating agentic decision-making, governance-aware learning, and protocol-based validation. The proposed framework explains how strategic intent, organizational capabilities, and decentralized trust jointly enable scalable administrative autonomy while preserving accountability and control. By reframing administration as an AI-mediated governance process, this paper extends research on agentic AI and contributes to administrative science by providing a conceptual foundation for the design and governance of autonomous administrative systems in decentralized organizations.

Umair Abbas

We introduce FlashChain, a decentralized framework that integrates IO-aware attention mechanisms—especially FlashAttention—into scalable, trustless AI systems. As Transformer-based models become foundational to Web3 infrastructure (e.g., DAOs, decentralized search, autonomous agents), their quadratic compute and memory bottlenecks present critical challenges. FlashChain adapts block-sparse FlashAttention into a modular architecture optimized for multi-node, low-bandwidth environments typical of blockchain and edge networks. We propose a hybrid protocol combining attention kernel optimization with zero-knowledge verifiability, enabling real-time, trustless AI inference across distributed nodes. Benchmarks show 3–5× speedups and up to 30× gas savings per inference compared to baseline on-chain models.

Swapna Mandu, P Thirumurugan, Sugur Balaji, Amboth Sirisha · 6 authors

The gig economy faces significant challenges with centralized platforms like Upwork and Fiverr, including high service fees (10–20%), opaque algorithms, unreliable reviews, and frequent payment disputes. To address these issues, this work proposes Work Bounty, a decentralized freelancing marketplace powered by Web3 and blockchain technologies. Built on the Ethereum blockchain, the platform utilizes smart contracts to automate critical processes such as job creation, bidding, work delivery, and escrow-based payments, thereby eliminating intermediaries and enhancing trust. Authentication is streamlined using MetaMask wallets, enabling secure, passwordless access tied to unique cryptographic addresses. Job details and deliverables are stored on the InterPlanetary File System (IPFS) to ensure immutable and tamper-resistant data storage, while a blockchain-based reputation system provides transparent, unalterable user ratings. Experimental evaluation on the Ethereum test network demonstrates that the system achieves a 100% success rate in smart contract executions and reduces overall transaction costs to 2–3% equivalent gas fees, compared to the 10–20% fees on centralized platforms. MetaMask authentication achieved a 98.7% success rate, and beta testing with freelancers and clients revealed 92% user satisfaction with the platform&s;s ease of use and trustworthiness. These results highlight the system&s;s potential to provide a secure, transparent, and cost-effective alternative to traditional freelancing platforms, fostering a more equitable and globally accessible gig economy aligned with Web3 principles.

Rasheed M, Parth Desai, Sujit Gujar

Propagation latency is inherent to any distributed network, including blockchains. Typically, blockchain protocols provide a timing buffer for block propagation across the network. In leader-based blockchains, the leader -- block proposer -- is known in advance for each slot. A fast (or low-latency) proposer may delay the block proposal in anticipation of more rewards from the transactions that would otherwise be included in the subsequent block. Deploying such a strategy by manipulating the timing is known as timing games. It increases the risk of missed blocks due to reduced time for other nodes to vote on the block, affecting the overall efficiency of the blockchain. Moreover, proposers who play timing games essentially appropriate MEV (additional rewards over transaction fees and the block reward) that would otherwise accrue to the next block, making it unfair to subsequent block proposers. We propose a double-block proposal mechanism, 2-Prop, to curtail timing games. 2-Prop selects two proposers per slot to propose blocks and confirms one of them. We design a reward-sharing policy for proposers based on how quickly their blocks propagate to avoid strategic deviations. In the induced game, which we call the Latency Game, we show that it is a Nash Equilibrium for the proposers to propose the block without delay under homogeneous network settings. Under heterogeneous network settings, we study many configurations, and our analysis shows that a faster proposer would prefer not to delay unless the other proposer is extremely slow. Thus, we show the efficacy of 2-Prop in mitigating the effect of timing games.

Marwa Mouallem, Lorenz Breidenbach, Ittay Eyal, Ari Juels

Smart contracts are stateful programs deployed on blockchains; they secure over a trillion dollars in transaction value per year. High-stakes smart contracts often rely on timely alerts about external events, but prior work has not analyzed their resilience to an attacker suppressing alerts via bribery. We formalize this challenge in a cryptoeconomic setting as the \emph{alerting problem}, giving rise to a game between a bribing adversary and~$n$ rational participants, who pay a penalty if they are caught deviating from the protocol. We establish a quadratic, i.e.,~$O(n^2)$, upper bound, whereas a straightforward alerting protocol only achieves~$O(n)$ bribery cost. We present a \emph{simultaneous game} that asymptotically achieves the quadratic upper bound and thus asymptotically-optimal bribery resistance. We then present two protocols that implement our simultaneous game: The first leverages a strong network synchrony assumption. The second relaxes this strong assumption and instead takes advantage of trusted hardware and blockchain proof-of-publication to establish a timed commitment scheme. These two protocols are constant-time but incur a linear storage overhead on the blockchain. We analyze a third, \emph{sequential alerting} protocol that optimistically incurs no on-chain storage overhead, at the expense of~$O(n)$ worst-case execution time. All three protocols achieve asymptotically-optimal bribery costs, but with different resource and performance tradeoffs. Together, they illuminate a rich design space for practical solutions to the alerting problem.

Muhammad Imran

The Internet of Things (IoT) security landscape requires the architectural solutions that can address the technical and operational challenges across the heterogeneous environments. The IoT systems operate in different conditions, and security issues continue to increase. This paper presents the comprehensive security framework for IoT that should integrate the Trusted Execution Environments (TEEs) with the semantic middleware and blockchain technologies. The work provides a systematic analysis of the architectural patterns based on more than twenty recent research works and the existing standards, and it proposes a layered security architecture. The architecture includes the hardware rooted trust at peripheral level, the zero trust principles at network level, and the semantic security mechanisms at application level. The framework focuses on practical implementation aspects such as the performance overhead, interoperability requirements, and the compliance with new regulations, which are very important for the real IoT deployments. The paper reports quantitative metrics which include the cryptographic performance on Cortex-M class microcontrollers with the detection accuracy rates and the energy consumption values. The proposed architecture shows that cross-layer security integration can provide defense in depth while it still satisfies the constraints of resource-limited IoT environments. The discussion highlights open challenges and the future research directions for the IoT security architectures that include the post-quantum migration, secure federated model exchange and the automated compliance verification.

Ruisheng Shi, Zhiyuan Peng, Tong Fu, Lina Lan · 6 authors

Many tracking companies collect user data and sell it to data markets and advertisers. While they claim to protect user privacy by anonymizing the data, our research reveals that significant privacy risks persist even with anonymized data. Attackers can exploit this data to identify users' accounts on other websites and perform targeted identity alignment. In this paper, we propose an effective identity alignment scheme for accurately identifying targeted users. We develop a data collector to obtain the necessary datasets, an algorithm for identity alignment, and, based on this, construct two types of de-anonymization attacks: the \textit{passive attack}, which analyzes tracker data to align identities, and the \textit{active attack}, which induces users to interact online, leading to higher success rates. Furthermore, we introduce, for the first time, a novel evaluation framework for online tracking-based identity alignment. We investigate the key factors influencing the effectiveness of identity alignment. Additionally, we provide an independent assessment of our generated dataset and present a fully functional system prototype applied to a cryptocurrency use case.

Ruisheng Shi, Ziding Lin, Haoran Sun, Qin Wang · 8 authors

Cryptomining poses significant security risks, yet traditional detection methods like blacklists and Deep Packet Inspection (DPI) are often ineffective against encrypted mining traffic and suffer from high false positive rates. In this paper, we propose a practical encrypted cryptomining traffic detection mechanism. It consists of a two-stage detection framework, which can effectively provide fine-grained detection results by machine learning and reduce false positives from classifiers through active probing. Our system achieves an F1-score of 0.99 and identifies specific cryptocurrencies with a 99.39\% accuracy rate. Extensive testing across various mining pools confirms the effectiveness of our approach, offering a more precise and reliable solution for identifying cryptomining activities.

Andrew Hoyt

At the dawn of the twentieth century, one of the most dynamic but forgotten communities of transnational journalistic innovators were the “libertarian-socialists.” Known more commonly today as “anarchists,” these deeply maligned and misrepresented propagandistic rebels were rooted in the old print-culture traditions of the transatlantic abolitionists and the republican movements of the mid-nineteenth century; yet, their historically specific use of periodicals represents a unique tactical deployment of newspapers in service to a decentralized and diasporic social movement. As such, the anarchist press is a critical subject of study for students of transnational journalism, highlighting the medium’s capacity to transform working-class migrant-chains into a cohesive and resilient globe-spanning social-network that was seen as a serious threat by the leaders of finance, government, and industry.

Manaf Ahmed, Mohammed Adnan, Ali Matar, Faez Hlail Srayyih · 7 authors

Predicting cryptocurrency price is challenging owing to high volatility, less historical data, and the impact of external parameters like news, public sentiment, and regulatory announcements. This challenge is tackled in this research by employing models of deep learning like Recurrent Neural Network (RNN), Bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU)—to predict Bitcoin's OHLC prices daily. Based on historical time-series data of Coin Codex, the research uses an autoencoder-based feature extraction method with five-day sliding window method for sequence generation. Hyperband optimization is used to tune hyperparameter of each model. The result shows that BiLSTM performs better than all the other models with minimum Mean Squared Error (MSE = 0.001183), Mean Absolute Error (MAE = 0.026090), and maximum R² score (0.980596) after optimization. The results emphasize the significance of deep learning in capturing nonlinear dynamics in time series of financial applications and bear testimony to the effectiveness of hyperparameter tuning in enhancing model accuracy. The study enhances the development of prediction tools for digital asset markets and enables more informed investment decisions.

MBAH-NJEI ACHA Patience

The rapid evolution of digital technologies has fundamentally disrupted traditional arbitration processes, introducing new complexities and opportunities at the intersection of blockchain technology, smart contracts, and online arbitration. This study examines the legal and practical challenges arising from the integration of blockchain technology, smart contracts, and online arbitration. The research addresses the problem of adapting traditional dispute resolution frameworks to decentralized automated agreements that transcend national borders. Using doctrinal analysis and comparative review of legal sources, case studies, and real-world platforms, the study identifies key types of smart contracts and evaluates on-chain and off-chain arbitration models. Findings highlight both the efficiency and transparency offered by blockchain-based dispute resolution, as well as persistent issues such as jurisdictional uncertainty, enforceability, technical vulnerabilities, and privacy risks. The results underscore the need for clear legal standards, technical safeguards, and the adoption of advanced technologies. The study recommends promoting interoperability, specifying governing law in smart contracts, and leveraging AI and off-chain execution systems to enhance the robustness and adaptability of digital dispute resolution.

Minh Trung Tran, Brayden Killeen, Tony McGrath

Slow Liquidity Drain (SLID) scams have recently emerged as a subtle and persistent threat within the decentralized finance (DeFi) environment. While prior studies have introduced heuristic and machine learning techniques for identifying SLID behaviors, deploying these methods in real-world industrial systems reveals substantial challenges. In particular, updated large-scale datasets collected from operational DeFi platforms show that SLID behaviors and their effective detection time-range evolve over time, rendering previously reported fixed thresholds unreliable for production use. This work presents a data-driven reassessment of SLID detection under contemporary DeFi conditions and demonstrates that the observation window required for reliable detection shifts as new data and new scam behaviors emerge. Building on these findings, we introduce an industry-oriented detection framework that decouples machine learning models from time-range selection and supports adaptive operation without retraining or feature redesign. Rather than proposing a single deployment strategy, we outline two practical operating modes: a slow-adaptive mode that prioritizes stability and auditability through periodic window updates, and a fast-adaptive mode that enables flexible sensitivity and tiered alerts for security-driven environments. Together, these designs translate empirical insights into concrete system architectures suitable for large-scale DeFi monitoring, bridging the gap between academic SLID detection research and production deployment requirements.

Tsokwa Daniel, Emmanuel Siman*

Abstract: The research presents a Privacy-Preserving Data Exchange (PPDE) framework for electronic health records (EHRs) that integrates elliptic-curve cryptography (ECC), zero-knowledge proofs (ZKPs), and capability tokens on a permissioned blockchain to create a cohesive, policy-compliant data-sharing workflow. In this system, IoT gateways establish ECC-based session keys to encrypt measurements at the edge, while only cryptographic hashes and pointers to the encrypted data are stored on a consortium blockchain, ensuring data confidentiality with tamper-evident integrity and auditable provenance. Healthcare providers issue Access Request Transactions that include ECC signatures and ZKPs to prove policy compliance (such as patient consent and role-based access) without revealing sensitive details. Off-chain ZKPs validate authorization decisions, with on-chain records providing verifiable evidence of conformance and access history, and a capability token is granted to authorize future data access within defined scopes and time windows. The framework emphasizes end-to-end confidentiality, privacy-preserving authorization, and tamper resistance through the blockchain. Our evaluation analyzes network throughput, end-to-end delay, packet delivery ratio, scalability, and energy efficiency, with particular attention to the overhead from blockchain operations, ECC, and ZKP verification. Results show improved privacy and security for EHR data, with only nominal overhead relative to the privacy and integrity gains, and favorable scalability and energy performance in realistic deployments.

Amir Bushansky

Smart contracts—self-executing agreements operating on decentralized blockchain networks—challenge the fundamental assumptions underlying traditional contract law. This Article challenges the prevailing scholarly approach that seeks to reshape smart contracts to fit existing doctrinal frameworks. Instead, it argues for adapting contract law itself to better accommodate smart contracts, given their unique advantages in cyberspace. Building on two complementary premises—that contract law primarily operates as “mimicking law,” reflecting the presumed expectations and preferences of contracting parties, and that default rules function as behavioral reference points that shape party behavior—the Article develops a positive theory predicting the expectations and preferences of parties entering into smart contracts. It contends that these expectations fundamentally differ from those that shape traditional contractual engagements and that, when existing doctrines diverge from those expectations, such doctrines can no longer serve as effective legal default rules. The Article concludes by proposing a set of normative adaptation principles designed to align contract law with the expectations underlying smart contracts. Grounded in and illustrated through Israeli contract law as a case study, these principles provide a foundation for evaluating and implementing doctrinal adaptations across diverse legal systems.

A.M. Soloviev, N.Yu. Schlundt, T.G. Slyusareva

The modern development of decentralized ledger and blockchain technologies has led to the emergence of smart contracts, which are becoming an important tool in the digital economy, transforming existing understandings of the conclusion and fulfillment of obligations in the digital environment. The authors believe that recognizing the objective multi-paradigmatic nature of this phenomenon will not only facilitate the integration of modern technological advances into the legal system but also stimulate the growth of an innovative economy, increase trust in digital platforms, and ensure their adaptation to the rapidly changing conditions of the digital market. The conclusions include proposals for legislative development based on a multi-paradigmatic approach, which assumes a comprehensive understanding of the legal status of smart contracts, taking into account technological, legal, and socioeconomic aspects. This, according to the authors, will help identify the most promising ways to integrate digital contractual instruments into the modern legal system.

Fitrio Pakana

Smart contract security mainly aims to discover vulnerabilities in contract code based on the assumption that malicious behavior emerges from exploitable execution paths. This assumption is increasingly misaligned with modern blockchain systems, where off-chain automated agents continuously observe on-chain state and act autonomously. In this paper, we argue that smart contracts now act as public signalling mechanisms, capable of encoding command-and-control (C2) primitives through events and persistent storage variables. Off-chain agents, including LLM-based agents, convert benign-looking state changes into coordinated, high-impact actions across protocols and chains, semantically interpreting these signals. This kind of behaviour does not require code-level exploitation and thus can be built to evade existing analysis methods by construction. We explore how such a kind of coordination enables delayed activation, selective targeting, and deception, and explain why contract-centric security models fall short of such risks. We conclude by examining the main challenges and the directions for securing agent-mediated blockchain ecosystems.

De Wet Swanepoel

PURPOSE OF REVIEW: This review examines recent advances in community-based care for conductive hearing loss in low- and middle-income countries (LMICs), focusing on developments that have materially shifted the feasibility of decentralized service delivery across prevencommution, screening and diagnosis, medical management, and rehabilitation. RECENT FINDINGS: World Health Organization guidance now explicitly endorses task-sharing for primary ear and hearing care, including community health worker-facilitated hearing aid provision. Long-term follow-up from pneumococcal vaccination trials demonstrates sustained otitis media prevention and improved hearing outcomes 16-20 years postvaccination. Smartphone-based screening and diagnostic tools, including AI-assisted otoscopy and mobile tympanometry, are enabling accurate community-level identification without specialist infrastructure. Medical management of common conductive hearing loss (CHL) causes, including cerumen impaction and uncomplicated chronic suppurative otitis media, can be safely delivered by trained community health workers and primary care nurses. Community-based hearing aid provision achieves sustained rehabilitation outcomes comparable to specialist-led models across diverse LMIC settings. SUMMARY: Recent evidence demonstrates that community-based CHL care is feasible, safe, and effective when supported by appropriate training, technology, and task-sharing frameworks. The primary constraint to scale is no longer technological feasibility but prioritisation within health planning and financing frameworks.

Saeed Khorrami, Maria Carmen Falvo, M. Pompili

Energy Communities (ECs) have emerged as central legal instruments for decentralized renewable energy deployment across Europe; however, their long-term viability depends critically on financial sustainability mechanisms that remain inadequately understood. This study examines the economic foundations of ECs through a narrative literature review of revenue generation, cost allocation, and the capital mobilization pathways in three representative European markets (Germany, Spain, and Italy). A structured Scopus database search identified 280 peer-reviewed studies published between 2019 and 2025. Following systematic screening, 89 articles were selected for analysis through bibliometric mapping in R (Biblioshiny) and qualitative synthesis in NVivo. The analysis reveals that stable feed-in tariffs, tax incentives, and self-consumption remuneration schemes form the primary revenue mechanisms, while cost management effectiveness varies substantially across countries due to differing grid-charge structures and administrative frameworks. Capital access remains constrained for smaller communities despite hybrid financing innovations combining public grants, cooperative equity, and emerging crowdfunding mechanisms. Regulatory heterogeneity, high upfront investment requirements, and limited institutional credit availability continue to impede scalability. The findings emphasize that achieving widespread EC adoption requires harmonized policy frameworks, transparent cost-sharing arrangements, and diversified investment instruments that align local participation with national decarbonization objectives while ensuring equitable access across diverse socio-economic contexts.

Hoseung Kang, Yeonchan Kang, Doojin Ryu, Robert I. Webb

This study evaluates machine learning models for forecasting daily Bitcoin returns using on-chain, macroeconomic, and market variables from January 2017 to December 2023. We implement a rolling-window framework with window lengths ranging from 365 to 730 days and compare several machine learning models against an autoregressive benchmark. Random Forest and Support Vector Machine achieve the lowest forecasting errors consistently across volatility regimes. Feature importance analysis using permutation importance and SHAP decomposition reveals that on-chain variables account for approximately 50 per cent of total forecasting contribution, with transaction fees and mining-related metrics ranking among the top important variables. Traditional market indicators such as VIX show limited relevance for Bitcoin return forecasting. These findings highlight the distinct informational value of blockchain-native variables for cryptocurrency forecasting.

Kazunori Ohumi

Why does the pursuit of "correct answers" and optimization suffocate modern organizations in the AI era? This paper analyzes Uniqlo (Fast Retailing) not merely as an excellent company, but as a "Generative Circular Enterprise" that structurally refuses to crystallize. It serves as a survival manifesto using Universal Phase Crystallization Theory (UPCT) to shift corporate OS from static management to dynamic Generativity. Highlights The Shift in Era: Contrasts the failure of traditional "S-origin" (Plan-driven) management in the population onus era with Uniqlo's "Φ-origin" (Generation-driven) adaptability. Structural Fluidity: Reveals how the absence of a "Corporate Planning Department" functions as a deliberate mechanism to prevent the fixation of strategy (S) and maintain organizational metabolism. Epistemology of Execution: Reinterprets "1% Plan, 99% Execution" not as spirit, but as a rational cycle of discarding static maps (S) to navigate the fluid reality (Φ). Purpose as OS: Defines "Global One" and "LifeWear" not as static slogans, but as a distributed operating system that enables autonomous decentralized processing. The AI Trap: Warns that using AI for mere optimization shrinks the "basin of attraction," and proposes using AI as a "solvent" to melt rigid structures. Summary Modern corporations face a paradox: the more they utilize data and AI to optimize efficiency, the more they lose vitality and resilience. This paper diagnoses this pathology as the "Curse of Crystallization" defined by Universal Phase Crystallization Theory (UPCT). Traditional organizations fixate on "Correct Answers (S)" derived from past data, creating rigid structures that cannot adapt to the "Generative Flow (Φ)" of the population onus era. Uniqlo (Fast Retailing) presents a counter-model. By reversing the management vector to Φ→G→S, Uniqlo starts with formless will and market intuition, crystallizing them into provisional products only to immediately deconstruct and regenerate them. This "Non-Fixed Structure"—exemplified by the lack of a central planning department and the fluid leadership of Tadashi Yanai—allows the giant enterprise to move with the agility of a startup, constantly surfing the phase transition between order and chaos. Finally, the paper addresses the critical challenge of the Artificial Intelligence era. If AI is used solely to reinforce past success models (S, it accelerates organizational rigidity (the shrinking of the basin of attraction). We argue that the role of human intelligence is to act as a "Generator" that utilizes AI to melt frozen concepts, ensuring the organization remains a living, metabolic system. This is a proposal for shifting from a "Snapshot Ontology" to a "Life-OS" in business management. Author’s Related Works Ohumi, K. (2025). Manifesto of the Life OS: The "It from Wave" Philosophy. Zenodo. https://doi.org/10.5281/zenodo.18106437 Ohumi, K. (2025). A Sampling-Theoretic Reinterpretation of Quantum Uncertainty and Wave Function Collapse. Zenodo. https://doi.org/10.5281/zenodo.18004579 Ohumi, K. (2025). Observation as Operational Crystallization: Resolving Quantum Paradoxes. Zenodo. https://doi.org/10.5281/zenodo.18220191 Ohumi, K. (2025). It from Wave: Phase Propagation as Physical Basis of Information. Zenodo. https://doi.org/10.5281/zenodo.18256968 Ohumi, K. (2025). Ontological Reconstruction of Quasi-Particles. Zenodo. https://doi.org/10.5281/zenodo.18140041 Ohumi, K. (2025). Envelopment over Unification: Recovering Einstein’s Dream. Zenodo. https://doi.org/10.5281/zenodo.18244683 Ohumi, K. (2025). Sampling, Horizons, and Recurrence: Reframing Thermal Pure States and Black Hole Information. Zenodo. https://doi.org/10.5281/zenodo.18364507 Ohumi, K. (2025). π Paradox: Relation-First Information and the Geometry of Meaning. Zenodo. https://doi.org/10.5281/zenodo.18204829 Ohumi, K. (2025). Dark Energy as a Diffusive Phase of a Relational Universe. Zenodo. https://doi.org/10.5281/zenodo.18081786 Ohumi, K. (2025). Envelopment Ethics: Generativity-First Inclusion. Zenodo. https://doi.org/10.5281/zenodo.18256968 Ohumi, K. (2025). Enveloping the Free Will–Determinism Divide. Zenodo. https://doi.org/10.5281/zenodo.18287169 Ohumi, K. (2025). Rationality without Transition. Zenodo. https://doi.org/10.5281/zenodo.18193256 Ohumi, K. (2025). Over-Immune Infosphere: When Protection Becomes Rigidity. Zenodo. https://doi.org/10.5281/zenodo.18149385 Ohumi, K. (2025). The KPI Trap: Over-Optimization and Meaning Collapse. Zenodo. https://doi.org/10.5281/zenodo.18264106 Ohumi, K. (2025). The WGS Model: The Implementation of Generative Governance. Zenodo. https://doi.org/10.5281/zenodo.18308450 Ohumi, K. (2025). Envelopment Integration: Reuniting Ethics, Well-Being, and Value. Zenodo. https://doi.org/10.5281/zenodo.18332397 Ohumi, K. (2025). Resonant Management. Zenodo. https://doi.org/10.5281/zenodo.18162380 Ohumi, K. (2025). Resonant Politics. Zenodo. https://doi.org/10.5281/zenodo.18180888 Ohumi, K. (2025). Demographic Decline and Environmental Crisis as Ontological Outcomes. Zenodo. https://doi.org/10.5281/zenodo.18197092 Ohumi, K. (2025). Population Onus as an Ontological Crisis. Zenodo. https://doi.org/10.5281/zenodo.18356710 Ohumi, K. (2026). Universal Phase Crystallization Theory (UPCT) Phase I: A Unified Resolution of Quantum Paradoxes via Temporal Sampling. Zenodo. https://doi.org/10.5281/zenodo.18230537 Ohumi, K. (2026). A Phase Theory of Intelligence and Mind: Reframing Cognition as Generative–Crystallization Dynamics under Universal Phase Crystallization Theory (UPCT). Zenodo. https://doi.org/10.5281/zenodo.18430732 Ohumi, K. (2026). Universal Phase Crystallization Theory (UPCT) Phase II: A Phase Transition Law for Generative Systems under Measurement Optimization. Zenodo. https://doi.org/10.5281/zenodo.18408708 Ohumi, K. (2026). Why "Correct" Ideologies Freeze Societies: A UPCT-Based Structural Analysis of Ideological Crystallization from Antiquity to the 20th Century. Zenodo. https://doi.org/10.5281/zenodo.18437668 Ohumi, K. (2026). The Silent Revolution of UPCT: The Birth of a New Physics to Thaw a Frozen World A Scientific Manifesto. Zenodo. https://doi.org/10.5281/zenodo.18439197 Ohumi, K. (2026). Civilizational Symmetry Breaking and the Pathology of Granulation under Strong Constraint Toward a Phase-Theoretic Account of Contemporary Crises. Zenodo. https://doi.org/10.5281/zenodo.18467976 Ohumi, K. (2026). From the Crystallized Self to the Generative Field Reclaiming the Observer's Perspective, the Ontological Value of Experience, and the Misalignment of Reason in Modernity. Zenodo. https://doi.org/10.5281/zenodo.18493557 Ohumi, K. (2026). Foundational Principles of Resonance Economics Reorienting Economic Theory from Output Maximization to Generative Sustainability. https://doi.org/10.5281/zenodo.18500861 Ohumi, K. (2026). Integration into the Life-OS Generativity Framework: Hokusai's The Great Wave off Kanagawa as an Ontological Model. https://doi.org/10.5281/zenodo.18505627 Ohumi, K. (2026). From Proof to Resonance: A Φ-Ontology of Existence, Labor, Education, and Economic Life. https://doi.org/10.5281/zenodo.18515955 Ohumi, K. (2026). Dialectics as a Relational Logic of Life: From Linear Ascent to Spiral Circulation. https://doi.org/10.5281/zenodo.18522371 Ohumi, K. (2026). Returning to the Source of Philosophy: Affirmation of Life as the Life-OS and a Radical Point of Departure. https://doi.org/10.5281/zenodo.18529485 Ohumi, K. (2026). Universal Phase Crystallization Theory (UPCT): The Pathology of Optimization and the Restoration of Generativity — Beyond Snapshot Ontology. https://doi.org/10.5281/zenodo.18597207

T.G. Aigumov, F.M. Abdulmukminova, A.I. Sakhbieva

This article explores the role of zero-knowledge cryptographic proofs as a basic trust mechanism for financial and government digital services. The theoretical section explains how such proofs shift compliance verification from a "reveal data and verify" mode to a "prove property and admit access to service" mode, thereby reducing transaction costs, mitigating information asymmetries, and limiting agency costs. Drawing on insights from new institutional economics and mechanism theory, it demonstrates how formalized verifiability improves rule robustness, makes truthful reporting individually rational, and reduces the negative externalities of leaks. The analytical section systematizes classes of constructions, highlighting tradeoffs between proof size, latency, the need for trusted configuration, and operational risks. The practical section describes applications in payment infrastructure, lending, insurance, cross-border settlements, digital identity, and ledger management: range constraint verification, threshold and attribute verification, verifiable computation, and selective disclosure. It is demonstrated that with proper design, it is possible to combine data minimization with targeted transparency and effective enforcement, including in projects involving central bank digital money and pan-European identity wallets. Implementation metrics are discussed: marginal cost of proof and verification, average latency, fault tolerance, and the proportion of cases where primary documents are replaced with proof of property.

Shaoyu Li, Hexuan Yu, Shanghao Shi, Md Mohaimin Al Barat · 7 authors

With the growing demand for wireless spectrum, dynamic spectrum sharing (DSS) frameworks such as the Citizens Broadband Radio Service (CBRS) have emerged as practical solutions to improve utilization while protecting incumbent users (IUs) such as military radars. However, current incumbent protection mechanisms face critical limitations. The Environmental Sensing Capability (ESC) requires costly sensor deployments and remains vulnerable to interference and security risks. Alternatively, the Incumbent Informing Capability (IIC) requires IUs to disclose their identities and operational parameters to the Spectrum Coordination System (SCS), creating linkable records that compromise operational privacy and mission secrecy. We propose IU-GUARD, a privacy-preserving spectrum sharing framework that enables IUs to access spectrum without revealing their identities. Leveraging verifiable credentials (VCs) and zero-knowledge proofs (ZKPs), IU-GUARD allows IUs to prove their authorization to the SCS while disclosing only essential operational parameters. This decouples IU identity from spectrum access, prevents cross-request linkage, and mitigates the risk of centralized SCS data leakage. We implement a prototype, and our evaluation shows that IU-GUARD achieves strong privacy guarantees with practical computation and communication overhead, making it suitable for real-time DSS deployment.