Blockchain Papers

Ethan Flowerday

No abstract is available for this record.

Anthony Coslett

We study what model-identifying information leaks through commercial language-model APIs that expose top-k token log probabilities. Building on extreme-value theory predictions for logit order-statistic gaps, we confirm that the normalized third logit gap (δ norm) remains near the Gumbel-class constant ≈0.318 across 6 models from 3 providers (OpenAI, Google Vertex AI, xAI) and 3 independent measurement sessions, demonstrating that output-layer universality persists through API truncation and quantization. We introduce a PPP-residualization transform that removes the dominant tail scale factor and reveals a low-dimensional but stable endpoint-specific geometry in the remaining gap spectrum. Contrary to common assumption, "provider" is not a geometrically coherent label: models do not cluster by corporate origin under these observables, but they do separate by model identity across independent sessions. Using a challenge-response protocol with centroid averaging and per-model thresholds, we demonstrate cross-session endpoint verification with a 0.83% breach rate (119/120 correct identifications across three temporal sessions); per-model thresholds eliminate all breaches on this dataset. We observe a robustness phase transition governed by enrollment depth. Under single-session enrollment, prompt selection is load-bearing: the majority of bootstrapped banks fail to separate the six endpoints. Under two-session enrollment, bank sensitivity collapses on this dataset, and a bank compiler produces small compiled banks that exceed the margin of larger uncompiled banks. A dimensionless robustness parameter SNR(K,S) unifies both axes: prompt count K and enrollment depth S jointly govern the transition from bank-sensitive to bank-robust verification. We discuss operational implications for re-enrollment cadence and template management in production deployments. Addendum (02/26/2026): Post-publication results extend this framework in two directions. A distillation experiment across six training protocols demonstrates that a model's structural fingerprint (weight-geometry regime) is completely invariant to knowledge distillation, while its functional fingerprint (PPP-residual template) converges 31--52% toward the teacher's — enabling forensic detection of distillation provenance through API measurements alone. A conditional impossibility theorem, machine-checked in Coq (41 theorems, 0 Admitted), proves that no standalone model can spoof another's PPP-residual template across independent challenge prompts without exhausting its KL divergence budget, under four explicit trust assumptions. Technical Note: Gap Invariance: Why PPP Measurements Are Domain-Independent by Construction (DOI: 10.5281/zenodo.19275524) The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Newest addition: Technical Note: The Disappearing Window — AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098) Paper 1: The δ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275) Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711) Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608) Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071) Paper 5: Beneath the Character: The Structural Identity of Neural Networks — Mathematical Evidence for a Non-Narrative Layer of AI Identity (DOI: 10.5281/zenodo.18907292) Paper 6: Which Model Is Running?: Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10.5281/zenodo.19008116) Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966) Paper 8: What Counts as Proof? — Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540) Paper 9: Composable Model Identity — Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10.5281/zenodo.19099911) Paper 10:Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10.5281/zenodo.19118807) Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10.5281/zenodo.19216634) Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10.5281/zenodo.19298857) Paper 13: Safety-Alignment Removal as a Model-Identity Failure — Structural Evidence from Published Weight-Level Mutation Checkpoints (DOI: 10.5281/zenodo.19383019) Technical Note: Agent Identity Is Not Model Identity (DOI: 10.5281/zenodo.19240883) Technical Note: Measured Model Substitution Under Valid Agent Credentials (DOI: 10.5281/zenodo.19342848) Technical Note: Artifact Identity Is Not Runtime Identity — Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) Formal Verification Stack for Neural Network Structural Identity (IT-PUF Coq Proofs) (DOI: 10.5281/zenodo.18930621) Copyright (c) 2026 Anthony Ray Coslett / Fall Risk AI, LLC. All Rights Reserved. Confidential and Proprietary. Patent Pending (Applications 63/982,893, 63/990,487, 63/996,680, 64/003,244).

Daniel Rabitha, Novi Dwi Nugroho, Ismail, Marpuah · 10 authors

This study explores the strategic shift in terrorist financing methods employed by the Mujahidin Indonesia Timur (MIT) network, specifically the transition from decentralized crowdfunding to centralized single-donor mechanisms. Using a qualitative case study grounded in Fraud Diamond Theory, this research investigates how foreign philanthropic channels are manipulated to support militant operations. The findings reveal that single donors possess the technical sophistication to exploit transnational financial systems, specifically through the manipulation of Non-Profit Organizations (NPOs) and informal charity networks. Consequently, this study proposes an enhanced risk-profiling framework for Financial Intelligence Units (FIUs) that prioritizes individual behavioral patterns and ideological alignments over mere transactional volumes, offering critical insights for anticipatory counter-terrorism financing measures

Faris Abuhashish, Waleed Maqableh, Nidal Yousef, Mohd Shahrizal Sunar · 5 authors

The Metaverse is turning into a constantly growing three-dimensional space that is radically changing the nature of digital interaction through the generative Artificial Intelligence and MetaIntelligence. The chapter outlines how Generative Adversarial Networks, Large Language Models, and Diffusion Models have been used to build immersive virtual worlds, non-player characters and plot progression. It proposes MetaIntelligence as an inclusive model of meta-learning, cyclic improvement and interoperability of heterogeneous systems. Moreover, the chapter provides an overview of how distributed ledger technology, cloud computing, and edge-computing functionality are foundations of scalable world construction, the consideration of consistency, user well-being, ethical and environmental implications, and the opportunities of symbiotic human-AI interaction and virtual/real world interface integration.

Inna P. Chaika, Oleksandr V. Khursa, Ivan O. Kaspir

The article examines the transformation of the paradigm of social and environmental responsibility in Ukraine’s energy sector amid the unprecedented challenges of martial law and the need to align with European standards of sustainable development. The relevance of the study is driven by the critical need to combine energy security with corporate social responsibility, urgent decarbonization, and the transition to a decentralized generation model. The aim of the article is to theoretically substantiate strategic directions and develop a practical set of tools for improving the management of environmental logistics in an energy holding (using the example of DTEK Group) through the integration of best European practices and the adaptation of logistical processes to the unique challenges of martial law. Special attention is given to the analysis of the company’s social initiatives, such as support for veterans, internally displaced persons, and local communities, as well as the formation of a corporate culture of sustainable development. The methodological basis of the research is a system approach to managing the environmental and social footprint of the enterprise. The study employs: the comparative analysis method – to examine the experience of European energy leaders; the systematization and classification method – in developing the strategic architecture of social and environmental management; the logical generalization method – to form a strategy for optimizing logistics flows. As a result of the study, a «resilience paradox» was identified, where military threats become a catalyst for the accelerated transition to renewable energy sources. A comprehensive benchmarking of the strategies of global energy companies was conducted, allowing for the adaptation of European experience to domestic realities. The authors have developed and structured an applied system of key performance indicators (KPI) for green logistics, covering three strategic areas: decarbonization of supply chains, operational energy efficiency of infrastructure, and social responsibility within the circular economy. The feasibility of implementing the Green Supply Chain Management (GSCM) conception has been substantiated, which involves integrating social and environmental criteria into supplier selection, inventory management, and the disposal of renewable energy components. It has been demonstrated that the implementation of GSCM is an indispensable condition for compliance with modern international standards, enhancing social trust, and attracting green financing. Prospects for further research have been identified in the area of digital integration of Ukrainian and European energy hubs, taking into account the social aspects of sustainable development.

Petar Zhivkov, Juri D. Kandilarov

Hyperparameter optimization for cryptocurrency trading strategies encounters distinct challenges owing to continuous operation, volatility rates 3–4 times higher than equity indices, and price dynamics influenced by market sentiment. Bayesian optimization (Tree-Structured Parzen Estimator, TPE) and evolutionary algorithms (Differential Evolution, DE) are great for machine learning, but there are not many systematic comparisons for trading cryptocurrencies. This research evaluates Random Sampling, TPE, and DE through 36 factorial experiments, comprising 3 trading strategies (3, 4, and 5 hyperparameters) × 3 optimizers × 4 cryptocurrency pairs (BTC/USDT, ETH/USDT, INJ/USDT, SOL/USDT), resulting in 14,400 backtesting trials with walk-forward validation. TPE won 75% of strategy–asset pairs (9 of 12), reaching 90% of optimal performance within 13–17% of trial budgets. We find strategy-specific optimizer compatibility: mean-reversion strategies show DE underperformance independent of topology (−1% to −8%), whereas trend-following strategies show consistent DE competitiveness across assets (+13% to +37%). Most notably, for the same strategy, parameter space topology differs significantly between assets (trend following: 4.6% viable on BTC to 82% on ETH = 17.8×; mean reversion: 10.8% on ETH to 92% on SOL = 8.5×), indicating that topology results from strategy–asset interaction rather than intrinsic properties. Complete testing failures and widespread severe overfitting point to regime non-stationarity as a fundamental problem. Among the contributions are: (1) evidence shows that topological effects are dominated by optimizer–strategy compatibility (DE fails on mean-reversion strategies even in 92% viable spaces, but succeeds on trend-following strategies regardless of topology, spanning 13.6–82% viable spaces); (2) this is the first systematic Bayesian versus evolutionary comparison across 4 cryptocurrency assets; (3) parameter space topology emerges from strategy–asset interaction, varying up to 17.8-fold; and (4) single-period backtests inadequately identify parameter instability.

Ziya Tan, Zijie Pan, Ying Liang, Shuyuan Yang

Secure and bandwidth-conscious transmission of model updates is a central bottleneck in distributed machine learning. Existing secure aggregation and homomorphic encryption pipelines either reveal more than the task requires or incur prohibitive computation and communication costs. We introduce a verifiable functional encryption (VFE) framework that releases only the intended linear functions of client gradients while providing end-to-end integrity and privacy guarantees under standard lattice assumptions. Our instantiation, FlowAgg-FE, combines two novel components. First, KS-IPFE, a key-splittable inner-product FE scheme, supports per-round weighted aggregation, vector packing, and on-the-fly function changes without client re-encryption; function keys are distributed across two non-colluding helpers, eliminating a single point of trust and enabling lightweight, homomorphically verifiable tags on decrypted outputs. Second, PaS-Stream is a rate-adaptive encryption-and-compression pipeline that couples sketch-based gradient compression with batched FE ciphertext streaming, ensuring unbiased aggregation in the presence of stragglers and dropouts. We further bind client-side clipping to zero-knowledge range proofs and offer an optional differentially private release layer that composes with FE to yield (ε,δ)-privacy. A prototype based on LWE demonstrates practicality across cross-device and cross-silo training: client uplink is reduced by 1.9–3.4× and server CPU time by 1.6× versus state-of-practice encrypted secure aggregation, with accuracy within 0.3% of plaintext baselines and correctness preserved under up to 30% client dropout. These results show that verifiable FE can make secure, communication-efficient gradient transmission viable, as appropriate for theme of security and privacy in distributed machine learning of the Special Issue.

Li, Y.Y.N.

Every standard signature scheme enforces one property: only the key holdercan sign. What the key holder signs is unconstrained. Policy enforcement-- spending limits, rate limits, access control -- lives in smartcontracts, middleware, or governance: layers that can be upgraded,bypassed, or exploited. We call this the software-layer assumption:compliance holds only if the enforcing code is correct and unmodified. We eliminate this assumption. We introduce behavior-bound signatures(BBS), in which a policy constraint delta(x) < epsilon is committed atkey generation and enforced inside the signature's zero-knowledge proof.If the action violates the policy, the ZK constraint system isunsatisfiable -- no witness, no proof, no signature. This is not asoftware check. It is a mathematical impossibility. No software canoverride. Unlike policy-based signatures (where an authority imposes policy onsigners), BBS is self-committed: the signer binds their own futurebehavior at key generation, and even the signer cannot later violate orrevoke this commitment. We formalize this as policy-soundness (PS-CMA), a security modelstrictly stronger than EUF-CMA, and prove it under standard assumptions(Pedersen binding, Poseidon CR, ZK knowledge soundness). From thissingle primitive, five independent consequences follow -- not as separatedesigns, but as necessary implications of one cryptographic root: (A) Compliance safety under f <= n-1 Byzantine faults, decoupled from honest-quorum assumptions.(B) O(1) verification and audit via a single ZK check and Pedersen homomorphic aggregation.(C) Elimination of the virtual-machine execution layer for policy-constrained transactions.(D) A gasless ledger: branch C removes metering, while ZK-encoded rate limits make spam mathematically nonexistent.(E) The first cryptographic guarantee that a compromised autonomous AI agent cannot exceed its authorized behavioral envelope.

Andrea K. Bjorklund, Simon Rollat

As business transactions and the global economy become increasingly digitalized, international investment disputes will deal with novel assets in new boundary-defiant contexts. Indeed, jurisdictional arguments and objections will likely require arbitral tribunals to confront with the uneasy task of delineating the ‘localization’ of investments in digital economy assets such as cryptocurrency, non-fungible tokens, and data-related investments. However, given that even more traditional assets have raised a variety of problems relating to territorial nexus and localization, the authors believe that the digital economy emphasizes what are essentially differences in degree rather than in kind. This chapter discusses the complexities that arise in considering the idiosyncrasies of investments in digital economy assets within a traditional territorially defined jurisdictional framework. First, the authors present some of those new digital economy assets and canvass several typical cross-border challenges inherent in international investment arbitration. Second, they question how traditional objections to jurisdiction ratione personae and jurisdiction ratione materiae might be employed when the investments in question relate to those digital developments. Third, the chapter raises questions about states’ jurisdiction to prescribe, and ponders the potential effects for purposes of jurisdiction of states asserting their authority to prescribe over investments or investors outside their territory.

Nikolaos A. Kyriazis, Shaen Corbet

This paper examines the dynamic spillovers between the VIX stock sentiment index, the Cryptocurrency Fear & Greed Index, and the returns of leading high-tech firms from 2018 through 2024. We quantify the direction and magnitude of spillovers between these variables by applying the Quantile Vector Autoregression (Q-VAR) model across lower, middle, and upper quantiles. Results indicate a stronger connection between technology firms and the VIX, with tech stocks being more influenced by cryptocurrency fear during the COVID-19 pandemic. These findings highlight the growing influence of technology firms upon financial markets, particularly during periods of heightened uncertainty in traditional markets and increased volatility in digital assets, reflecting their continually growing role in the evolving digital financial landscape. • Examines the influence of cryptocurrency fear on major tech firms from 2018 to 2024. • Applies Quantile-VAR model to analyse sentiment-driven volatility spillovers. • Highlights stronger spillovers from traditional stock fear than cryptocurrency fear. • Reveals tech stocks’ resilience during periods of high market and crypto volatility. • Identifies technology firms as key intermediaries in evolving digital financial markets.

Róbert Kovács, Christina Liew

This chapter examines the applicability of international investment law to emerging digital asset classes such as data packages, cryptocurrencies, and non-fungible tokens (NFTs). These assets, now mainstream investments, raise unique issues in terms of their protection under investment treaties. The chapter explores whether digital assets qualify as ’investments’ under traditional treaty definitions, and the application of the common protections offered under investment treaties to such assets. It assesses digital assets against criteria often applied by investment treaty tribunals to argue that digital assets can broadly be classified as investments. The chapter also analyses the key questions arising from the application of the fair and equitable treatment (FET) standard and protection against expropriation to digital assets, especially given the current relative lack of regulation in this area. Valuation complexities, including market volatility and the absence of benchmarks, are addressed, emphasising the need for close consideration of these issues in the context of investment treaty claims. Lastly, the chapter addresses structuring investments via corporate vehicles to enhance treaty protections and mitigate risks. It concludes that, while investment law can accommodate digital assets, careful structuring and awareness of treaty terms are vital for investor protection within an uncertain and ever-evolving regulatory environment.

Nagender Yamsani

The rapid expansion of enterprise-scale data ecosystems and AI-driven services has created an urgent need for autonomous governance mechanisms capable of operating across distributed, dynamic, and heterogeneous environments, where traditional centralized control models increasingly fail to provide the scalability, adaptability, and real-time compliance required by modern enterprises. In response to these limitations, this paper introduces the concept of Multi-Agent Autonomous Governance Networks (MAAGN), a novel architectural paradigm that leverages advances in multi-agent systems (MAS), policy-driven governance, and self-adaptive computing to enable truly self-regulating AI ecosystems. MAAGN is designed to distribute governance responsibilities across intelligent, cooperative agents that operate with contextual awareness, enabling localized decision-making while maintaining global policy alignment. By integrating cognitive agent models capable of perception, reasoning, and learning with layered governance frameworks that enforce regulatory, organizational, and operational constraints, the architecture supports continuous compliance and dynamic policy evolution. Furthermore, the incorporation of enterprise-scale coordination mechanisms such as decentralized consensus protocols, adaptive orchestration layers, and feedback-driven control loops ensures system-wide resilience and fault tolerance even in highly volatile environments. The study synthesizes foundational theories in MAS, contemporary developments in multi-agent reinforcement learning, and emerging governance-aware AI frameworks to propose a scalable, extensible, and future-ready model for enterprise AI control systems, positioning MAAGN as a critical enabler for trustworthy, transparent, and autonomous digital infrastructures.

Dar Gilboa, Siddhartha Jain, Or Sattath

Sybil resistance is a key requirement of decentralized consensus protocols. It is achieved by introducing a scarce resource (such as computational power, monetary stake, disk space, etc.), which prevents participants from costlessly creating multiple fake identities and hijacking the protocol. Quantum states are generically uncloneable, which suggests that they may serve naturally as an unconditionally scarce resource. In particular, uncloneability underlies quantum position-based cryptography, which is unachievable classically. We design a consensus protocol that combines classical hybrid consensus protocols with quantum position verification as the Sybil resistance mechanism, providing security in the standard model, and achieving improved energy efficiency compared to hybrid protocols based on Proof-of-Work. Our protocol inherits the benefits of other hybrid protocols, namely the faster confirmation times compared to pure Proof-of-Work protocols, and resilience against the compounding wealth issue that plagues protocols based on Proof-of-Stake Sybil resistance. We additionally propose a spam prevention mechanism for our protocol in the Random Oracle model.

C. David Wright

No abstract is available for this record.

Walter Hernandez Cruz, Peter Devine, Nikhil Vadgama, Paolo Tasca · 5 authors

We introduce DLT-Corpus, the largest domain-specific text collection for Distributed Ledger Technology (DLT) research to date: 2.98 billion tokens from 22.12 million documents spanning scientific literature (37,440 publications), United States Patent and Trademark Office (USPTO) patents (49,023 filings), and social media (22 million posts). Existing Natural Language Processing (NLP) resources for DLT focus narrowly on cryptocurrency price prediction and smart contracts, leaving domain-specific language underexplored despite the sector's ~$3 trillion market capitalization and rapid technological evolution. We demonstrate DLT-Corpus' utility by analyzing patterns of technology emergence and market-innovation correlations. Findings reveal that technologies first appear in our scientific literature subset before reaching patents and social media, following traditional technology transfer patterns. While social media sentiment remains overwhelmingly bullish even during crypto winters, scientific and patent activity grows less tied to short-term sentiment, tracking overall market expansion in a virtuous cycle in which research precedes and enables economic growth that, in turn, funds further innovation. We release the DLT-Corpus and companion artifacts: LedgerBERT (+23% over BERT-base on DLT-specific Named Entity Recognition (NER) task), a sentiment analysis dataset of 23,301 crypto news headlines and descriptions, tools, and code.

Stanislav S. AKULINKIN

The article examines digital payment tokens circulating in decentralized finance. The aim of the study is to de-velop a typology of digital payment tokens for their subsequent adaptation to the cross-border payment infra-structure as a specific payment token type that meets the necessary economic characteristics. The objectives of the study include an overview of the key innovations that led to the emergence and spread of decentralized finance, an analysis of the capabilities and advantages of smart contracts for creating digital tokens, a systema-tization of approaches to the regulatory framework for unsecured cryptocurrencies and stablecoins, and the selection of the optimal type of digital payment token for use in a cross-border payment infrastructure based on distributed ledger technology. The results of the study include a developed typology of digital payment tokens based on their suitability for use in a cross-border payment system. The study concludes that, in order to elimi-nate the fragmentation of national legislation that hinders the use of digital payment tokens in cross-border payment infrastructure, it is advisable for national regulators in countries participating in the unified cross-border payment space to focus their attention on the development and implementation of harmonized regula-tion of stablecoins.

Yongki Baek, Joohee Kim, Daeho Lee, Jungwoo Shin · 6 authors

Since 2021, interest in non-fungible tokens (NFTs) and associated trading volume have increased substantially, as celebrities increasingly adopted profile picture non-fungible tokens (PFP NFTs) for their social media profile images. In this study, the factors influencing consumer decisions on purchasing a PFP NFT were analyzed by Conjoint analysis. The characteristics of profile picture and NFT were researched through previous studies, and key attributes and levels that affect purchasing of a PFP NFT were set through market research. The results of the study showed that consumers made decisions based on the number of promoting celebrities as the most important attribute when they buy a PFP NFT, followed by number of community members, floor price, and commercial use of NFT intellectual property. This research has value in that it suggests a forward-looking perspective regarding development of the NFT market, which is in its early stages.

Yanna Jiang, Delong Li, Haiyu Deng, Baihe Ma · 7 authors

Agentic systems increasingly rely on reusable procedural capabilities, \textit{a.k.a., agentic skills}, to execute long-horizon workflows reliably. These capabilities are callable modules that package procedural knowledge with explicit applicability conditions, execution policies, termination criteria, and reusable interfaces. Unlike one-off plans or atomic tool calls, skills operate (and often do well) across tasks. This paper maps the skill layer across the full lifecycle (discovery, practice, distillation, storage, composition, evaluation, and update) and introduces two complementary taxonomies. The first is a system-level set of \textbf{seven design patterns} capturing how skills are packaged and executed in practice, from metadata-driven progressive disclosure and executable code skills to self-evolving libraries and marketplace distribution. The second is an orthogonal \textbf{representation $\times$ scope} taxonomy describing what skills \emph{are} (natural language, code, policy, hybrid) and what environments they operate over (web, OS, software engineering, robotics). We analyze the security and governance implications of skill-based agents, covering supply-chain risks, prompt injection via skill payloads, and trust-tiered execution, grounded by a case study of the ClawHavoc campaign in which nearly 1{,}200 malicious skills infiltrated a major agent marketplace, exfiltrating API keys, cryptocurrency wallets, and browser credentials at scale. We further survey deterministic evaluation approaches, anchored by recent benchmark evidence that curated skills can substantially improve agent success rates while self-generated skills may degrade them. We conclude with open challenges toward robust, verifiable, and certifiable skills for real-world autonomous agents.

Tayeb Kenaza, Islam Debicha, Youcef Fares, Mehdi Sehaki · 5 authors

Electronic Health Records (EHRs) store sensitive patient information, necessitating stringent access control and sharing mechanisms to uphold data security and comply with privacy regulations such as the General Data Protection Regulation (GDPR). In this paper, we propose a comprehensive architecture with a suite of efficient protocols that leverage the synergistic capabilities of the Blockchain and Interplanetary File System (IPFS) technologies to enable secure access control and sharing of EHRs. Our approach is based on a private blockchain, wherein smart contracts are deployed to enforce control exclusively by patients. By granting patients exclusive control over their EHRs, our solution ensures compliance with personal data protection laws and empowers individuals to manage their health information autonomously. Notably, our proposed architecture seamlessly integrates with existing health provider information systems, facilitating interoperability and effectively addressing security and data heterogeneity challenges. To demonstrate the effectiveness of our approach, we developed a prototype based on a private implementation of the Hyperledger platform, enabling the simulation of diverse scenarios involving access control and health data sharing among healthcare practitioners. Our experimental results demonstrate the scalability of our solution, thereby substantiating its efficacy and robustness in real-world healthcare settings.

Mansi Dilip Shriwastav, Madhavi Satish Avhankar

The increasing deployment of Agricultural Internet of Things (Ag-IoT) systems is transforming food production and enabling climate-resilient farming practices.However, the growing reliance on interconnected sensing, automation, and cloud platforms significantly expands the attack surface, exposing agricultural operations to cyber threats that can disrupt critical processes, compromise data integrity, and undermine food security.This paper explores adaptive cybersecurity mechanisms designed to enhance the resilience of Ag-IoT ecosystems operating under climate-induced environmental and network constraints.The proposed approach integrates context-aware access control, federated threat learning, zero-trust architectures, and distributed ledger technologies to secure dataflows, device interactions, and supply-chain processes.Experimental evaluations and simulated farm scenarios demonstrate improved attack detection, operational continuity, and system reliability during extreme weather events and adversarial conditions.The results suggest that adaptive cybersecurity strategies are essential for protecting next-generation digital agriculture and ensuring resilient, secure, and sustainable food systems in an era of accelerating climate variability.

Tabitha Chukwudi Aghaunor, Eferhire Valentine Ugbotu, Emeke Ugboh, Paul Avwerosuoghene Onoma · 9 authors

The proliferation of cloud infrastructures has intensified concerns regarding data security, integrity, identity and access management, and user privacy. Despite recent advances, existing solutions often lack comprehensive integration of privacy-preserving mechanisms, dynamic trust management, and cross-provider interoperability. This study proposes an AI-enabled, zero-trust, blockchain-fused identity management framework for secure, privacy-preserving multi-cloud environments. The framework integrates homomorphic encryption with differential privacy for aggregate-level protection and secure multi-party computation for collaborative data processing. The proposed system was validated in a simulated multi-cloud environment using CloudSim, Ethereum blockchain, and AWS EC2. Experimental results indicate homomorphic encryption latency of approximately 450ms per operation and statistically significant security improvements (t(128) = 12.47, p &lt; 0.001), privacy (t(95) = 8.93, p &lt; 0.001), and throughput (t(156) = 15.21, p &lt; 0.001). The framework achieved differential privacy with ε = 0.1 while retaining 99.2% data utility, and demonstrated a 34% improvement in processing speed over conventional differential privacy approaches. In addition, the implementation was observed to be 2.3× faster than BGV-based configurations, with 45% lower memory consumption than CKKS and a 67% reduction in ciphertext size relative to baseline implementations. From an operational perspective, the framework shows a 23% reduction in security management costs, a 31% improvement in resource utilization efficiency, and an 18% decrease in compliance audit expenses. The model further indicates a 27% reduction in total cost of ownership (TCO) compared with multi-vendor security solutions, a projected return on investment (ROI) within 14 months, and an 89% reduction in security incident response costs under the evaluated conditions.

Longbo Han, Xiaodong Li, Lin You, Gengran Hu · 8 authors

Vehicular ad-hoc networks (VANETs) require authentication mechanisms that simultaneously deliver privacy, accountability, and timely cross-domain synchronization. The existing schemes struggle to balance unlinkable anonymity with effective tracing. They are also vulnerable to future quantum adversaries and rely on slow and costly revocation workflows. We present ZebraCPA, a decentralized conditional privacy-preserving authentication (CPPA) framework that combines lattice-based traceable ring signatures (TRS) with zero-knowledge (ZK) proofs and a consortium blockchain. Our TRS design removes linkability tags and embeds a tracing trapdoor only recoverable by the authorized auditors. It naturally extends to threshold tracing for multi-auditor settings. To avoid the plain-text key escrow, ZebraCPA leverages the additively homomorphic property of the commitments to support the ciphertext-only key updates by the vehicles, preventing the catastrophic key leakage at authorities. A hierarchical blockchain layer provides fast, consistent synchronization of active-key status across regions. The experiments show 1.7×–7.0× speedups over state-of-the-art baselines in signing/verification while retaining an anonymity-set size of N=10. The network-level simulations further indicate that ZebraCPA reduces an average packet delay by 30.7% - 61.6% compared with the baselines under moderate traffic densities. Moreover, the security of ZebraCPA is validated through our informal analysis under the Dolev-Yao model. Overall, ZebraCPA achieves post-quantum security, strong anonymity with conditional traceability, and practical deployment efficiency for VANETs, outperforming the existing solutions in terms of both latency and robustness.

Anthony Uchenna Eneh, Love Allen Chijioke Ahakonye, Jae Min Lee, Dong-Seong Kim

Modern auto-insurance workflows require sharing heterogeneous digital evidence across multiple organizations. Yet, current cloud-based role-based access control mechanisms remain coarse-grained and poorly suited for expressing time, purpose, and case-specific constraints. This study presents ClaimGuard, which addresses these limitations by placing a blockchainbacked attribute-based access control gateway in front of existing evidence stores, enforcing fine-grained on-chain policies, and issuing short-lived capability tokens for authorized access. Implemented as a REST gateway with PureChain smart contracts, ClaimGuard is evaluated using realistic workloads involving up to 200 subjects and 1000 evidence resources. Experiments on a local Ethereum network shows sub$\sim 70 ~\text{ms}$tail latency, throughput exceeding$\sim 1000$requests/s, rapid policy updates, and zero false accepts, demonstrating the practicality of decentralized, auditable access control for privacy-preserving claims evidence sharing.

George Chidera Akor, Love Allen Chijioke Ahakonye, Jae Min Lee, Dong-Seong Kim

Zero-knowledge machine learning (ZKML) enables verifiable inference on private data, but deploying convolutional neural networks (CNNs) in production remains constrained by a multi-dimensional tradeoff between proof-generation latency, bandwidth consumption, and computational complexity. Existing ZKML frameworks and engineering blogs provide qualitative heuristics, yet practitioners lack systematic, layer-level measurements to guide architecture design under these constraints. This work presents the first systematic, layer-level characterization of CNN component costs in EZKL, a Halo2-based ZKML framework targeting EVM-compatible blockchains. We profile 8 feasible layer types (activations, pooling, normalization, and linear) across two EZKL precision settings (scale 7 and 10), measuring proof-generation time, proof size, circuit complexity, and peak memory in 26 experiments. We reveal critical infrastructure requirements by documenting 10 additional experiments that exceeded hardware limits (Conv2d operations, LayerNorm, and ReLU-based composite CNNs requiring$&gt;125\ \text{GB}$RAM). Contrary to conventional wisdom, we find that precision configuration has a negligible performance impact ($1.002 \times$ratio), and that system RAM, not GPU VRAM, is the primary bottleneck. We release an open-source profiling toolkit and a public dataset that enable practitioners to query expected costs for their architectures and constraints.