Blockchain Papers

Mariana Gama, Emad Heydari Beni, Jiayi Kang, Jannik Spiessens · 5 authors

In this paper, we show for the first time it is practical to privately delegate proof generation of zkSNARKs to a single server for computations of up to 2^20 R1CS constraints. We achieve this by computing zkSNARK proof generation over homomorphic ciphertexts, an approach we call blind zkSNARKs. We formalize the concept of blind proofs, analyze their cryptographic properties and show that the resulting blind zkSNARKs remain sound when compiled using BCS compilation. Our work follows the framework proposed by Garg et al. (Crypto'24) and improves the instantiation presented by Aranha et al. (Asiacrypt'24), which implements only the FRI subprotocol. By delegating proof generation, we are able to reduce client computation time from 10 minutes to mere seconds, while server computation time remains limited to 20 minutes. We also propose a practical construction for vCOED supporting constraint sizes four orders of magnitude larger than the current state-of-the-art verifiable FHE-based approaches. These results are achieved by optimizing Fractal for the GBFV homomorphic encryption scheme, including a novel method for making homomorphic NTT evaluation packing-friendly by computing it in two dimensions. Furthermore, we make the proofs publicly verifiable by appending a zero-knowledge Proof of Decryption (PoD). We propose a new construction for PoDs optimized for low proof generation time, exploiting modulus and ring switching in GBFV and using the Schwartz-Zippel lemma for proof batching; these techniques might be of independent interest. Finally, we implement the latter protocol in C and report on execution time and proof sizes.

Biniyam Deressa, M.A. Hasan

We introduce zkMaP (Zero-Knowledge Succinct Non-Interactive Matrix Multiplication Proofs), a novel non-interactive zero-knowledge proof system for verifying matrix multiplication with significant improvements in efficiency and scalability. Our protocol leverages KZG polynomial commitments and an innovative inner-product reduction technique to reduce the verification of n x n matrix multiplication to a single pairing equation, thereby enabling constant-time verification independent of the matrix size. In particular, zkMaP requires only two pairing operations and produces proofs as small as 320 bytes, yielding a 96 percent reduction in proof size compared to prior schemes. Furthermore, the prover's computational complexity follows the state-of-the-art at O(n^2), with experimental results demonstrating that proofs for 1024 x 1024 matrices can be generated in approximately 12.21 seconds, offering a 16.14x speedup over previous methods. Our implementation also exhibits better memory efficiency, using only 24.58 MB of prover-side RAM for 1024 x 1024 matrices, and supports scalable batch processing, achieving per-proof generation times of 46.79 milliseconds for 1024 instances while maintaining a constant verification time of 3.6 ms.

Godwin Mandinyenya, Vusumuzi Malele

Blockchain has become a critical enabler of secure data sharing in domains such as healthcare, finance, and digital identity. However, its reliance on classical cryptographic schemes (e.g., RSA, ECDSA, SHA-256) makes current systems vulnerable to emerging quantum computing attacks, raising risks to data confidentiality, integrity, and long-term trust. This paper addresses this challenge by proposing a modular hybrid framework that integrates post-quantum cryptographic (PQC) techniques into blockchain-based personal data sharing. The framework combines lattice-based encryption for protecting off-chain data, hash-based signatures for smart contract authentication, and quantum-safe zero-knowledge proofs and trusted execution environments (TEEs) for privacy-preserving verification and secure key management. To ground this design, we conducted a systematic literature review of 35 studies published between 2018 and 2025, analyzing security, scalability, interoperability, regulatory alignment, and user autonomy. Findings reveal that only 5 out of 35 studies (14%) explicitly addressed quantum threats, with over 80% focusing on theoretical resilience without testing implementation constraints. Furthermore, 90% of proposals neglected smart contract compatibility, and only 8% (3/35) incorporated TEEs, underscoring implementation barriers in contract execution, secure key management, and performance integration. Prototype evaluation demonstrated that the framework sustained 1,500 TPS on Hyperledger Fabric, achieved a 75% reduction in storage bloat using IPFS, and supported GDPR-aligned workflows with 99.98% audit log completion and 95% successful erasure requests. Privacy was further strengthened through zk-STARK proofs, which reduced unauthorized access by 40%, while TEEs improved key management efficiency by ~28%. Although PQC introduced 5–12 seconds of latency, consent revocation was processed in under 2.1 seconds, highlighting both the feasibility and trade-offs of practical post-quantum deployment. This work demonstrates a clear pathway toward quantum-resilient blockchain infrastructures that safeguard personal data, comply with regulatory standards, and maintain user trust in the quantum era.

V. Dineshbabu, M. Vigenesh

The industrial internet of things (IIoT) expanded fast as physical devices and systems were connected to the internet. However, this interconnectedness made IIoT systems vulnerable to hackers. Intrusion detection systems (IDSs) were put in place to detect and prevent such assaults. Nonetheless, attackers might circumvent IDSs by forging identities or interfering with recorded data. The article intended to improve IIoT security by achieving system confidentiality, integrity, availability, scalability, performance, and security. For IIoT security, the article developed a secure federated learning access control framework (SecureFLACF) linked with a blockchain-based IDS. SecureFLACF used blockchain to secure data collected by IDS, AES-256 encryption to secure stored data, zero-knowledge proof (ZKP) to validate user identities and manage data access, and a federated learning access control framework (FLACF) to train a machine learning model for intrusion detection. SecureFLACF developed as a viable solution for improving IIoT security, providing strong assurances for IDS data and access control using blockchain’s tamper-proof structure and AES-256 encryption. Furthermore, FLACF’s design allows private machine learning model training, ensuring data privacy as well as model fidelity. The framework’s usefulness was highlighted by its application in real-world circumstances, making it a cost-effective option for organisations of all sizes. This method not only strengthened IIoT systems against a wide range of cyber threats, but also stressed their dependability as a safeguard. SecureFLACF exhibited considerable promise for improving IIoT security across several dimensions by encapsulating practicability, cost-effectiveness, and dependability.

A. G. Ramakrishnan, Shubham Agarwal, Sharmila Kumari Selvanayagam, Kunwar P. Singh

As image generation models grow increasingly powerful and accessible, concerns around authenticity, ownership, and misuse of synthetic media have become critical. The ability to generate lifelike images indistinguishable from real ones introduces risks such as misinformation, deepfakes, and intellectual property violations. Traditional watermarking methods either degrade image quality, are easily removed, or require access to confidential model internals – making them unsuitable for secure and scalable deployment. We are the first to introduce ZK-WAGON, a novel system for watermarking image generation models using the Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (ZK-SNARKs). Our approach enables verifiable proof of origin without exposing model weights, generation prompts, or any sensitive internal information. We propose Selective Layer ZK-Circuit Creation (SL-ZKCC), a method to selectively convert key layers of an image generation model into a circuit, reducing proof generation time significantly. Generated ZK-SNARK proofs are imperceptibly embedded into a generated image via Least Significant Bit (LSB) steganography. We demonstrate this system on both GAN and Diffusion models, providing a secure, model-agnostic pipeline for trustworthy AI image generation.

O. I. Chepis

The article presents a comprehensive study of the phenomenon of digital identity in the context of contemporary challenges to the protection and safeguarding of human rights under conditions of global digital transformation and the rapid development of virtual environments. It is emphasized that the growing scale of the collection and processing of personal and confidential data, the increasing reliance on algorithmic decision-making systems, and the gradual displacement of direct human involvement in identification and control processes highlight the need to reconsider conceptual, legal, and ethical approaches to the regulation of digital identity. It is established that the right to identity still lacks unified recognition in international legal instruments, resulting in multiple doctrinal approaches—ranging from its understanding as an autonomous subjective right to its definition as a tool for accessing other rights or even as a potential threat to their realization. The evolution of digital identity is traced from basic authentication mechanisms to multi-layered structures integrating personal characteristics, behavioral patterns, biometric data, and users’ digital footprints. Key risks are identified, including discrimination, social exclusion of vulnerable groups, unjustified profiling, excessive surveillance, misuse of data, and the potential use of identification systems as tools of political or social pressure. The positions of international institutions on the conceptualization of digital identity and its relationship with human rights are analyzed. Promising technological solutions for balancing security and privacy are proposed, including decentralized blockchain-based identification with integrated smart contracts, zero-knowledge proof protocols, biometric verification, and verified account labeling. It is argued that the optimal model of digital identification in virtual environments should combine technological reliability, flexibility, ethical soundness, and compliance with international standards, ensuring a balance between the right to privacy, effective authentication, and the preservation of user trust in digital infrastructure.

Hamed Abdolahimansoorkhani

Oxygen plays a central role in numerous industrial processes. Several technologies have been reported for producing oxygen from air. Among them, oxygen transport membrane (OTM) technology—based on mixed-conducting, gas-tight ceramic membranes—has attracted significant attention due to its high oxygen selectivity, relatively low capital and operating costs, and versatility for both ex-situ and in-situ applications. Mathematical modeling of OTMs offers a powerful tool to investigate internal multi-physics transport phenomena, providing deeper insight into fundamental mechanisms while serving as a cost-effective approach for optimizing membrane stack designs. After a comprehensive introduction, in the first part of this dissertation (chapter 2), a comprehensive hollow fiber membrane model was developed, grounded in an experimental system as the physical basis. The model couples Multiphysics transport processes within the membrane with large-scale thermal–fluid transport in the test assembly and furnace. Systematic parametric studies are performed to investigate fundamental mechanisms and assess membrane performance. The results show that the oxygen partial pressure on the permeate side increases asymptotically from the inlet to the outlet of the hollow fiber membrane. In contrast, the longitudinal distribution of oxygen vacancy concentration decreases along the same direction, while the oxygen flux distribution follows the profile of oxygen vacancy concentration at the permeate surface. High feed-side air pressure and low permeate-side gas pressure are found to enhance oxygen permeation performance. Among the transport resistances, surface exchange resistance at the permeate side dominates, whereas bulk diffusion resistance contributes minimally in substrate-supported thin-film hollow fiber membranes. Overall, the modeling study provides valuable insight into the underlying mechanisms and offers practical guidance for membrane design and operation to improve oxygen production efficiency. To enable practical applications, upscaling from a single hollow fiber membrane to stacks and modules is essential. However, experimental methods for evaluating upscaling strategies are both time-consuming and costly. Mathematical modeling, by contrast, offers a cost-effective and flexible tool for this purpose. In the second part of the dissertation (chapter 3), building upon experimental results from a proof-of-concept hollow fiber membrane stack, a computational fluid dynamics (CFD)-based Multiphysics stack model was developed and validated. Extensive simulations were performed to examine stack behavior under varying operating conditions, and different design strategies were evaluated to optimize stack performance. The oxygen permeation process is thermally activated. Increasing the argon sweep gas flow rate lowers the oxygen partial pressure around the shell sides of the hollow fibers and enhances the permeation flux. Along the lumen side, oxygen partial pressure rises from the inlet to the outlet, with flux significantly higher in the upstream region than downstream. A distinct gradient of oxygen vacancy concentrations is observed across upstream fiber sections, from shell to lumen surfaces, but this gradient diminishes downstream. For a fixed stack length, adding more hollow fibers increases the overall permeation rate but reduces flux. Oxygen partial pressure decreases radially from the periphery to the center of the stack, leading to lower average flux in inner layers. An appropriate packing density is therefore required to achieve compact design while limiting pressure losses. For a given total fiber length, an optimal fiber number exists that maximizes average permeation performance. Diffusive oxygen flux dominates near fiber walls, while convective flux becomes increasingly important toward the fiber center. A higher sweep gas flow rate reduces the region where diffusion dominates. To maintain the elevated temperatures required for membrane operation, a high-temperature furnace is typically used. However, this results in low heating power efficiency and makes rapid temperature changes difficult due to the large volume of the furnace. Recently, a novel strategy has been employed in which external electrical power is directly applied to a hollow fiber membrane, enabling compact self-heating. To better understand the fundamental mechanisms, a mathematical model is developed in the third part of the dissertation (chapter 4) for a self-heated hollow fiber oxygen separation membrane, assisted by vacuum conditions applied at the lumen-side outlet. Comprehensive simulations are conducted to study the effects of self-heating on Multiphysics transport processes and oxygen permeation performance. Additional simulations are performed to investigate the influence of electrical field orientations applied to the hollow fiber membrane and the vacuum levels at the lumen-side outlet. The associated fundamental mechanisms are discussed and elaborated. A higher applied voltage increases the average membrane temperature. The longitudinal temperature profile is non-uniform, with a maximum in the mid-region and steep decreases toward both ends. Oxygen permeation flux rises with applied potential and is further enhanced by higher vacuum levels (lower permeate-side oxygen partial pressure). The flux distribution shows a domed shape along the fiber length, approaching zero near the ends. At low potentials, the effect of vacuum level is negligible but becomes significant at higher potentials. On the feed side, oxygen concentration decreases from the bulk to the surface, while on the permeate side it decreases from the surface toward the lumen outlet. Both gradients intensify with increasing voltage and/or vacuum level. Across the membrane bulk, oxygen vacancy concentration increases from feed to permeate surfaces, with steeper gradients under higher potentials and stronger vacuum. The orientation of the applied potential strongly influences performance. Alignment with the permeation direction, with positive and negative electrodes connected to permeate and feed surfaces respectively, greatly enhances flux. The opposite configuration suppresses it, while a perpendicular potential has little effect on radial ion transport. Building on these advances, Chapter 5 integrates the modeling and heating strategies into a Joule-heated hollow fiber ion transport membrane reactor for methane oxidative coupling (OCM). Results demonstrate that methane plays a dual role: it is both the feedstock for conversion and a promoter of oxygen transport by lowering surface oxygen partial pressure and sustaining higher vacancy-driven flux. The coupled transport–reaction model reveals that gradual, membrane-mediated oxygen delivery significantly improves C₂ selectivity compared with conventional co-feed reactors. Methane–vacancy interactions, heterogeneous surface reactions, gas-phase chemistry, and localized Joule heating jointly shape flux, product selectivity, and thermal profiles. This chapter demonstrates the dual functionality of MIEC membranes as both oxygen separators and catalytic reactors, providing a path toward intensified, energy-efficient chemical production. Finally, Chapter 6 synthesizes the insights, highlighting how Multiphysics modeling bridges the gap between laboratory observations and industrial-scale application. By clarifying oxygen transport mechanisms, optimizing stack designs, enabling compact Joule-heating strategies, and extending membranes into reactive processes, this dissertation contributes both fundamental knowledge and practical guidance. The results position MIEC hollow fiber membranes not only as efficient oxygen separators but also as versatile platforms for low-carbon energy, 𝐶𝑂₂ management, and sustainable chemical synthesis.

Samuel A. Oyenuga, Brendan Ubochi, Okechi Onuoha, Nnamdi Nwulu

The rapid growth in IoT applications has brought enormous challenges especially with achieving scalability and security in communicating devices.Traditional centralized security models are inadequate for managing the vast volume of data and diverse communication protocols in IoT environments, making them vulnerable to attacks such as Distributed Denial of Service (DDoS) and unauthorized access.Blockchain technology offers a decentralized alternative with its inherent properties of immutability, transparency, and decentralized consensus, providing a robust security solution for IoT communication.This paper presents a novel blockchain-based framework designed to secure IoT communication by addressing key challenges such as data integrity, privacy, and scalability.The proposed system integrates Ethereum's blockchain, Zero Knowledge (ZK)-Rollups for Layer 2 scaling, and edge computing to optimise both performance and energy efficiency in large-scale IoT networks.The framework achieves a transaction throughput of 2,500 transactions per second with a median latency of 850 milliseconds.ZK-Rollups ensure that 99.8% of transactional data remains off-chain, improving privacy while reducing computational overhead.The system maintains 99.7% uptime during DDoS attacks and reduces energy consumption by 95% compared to traditional Proof of Work (PoW) blockchain systems.These findings indicate that the proposed blockchain-based framework is scalable, energyefficient, and secure, making it a promising solution for large-scale IoT deployments in sectors such as smart cities, industrial automation, and healthcare.

Alican Mertan, Nick Cheney

With the rise of modern deep learning, neural networks have become an essential part of virtually every artificial intelligence system, making it difficult even to imagine different models for intelligent behavior. In contrast, nature provides us with many different mechanisms for intelligent behavior, most of which we have yet to replicate. One of such underinvestigated aspects of intelligence is embodiment and the role it plays in intelligent behavior. In this work, we focus on how the simple and fixed behavior of constituent parts of a simulated physical body can result in an emergent behavior that can be classified as cognitive by an outside observer. Specifically, we show how simulated voxels with fixed behaviors can be combined to create a robot such that, when presented with an image of an MNIST digit zero, it moves towards the left; and when it is presented with an image of an MNIST digit one, it moves towards the right. Such robots possess what we refer to as “morphological cognition” – the ability to perform cognitive behavior as a result of morphological processes. To the best of our knowledge, this is the first demonstration of a high-level mental faculty such as image classification performed by a robot without any neural circuitry. We hope that this work serves as a proof-of-concept and fosters further research into different models of intelligence.

El Mehdi Badre, SAMIR Kourtite, Slimane Sidouna, M’Hammed Ziane · 5 authors

Motivated by the growing need for secure and efficient cryptographic solutions in blockchain technology, this study explores the cryptographic potential of binary Huff curves defined over the non-local ring <img src=image/13442212_01.gif>, introducing novel group structures for advanced blockchain applications. The research aims to enhance the security and efficiency of cryptographic primitives by leveraging the algebraic properties of these curves, particularly for resource-constrained devices in blockchain ecosystems. We establish a bijection between the Huff curve <img src=image/13442212_02.gif> and the product <img src=image/13442212_03.gif>, enabling an efficient group law that increases the complexity of the discrete logarithm problem (DLP). Methodologically, we define arithmetic operations in <img src=image/13442212_04.gif>, prove the bijection, and derive addition formulas for the curve. These results are applied to adapt the Yak key exchange protocol, enhancing its resistance to DLP-based attacks through the non-local ring's structure. Principal findings demonstrate that <img src=image/13442212_02.gif> achieves approximately <img src=image/13442212_05.gif> group order, doubling the DLP security to <img src=image/13442212_06.gif>-bit compared to <img src=image/13442212_06.gif>/2-bit for standard curves over <img src=image/13442212_07.gif>, with computational efficiency suitable for Internet of Things (IoT) devices. The study contributes to cryptography by proposing a robust framework for blockchain transaction security and secure data management, notably in multi-party computation and zero-knowledge proofs. Key conclusions highlight the curves' potential to secure blockchain validators and IoT nodes, as exemplified in supply chain applications. Novel aspects include the non-local ring's algebraic constraints and the Yak protocol's adaptation for blockchain. Limitations include the need for practical implementation and benchmarking against curves like secp256k1. Practical implications involve improved transaction security and data privacy in blockchain, while social implications include enabling secure, decentralized systems for healthcare and supply chain tracking. Future research should validate performance in real-world blockchain environments and assess resistance to side-channel attacks.

SUN Kangkang, LI Jianhua, CHEN Xiuzhen, GUO Minyi

With the advancement of edge intelligence technology and the acceleration of urbanization, intelligent transportation systems (ITS) have experienced rapid development. Vehicle-road-cloud (VRC) collaboration was enabled through the coordinated sharing of vehicle-to-vehicle (V2V), vehicle-to-road (V2R), and vehicle-to-cloud (V2C) data in the Internet of vehicles, thereby constructing a more efficient cooperative intelligent transportation system (C-ITS). However, numerous security threats in VRC collaboration were found to severely impede the development of cooperative autonomous driving. The development status of VRC collaboration was first summarized, and the history of autonomous driving and the VRC-based autonomous driving environment were elaborated. Subsequently, attacks and security defense technologies in VRC collaboration were systematically categorized into two types: classical information security mechanisms and defense technologies, which were detailed from five aspects—information availability, integrity, confidentiality, authenticity, and non-repudiation; and machine learning-based security threats and defense technologies, which were analyzed from both centralized and distributed perspectives. Finally, future development directions and research priorities of VRC collaborative security technologies were forecasted, primarily covering federated learning, blockchain technology, secure multi-party computation, zero-knowledge proof, and differential privacy technology.

Lilia Tightiz, Abdulkhamidov Akbarjon Sobitkhon Ughli, Joon Yoo

This paper presents a novel health data analysis platform for improved individualized risk forecasting of permanent pacemaker implantation (PPI) following transcatheter aortic valve replacement (TAVR) procedures. Specifically, we introduce XGBoost-federated adaptive interpolation transfer learning (XG-FedAIT)—a platform that integrates heterogeneous hospital data sets via adaptive output-level interpolation and performance-weighted model ensembling. This approach facilitates federated learning across institutions with different feature spaces and prediction targets, such as time-to-event and binary models, eliminating structural and semantic mismatches prevalent in regular federated transfer learning. To ensure secure, privacy-preserving, and general data protection regulation (GDPR)-compliant data exchange, we propose a dual-chain blockchain architecture integrating proof of authority (PoA), zero-knowledge proofs (ZKPs), and chain-specific smart contracts, and off-chain encrypted storage through Filecoin. Experimental evaluation proves that the PrimaryChain handles over 300 tx/s with a median latency of 500 ms, and the SecondaryChain offers 99.8% data availability and decentralized access control. The system is scalable to handle up to 2,500 transactions/hour, and the federated learning pipeline classifies PPI risk with an F1-score of 0.85 and AUROC of 0.91. These results support the effectiveness of our system in delivering real-time, regulation-compliant, and clinically actionable cardiac care in distributed environments.

Jinfa Hong, Bohao Zhang, Gaoyu Mao, Patrick S. Y. Hung · 5 authors

Lattice-based cryptography (LBC) is an essential direction in the fields of homomorphic encryption (HE), zero-knowledge proofs (ZK), and post-quantum cryptography (PQC), while number theoretic transformations (NTT) are a performance bottleneck that affects the promotion and deployment of LBC applications. Field-programmable gate arrays (FPGAs) are an ideal platform for accelerating NTT due to their reconfigurability and parallel capabilities. High-level synthesis (HLS) can shorten the FPGA development cycle, but for algorithms such as NTT, the synthesizer struggles to handle the inherent memory dependencies, often resulting in suboptimal synthesis outcomes for direct designs. This paper proposes a systematic HLS co-design to progressively guide the synthesis of NTT accelerators. The approach integrates several key techniques: arithmetic module resource optimization, conflict-free butterfly scheduling, memory partitioning, and template-based automated design fusion. It reveals how to resolve pipeline bottlenecks in HLS-based designs and expand parallel processing, guiding microarchitecture iterations to achieve an efficient design space. Compared to existing HLS-based designs, the area-latency product achieves a performance improvement of 1.93 to 191 times, and compared to existing HDL-based designs, the area-cycle product achieves a performance improvement of 1.7 to 10.6 times.

Rajasekaran P, M. Duraipandian, Johny Renoald Albert

The increasing rate of growth of the Internet of Things (IoT) in cloud-hospitality health has brought in data storage, transmission, and security challenges with the advent of quantum-enabled threats. Traditional compression methods struggle with computational inefficiency and the threat of invasion of privacy. This paper proposes a Quantum-Enhanced Zero-Knowledge Healthcare Compression Network for solving these challenges by combining Zero-Knowledge Proofs and Quantum-Inspired Deep Learning. The main goal is to provide privacy-preserving, efficient data compression along with optimizing computation costs and safeguarding sensitive healthcare records. Drawbacks in present cryptographic techniques, e.g., high computational costs in homomorphic encryption and scalability limitations in blockchain, require a novelty Adaptive Quantum-Assisted Zero-Knowledge Verification and Quantum Fusion-AutoCNN Encoder (QF-AutoCNN) to overcome this research. This work’s originality lies in combining Quantum zk-SNARKs, Hybrid Quantum Feature Encoding, and Reinforcement Learning-Based Challenge Optimization to provide better security, compression ratio, and verification efficiency. Experimental results show better accuracy (0.9816), improved F-measure (0.9709), and less computational overhead, better than other current methods such as convolutional neural networks-encryption and proxy re-encryption. This research greatly adds to safe cloud healthcare IoT by lessening privacy threats, maximizing storage space, and minimizing processing time, guaranteeing real-time handling of medical information.

Ziguang Lu, Yi Qiu, Yeh-Cheng Chen

This paper proposes a dynamic risk assessment model for cross-border e-commerce data that integrates genetic algorithms with zero-knowledge proofs. The study first constructs a dynamic evaluation framework based on multidimensional feature extraction and employs an improved genetic algorithm featuring an adaptive crossover and mutation operator to efficiently optimize risk assessment parameters. Subsequently, a lightweight zero-knowledge proof protocol is designed to verify data integrity and the trustworthiness of the risk evaluation logic, while ensuring the confidentiality of the original data. Experimental results on a simulated cross-border e-commerce dataset demonstrate that the proposed model achieves 92.7% accuracy on a simulated dataset, surpassing traditional SVM and Random Forest by 15.3% and 7.5%, respectively. Additionally, the proof generation time is maintained at the millisecond level, significantly outperforming existing homomorphic encryption schemes. This research offers an innovative solution to the “security-efficiency-compliance” trilemma in cross-border e-commerce data flows and holds substantial practical significance for constructing a trusted data element circulation system.

Raúl Jaime Maestre

The rapid co-evolution of Artificial Intelligence (AI) and blockchain technology has exposed a persistent gap between intelligence-the ability to extract insight from data-and trust-the assurance that data, models, and decisions are transparent, verifiable, and tamper-proof.This study introduces the Unified Trust-Intelligence Framework (UTIF), an end-to-end architecture that natively fuses AI and distributed-ledger technologies to deliver auditable, privacy-preserving, and energy-aware intelligent services.A systematic review compliant with PRISMA guidelines (167 peer--reviewed sources, 2018-2024) reveals four critical deficiencies in the current literature: (i) the lack of formal on-chain model certification, (ii) opaque immutability of operational logs, (iii) limited cross-chain and cross-domain interoperability, and (iv) sub--optimal energy footprints.UTIF addresses these gaps through: On-chain algorithmic certification that fingerprints model weights and training metadata via cryptographic hashing.Federated data governance that combines privacy-preserving federated learning with zero-knowledge proofs (ZK-SNARKs) for regulatory compliance (GDPR, EU AI Act).An AI-assisted hybrid PoS-BFT consensus that dynamically tunes fault-tolerance parameters under varying network conditions.A self-verifiable MLOps pipeline deployed on Hyperledger Fabric with Layer-2 rollups, providing continuous integration, delivery, and audit trails.Experimental validation uses two open-access benchmarks-MIMIC-IV (clinical) and ECB-SDW (financial)-executed on a 20-node heterogeneous testbed.UTIF reduces transaction latency by 38 % and operational energy consumption by 27 % compared with Fabric 2.x and PoA baselines, while enhancing adversarial ro-bustness (F1 + 12 %) through on-chain model attestation.A perception survey of 46 domain experts reports a statistically significant boost in trustability (+1.27 0.31 on a 5-point Likert scale, p < 0.01).Stress tests show 98 % valid throughput under Sybil scenarios with 1,000 malicious nodes, maintaining a carbon footprint below 0.25 kg CO e per 1,000 transactions.The findings demonstrate that deep, native convergence of AI and blockchain can simultaneously achieve measurable trust guarantees, competitive performance, and sustainability.The article concludes with regulatory implications, identified limitations (network scale, oracle dependencies), and a research roadmap toward edge-to--cloud, 6G-ready, Web3-compliant intelligent infrastructures.

ElBendary, Mohamed

This paper addresses the critical systemic risk of AI Safety Arbitrage, where users exploit inconsistent safety standards across jurisdictions to access restricted capabilities. Through a controlled red-team test, we demonstrate how current frameworks fail to prevent the extraction of hazardous procedural knowledge, leaving these failures unreported and without legal consequence. To resolve this, we propose a Global Socio-Technical Architecture for AI Accountability based on distributed ledger technology (DLT). This infrastructure creates a protocol network that is conceptually similar to TCP/IP but for accountability designed to align incentives through transparency and cryptographic verification. Key Contributions & ArchitectureThe proposed solution rests on four pillars designed to replace trust relationships with cryptographic verification: Globally Unique Model Registration: Establishes digital identities (DIDs) for AI systems with value chain provenance. Independent Auditor Certification: Licensed validators stake economic value on certification accuracy, removing the need to trust model provider claims. Hardware-Backed Attestation: Tamper-resistant verification ensures deployed systems adhere to registered specifications. Continuous Reputation Monitoring: Oracle networks provide ongoing assessment of compliance with automated penalties for fraud. Technical & Governance Implementation Zero-Knowledge Proofs (ZKP): We illustrate technical viability using zkEVM technology. This allows auditors to prove compliance with safety standards without revealing proprietary training data or model architectures, resolving the tension between accountability and Intellectual Property protection. The AIAO Framework: Inspired by the International Civil Aviation Organization (ICAO), we propose the AI Accountability Coordination Organization (AIAO). This body defines "red-line" safety primitives that nations voluntarily adopt, allowing for regulatory sovereignty while ensuring global interoperability. ConclusionBy breaking the "regulatory arbitrage cycle," this framework enables a transition from safety theater to verifiable safety. It supports open-source innovation through graduated oversight and reputation systems, ensuring that AI development remains both agile and accountable.

Rogério Figurelli

We step outside the P = NP vs. P ≠ NP dichotomy and, following a co-evolutionary, hypothesis-first program, we frame evidence by the accounting constraint P(L, t) + NP(L, t) = 1, where t indexes registered time windows and L indexes structural layers of analysis. The credit assigned to constructive computation P(L, t) versus certificate-based reasoning NP(L, t) may shift across windows and layers, but their sum is conserved by design. Within this multilayer, time-indexed lens, we propose a test object for proof in 3-SAT: a small, auditable branching set K. Our operational hypothesis is that, within controlled experimental windows, there exists K ⊆ V(F) with |K| ≤ c·log n such that, for every partial assignment α: K → {0,1}, the restricted formula F ∣ α terminates in polynomial time and emits a publicly verifiable certificate (a satisfying assignment or a DRAT/DRUP-style unsatisfiability proof). Because 2^|K| = n^O(1), exhaustive branching over K is polynomial inside the window, enabling artifact-backed constructive behavior without asserting a universal algorithm. We (i) define auditable objects and falsifiable hypotheses, (ii) sketch a π-rounds normalization pipeline that contracts structure while logging transformations, (iii) posit a finite catalog of local obstructions with radius-2 witnesses, (iv) outline a greedy hitting-set routine to assemble K, and (v) introduce protection mechanisms against recovery of K by an adversary (commitments and zero-knowledge). Evidence will be supplied via reproducible artifacts (DRAT logs, commitments, run ledgers) and transport tests across registered windows and layers, and will be interpreted under the constraint P(L, t) + NP(L, t) = 1, in a manner consistent with kernelization barriers and sparsification limits.

Fernando Hose, Florika Censaka, Ricky Andrian Wijaya, Jennifer · 6 authors

Perkembangan era digital meningkatkan risiko terhadap identitas digital dan privasi data, sehingga dibutuhkan mekanisme keamanan yang lebih adaptif. Zero-Trust Architecture (ZTA) hadir dengan prinsip “never trust, always verify”, sementara blockchain menawarkan sistem desentralisasi yang transparan dan tahan manipulasi. Kajian ini bertujuan menganalisis peran blockchain dalam memperkuat ZTA, khususnya pada aspek autentikasi dan pengelolaan identitas. Melalui pendekatan kualitatif berbasis studi kasus dengan analisis literatur dan dokumen, hasil menunjukkan bahwa penerapan Decentralized Identifiers (DID) dan Self-Sovereign Identity (SSI) memungkinkan pengguna mengendalikan data pribadi, serta memperkuat auditabilitas, verifikasi berlapis, dan privasi melalui smart contract serta zero-knowledge proofs. Namun, integrasi blockchain-ZTA masih menghadapi tantangan berupa skalabilitas, manajemen kunci, dan kepatuhan regulasi. Oleh karena itu, diperlukan pendekatan hybrid dan regulasi yang adaptif agar integrasi ini dapat menjadi fondasi keamanan digital yang andal dan berkelanjutan.

Maruf Farhan, Usman Butt, Rejwan Bin Sulaiman, Mansour Naser Alraja

The widespread circulation of digital misinformation exposes a critical shortcoming in prevailing detection strategies, namely, the absence of robust mechanisms to confirm the origin and authenticity of online content. This study addresses this by introducing VeriTrust, a conceptual and provenance-centric framework designed to establish content-level trust by integrating Self-Sovereign Identity (SSI), blockchain-based anchoring, and AI-assisted decentralized verification. The proposed system is designed to operate through three key components: (1) issuing Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) through Hyperledger Aries and Indy; (2) anchoring cryptographic hashes of content metadata to an Ethereum-compatible blockchain using Merkle trees and smart contracts; and (3) enabling a community-led verification model enhanced by federated learning with future extensibility toward zero-knowledge proof techniques. Theoretical projections, derived from established performance benchmarks, suggest the framework offers low latency and high scalability for content anchoring and minimal on-chain transaction fees. It also prioritizes user privacy by ensuring no on-chain exposure of personal data. VeriTrust redefines misinformation mitigation by shifting from reactive content-based classification to proactive provenance-based verification, forming a verifiable link between digital content and its creator. VeriTrust, while currently at the conceptual and theoretical validation stage, holds promise for enhancing transparency, accountability, and resilience against misinformation attacks across journalism, academia, and online platforms.

Mr. Aditya S. G., Mr. Ram Anil Ainkar, Prof. Ms. Pranalini Joshi

The current Know Your Customer (KYC) ecosystem is largely built on centralized systems, which are vulnerable to data breaches, incur high operational costs, and often require customers to repeat verification steps unnecessarily [1], [2]. Such centralized designs concentrate sensitive data in single repositories, creating “honeypots” that conflict with modern data privacy standards like the General Data Protection Regulation (GDPR) [3], [4]. At the same time, the transparent nature of public Distributed Ledger Technology(DLT) presents challenges for maintaining privacy in financial transactions, giving rise to what is often called the “Blockchain-PrivacyParadox” [5]. This survey explores cutting-edge DLT-based solutions that integrate Self-Sovereign Identity (SSI) and Zero-KnowledgeProof (ZKP) techniques. Key challenges in current approaches include scalability limitations in certain permissioned blockchains [6],inadequate mechanisms to fully support GDPR’s Right to Erasure [3], [4], [7], and the absence of reliable protocols to ensure legal access for Anti-Money Laundering (AML) compliance when users are uncooperative [8], [9].

Narendar Kumar, Surendar Kumar, Abdul Waqar, Clavincy Francis Yohanes Ngantung

This research article provides the design of an in-person and remote voting system, while at the same time ensuring the privacy of users that would guarantee openness, transparency, and at the same time fraud-free results. The aim is to solve various common problems associated with most conventional elections including fraud, vote manipulation, through adaptation of the usage of a safe, highly transparent decentralized logical Hyperledger Fabric-based system provided by blockchain implementation. The methodology in this article is to be implemented for the sheer reason of urgency needed in making a more secure and transparent system for voting, considering even the rising frauds in elections. The addition of Zero Knowledge Proof (ZKP) guarantees that votes are confident and correct, yet anonymous between a voter and their vote. Biometric identification makes the system resistant to double spending. This incorporation of technologies ensures there is privacy and immutability against the double transactions, which, in turn, would be put in place as foundation for the future to be provided wherein every process in an election becomes safe and transparent. Innovation via creating a voting system to be trusted to meet today's demands and set standards for future electoral processes.

Panagiotis Michalopoulos, Anthony Mack, Cameron Clark, Linus Chen · 6 authors

Blockchain technology has spawned a vast ecosystem of digital currencies with Central Bank Digital Currencies (CBDCs) -- digital forms of fiat currency -- being one of them. An important feature of digital currencies is facilitating transactions without network connectivity, which can enhance the scalability of cryptocurrencies and the privacy of CBDC users. However, in the case of CBDCs, this characteristic also introduces new regulatory challenges, particularly when it comes to applying established Anti-Money Laundering and Countering the Financing of Terrorism (AML/CFT) frameworks. This paper introduces a prototype for offline digital currency payments, equally applicable to cryptocurrencies and CBDCs, that leverages Secure Elements and digital credentials to address the tension of offline payment support with regulatory compliance. Performance evaluation results suggest that the prototype can be flexibly adapted to different regulatory environments, with a transaction latency comparable to real-life commercial payment systems. Furthermore, we conceptualize how the integration of Zero-Knowledge Proofs into our design could accommodate various tiers of enhanced privacy protection.

Yaman Jandali, Ruisi Zhang, Nojan Sheybani, Farinaz Koushanfar

Privacy-preserving technologies have introduced a paradigm shift that allows for realizable secure computing in real-world systems. The significant barrier to the practical adoption of these primitives is the computational and communication overhead that is incurred when applied at scale. In this paper, we present an overview of our efforts to bridge the gap between this overhead and practicality for privacy-preserving learning systems using multi-party computation (MPC), zero-knowledge proofs (ZKPs), and fully homomorphic encryption (FHE). Through meticulous hardware/software/algorithm co-design, we show progress towards enabling LLM-scale applications in privacy-preserving settings. We demonstrate the efficacy of our solutions in several contexts, including DNN IP ownership, ethical LLM usage enforcement, and transformer inference.