Blockchain Papers

Ilyes Tarik Mazari, Yanis Mazari, Ilyan Mazari

This paper presents the completed Y.I.N. Mazari Architecture in its final 20-layer form, addressing two compounding failures in AI governance: the verification paradox where organizations cannot prove compliance without trusting their own infrastructure, and the platform determinism gap where AI inference produces different results across hardware architectures. The architecture integrates five physics foundation layers establishing energy-anchored provenance through Landauer limit accounting, domain routing, measurement precision, blockchain anchoring, and deterministic parity verification using Residue Number System arithmetic. Core governance layers enforce constitutional constraints through cryptographic authorization, differential privacy, and multi-party verification. Advanced layers provide zero-knowledge proofs, immutable audit trails, automated regulatory reporting, quantum resistance, and meta-governance oversight. Layer 0E, the Deterministic Parity Engine introduced in this final architecture, achieves bit-exact cross-platform computational reproducibility, enabling independent verification of AI operations by any party on any hardware. Combined with Layer 14, SENTINEL independent verification, the architecture produces governance evidence that no party can forge, no party can suppress, and any party can reproduce independently on arbitrary hardware. The complete 20-layer stack addresses GDPR Article 5, DORA Article 28, EU AI Act Article 50, HIPAA Security Rule, and provides 30-year quantum-resistant durability through NIST FIPS 203 post-quantum cryptography. Patent portfolio: 27 USPTO applications covering the architecture, priority November 23, 2025. The name Y.I.N. honors Yanis, Ilyan, and Neylia Mazari, representing the principle: Your Information Never leaves your control.

Tunahan TİMUÇİN, Serdar Biroğul

The blockchain technology has attracted more and more interest recently as a reliable and secure platform for a variety of applications. This study presents a comprehensive comparative analysis of monolithic and modular architectural patterns in smart contracts, which have become a revolutionary technology thanks to the integration of blockchain technology. A real-world vehicle purchase and sale system was used as a case study. Two contract structures were used that perform the same function: a modular architecture with five interacting contracts, and a monolithic architecture that combines all these functions in a single contract. An empirical analysis conducted for 100 vehicle sales revealed that while the modular architecture offers advantages such as independent upgradeability, testability, and maintainability, the monolithic approach outperforms it in many metrics, including a 36.7% reduction in transaction costs and a 75% faster deployment time. The findings provide evidence-based architectural guidance for blockchain and smart contract developers in selecting appropriate design patterns, particularly in real-world applications where gas costs are critical. Cite this article as: T. Timu.in and S. Biroğul, "A comparative analysis of monolithic and modular smart contract architectures: A case study of vehicle trading systems," Electrica, 2026, 26, 0333, doi:10.5152/electrica.2026.25333.

Adla Sanober, Shamama Anwar

ABSTRACT The digitization of healthcare insurance claims faces persistent challenges including data breaches, fraudulent submissions, and inefficiencies in verification and settlement. This paper presents a Zero‐Knowledge Succinct Non‐Interactive Argument of Knowledge (Zk‐SNARK) enabled blockchain framework deployed on the Polygon Proof of Stake (PoS) network for secure and privacy‐preserving health insurance processing. The proposed architecture integrates Attribute‐Based Encryption (ABE) for data confidentiality and the Elliptic Curve Digital Signature Algorithm (ECDSA) for authentication, ensuring end‐to‐end data integrity and access control. Experimental evaluation on the Polygon PoS testbed demonstrates a transaction cost of approximately $0.002, which is over 99% lower than Ethereum's 3–10 $ per transaction, while maintaining 100% resistance to data tampering, replay attacks, and transaction manipulation. Under the Polygon real network, the proposed framework supports a network‐level transaction capacity of up to 7000 transactions per second (TPS) under nominal operating conditions, with an approximately 9.3% reduction in effective capacity under stress scenarios, while maintaining 100% verification accuracy for all Zk‐SNARK proofs. The average on‐chain verification and settlement latency was measured at 4.7 s, confirming the system's suitability for real‐time healthcare claim settlement. These results validate that the proposed Zk‐SNARK enabled Polygon PoS framework offers a scalable, cost‐efficient, and cryptographically robust solution for healthcare insurance automation, outperforming existing blockchain implementations across security, efficiency, and economic performance metrics.

Iswarya Konasani

To prevent the reprocessing of the same trade message in different distributed financial infrastructures, electronic trading systems must have powerful duplicate trade detection protocols. Redundant messages are a result of network timeouts, TCP retransmission protocols, upstream retry queues, and manual resubmission workflows that are part of heterogeneous trading structures. Idempotency models define message uniqueness by using composite business keys, cryptographic fingerprints using the SHA-256 hashing functions, and deduplication logic on time windows that trades off between accuracy of detection and scalability of computation. Graphed graph frameworks are enhanced with blockchain and deliver distributed data models to specify intricate trade relations in the form of immutable ledger records, smart contract validation logic, and multi-channel designs, which assure information integrity across trading networks. Multi-channel correlation algorithms differentiate between actual trade amendments and replay events based on machine learning classification models and partial fill cases and cross-venue execution strategies. Strategies of implementation are used to optimize parameters of tolerance windows with the use of hierarchical composite key matching, progressive sampled indexing, and container-based pre-fetching strategies. Microsecond-latency duplicate-detection In-memory caching architectures in conjunction with Bloom filter probabilistic structures can achieve duplicate detection at millions of trade messages per day to protect downstream risk management and regulatory reporting systems against position inflation and compliance violations.

Prabhavathi Jayaraman, Radhakrishnan Delhibabu

Introduction In the contemporary digital health landscape, securing personal health data against unauthorized access while ensuring its verifiability is a paramount challenge. A critical conflict exists between the transparency required for data verification and the privacy mandated by global regulations such as HIPAA and GDPR. Existing Layer-1 blockchain solutions suffer from prohibitive gas costs and high latency, rendering them unsuitable for real-time monitoring of high-volume health data streams. Methods This paper proposes TeleZK-L2, a novel framework that synergizes distributed Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs) with Layer-2 scaling solutions. The architecture introduces a Distributed Prover Network (DPN) to parallelize heavy cryptographic computations and utilizes Optimistic Proof Aggregation to minimize on-chain data footprints. The verification logic is anchored on the Polygon zkEVM to ensure high throughput and low-cost settlement. Results Extensive simulations on a 16-node high-performance cluster demonstrate that TeleZK-L2 generates proofs at a rate 40% faster than the standard Groth16 baseline. Furthermore, the framework reduces on-chain verification costs by approximately 52%. The system maintains constant-time verification complexity regardless of batch size, achieving a peak throughput of 260 TPS. Discussion TeleZK-L2 provides the technical privacy guarantees necessary to support adherence to HIPAA and GDPR data minimization mandates while maintaining cryptographic soundness. By resolving the "Scalability-Privacy Trilemma," this framework demonstrates significant potential for large-scale deployment in national telehealth infrastructures and remote patient monitoring ecosystems.

Eduardo Sardenberg, Antonio José G. Busson, Daniel de Sousa Moraes, Julio Cesar Duarte · 5 authors

Smart contracts play a central role in blockchain systems by encoding financial and operational logic. Still, their susceptibility to subtle security flaws poses significant risks of financial loss and erosion of trust. LLMs create new opportunities for automating vulnerability detection, yet the effectiveness of different prompting strategies and model choices in real-world contexts remains uncertain. This paper evaluates state-of-the-art LLMs on Solidity smart contract analysis using a balanced dataset of 400 contracts under two tasks: (i) Error Detection, where the model performs binary classification to decide whether a contract is vulnerable, and (ii) Error Classification, where the model must assign the predicted issue to a specific vulnerability category. Models are evaluated using zero-shot prompting strategies, including zero-shot, zero-shot Chain-of-Thought (CoT), and zero-shot Tree-of-Thought (ToT). In the Error Detection task, CoT and ToT substantially increase recall (often approaching ~ 95--99%), but typically reduce precision, indicating a more sensitive decision regime with more false positives. In the Error Classification task, Claude 3 Opus attains the best Weighted F1-score (90.8) under the ToT prompt, followed closely by its CoT.

S. K. Abirami, G.R. Karpagam, M. Karthikeyan

Everything is becoming digital these days, health records are no exception. Digitization of healthcare sector would make the outcome improved such as enhanced patient care, but like every digitization, this has also introduced significant privacy and security concerns such as health data breaches. This paper proposes a “Health-ID Verification Framework” that combines blockchain, ECDSA and ZKPs to enable secure, GDPR-compliant health id verification. zk-ID leverages zk-SNARKS for privacy preservation, chameleon hashes are used for retroactive redaction that is in line with GDPR’s “right to be forgotten” clause. Sharded Blockchain networks achieve about 1,200 TPS. Evaluations show zk-ID reduces verification latency by 62% compared to Sovrin and lowers storage costs to 21.67/record via hybrid on/off-chain architectures. Interoperability is achieved through FHIR/HL7 APIs while eliminating fraud through multi-authority attribute-based encryption (MA-ABE).

Anderson Melo de Morais, Fernando Lins, Nelson Souto Rosa

The Internet of Things (IoT) drives technological advances across various sectors by enabling seamless communication among smart devices. However, significant challenges remain regarding the integrity and reliability of the data stored by these devices. Traditional blockchain solutions, such as those based on Proof of Work (PoW), are generally unsuitable for IoT applications due to their high computational resource demands. Although approaches combining multiple consensus algorithms have emerged as alternatives to optimise performance and security, determining the best combination for each scenario remains an open problem. This paper proposes a strategy based on Genetic Algorithms (GAs) to adaptively select and combine consensus algorithms, thus improving blockchain efficiency in IoT environments. The approach was evaluated on a test blockchain, OmniBlock, implemented using a Directed Acyclic Graph (DAG) and designed specifically for evaluation purposes in IoT applications. OmniBlock supports multiple consensus algorithms, including Proof of Authority (PoA), Proof of Stake (PoS), Proof of Work (PoW), Practical Byzantine Fault Tolerance (PBFT), Raft, and others. The consensus algorithm combination is chosen based on performance attributes. All combinations of consensus algorithms evaluated in this work were suggested by GAs; the practical feasibility of each is analyzed empirically. Experimental results indicate that the evolutionary optimization-based strategy performs better across most of the combinations suggested by the GAs.

Abhishek Srivastava

We present BAZINGA, a novel distributed system that achieves unification of artificial intelligence and blockchain through a new consensus mechanism called Proof-of-Boundary (PoB). Unlike traditional approaches that treat AI and blockchain as separate layers ("AI on blockchain"), BAZINGA demonstrates that AI and blockchain are Subject and Object of a single system, with consensus emerging from the boundary between them. The key discovery is that blockchain consensus can be achieved through understanding rather than computational work or financial stake. Nodes validate blocks by demonstrating comprehension via a mathematical boundary condition: the ratio of Physical to Geometric measures must equal φ⁴ ≈ 6.854 (where φ is the golden ratio). Key results: • 70 billion times more energy-efficient than Bitcoin • Sybil-resistant without financial stake • Unified with federated learning for distributed AI training • Validated through mathematical understanding rather than arbitrary computation The system includes four integration layers (Trust Oracle, Knowledge Ledger, Gradient Validator, Inference Market) that bind AI intelligence with blockchain validation. Fully implemented as open-source software (MIT License). Software: https://pypi.org/project/bazinga-indeed/ Source: https://github.com/0x-auth/bazinga-indeed Demo: https://huggingface.co/spaces/bitsabhi/bazinga

Abirami S.K, Karpagam G.R

Abstract Electronic health records (EHRs) capture the entire patient data history that enables early diagnosis, predictive analytics,improved co-ordination between health care providers,etc. However, centralized storage of EHR exposes the sensitive patient data to breaches while regulations such as GDPR Article 17 “right to be forgotten” and HIPAA 6-year retention limits sharing of data across different institutions. This systematic review consolidates 81 peer- reviewed studies (2015–2024) across different domains namely zero-knowledge proofs (ZKPs), blockchain consensus, trusted cloud execution, and regulatory compliance to propose a three-tier integrated framework that helps in regulatory compliant health care data sharing, yet preserving the privacy.The first tier is to generate ZKPs on the client side and redact personal information with chameleon hashes. The second tier anchors metadata hashes on PBFT blockchain with $f=\lfloor(n-1)/3\rfloor$ fault tolerance. The third tier stores data with an attribute-based encryption (ABE) off-chain on the cloud, integrated with FHIR/HL7 standards. It allows selective sharing, balances blockchain immutability with deletion via ZKP invalidation. The proposed architecture maps tools like zk-SNARKs/Circom for proofs, Hyperledger Fabric for blockchain, AWS KMS for keys. Challenges include lack of real-world testing, achieving proof generation latency (2-15 seconds), and scaling needs via Layer-2 hybrid consensus prototypes. \textbf{Keywords:} Zero-knowledge proofs, blockchain PBFT consensus, GDPR-HIPAA compliance, FHIR interoperability, attribute-based encryption.

Shymaa M. Arafat

Abstract When Nakamoto invented Bitcoin, the first generation of cryptocurrencies followed it in applying POW (Proof of Work) consensus mechanism; due to its excessive energy consumption and heavy carbon footprints, new innovations evolved like Proof of Space , POS (Proof of Stake) , and a lot more with many variants for each. Furthermore, the emergence of more blockchain applications and kinds beyond just cryptocurrencies needed more consensus mechanisms that is optimized to fit requirements of each application or blockchain kind; examples range from IoT (Internet of Things) blockchains for sustainability applications that often use variants of BFT (Byzantine Fault Tolerance) algorithm, and consensus needed to relay transactions and/or assets between different blockchains in interoperability solutions. Previous studies concentrated on surveying and/or proposing different blockchain consensus rules, on a specific consensus issue like attacks, randomization, or on deriving theoretical results. Starting from discussing most important theoretical results, this paper tries to gather and organize all significant existing material about consensus in the blockchain world explaining design challenges, tradeoffs and research areas. We realize that the topic could fit for a complete textbook, so we summarize the basic concepts and support with tables and appendices. Then we highlight some case examples from interoperability solutions to show how flexible and wide the design space is to fit both general and special purpose systems. The aim is to provide researchers with a comprehensive overview of the topic, along with the links to go deeper into every detail.

Cheng Junru, Li Na, Toksobaev Bulat T, Kambarova Zhumagul Ularbaevna · 5 authors

Despite regional integration frameworks like the Lisbon Recognition Convention, cross-border academic mobility in Central Asia remains constrained by fragmented credential verification systems. This inefficiency stems from the absence of interoperable infrastructure and asymmetric institutional capacity across national systems. This paper employs a comparative case study to analyze how Kazakhstan and Kyrgyzstan manage credential recognition. We identify that while Kazakhstan has centralized its digital governance, Kyrgyzstan operates under a bifurcated model where academic and scientific degrees are verified by separate bodies. To address this, we propose the Central Asian Blockchain Education Alliance (CABEA), a consortium blockchain framework acting as a "middleware" layer. This architecture allows for functional centralization without requiring administrative consolidation. Based on the technical specifications of Hyperledger Fabric, the proposed model has the potential to reduce cross-border verification time from weeks to near-instantaneous automated queries. We conclude that distributed ledger technology offers a scalable path for regional educational integration by bridging the gap between divergent state infrastructures.

Sharath Sathish

Overview Pramana introduces the first large language models fine-tuned on explicit Navya-Nyaya epistemological methodology—a 2,500-year-old Indian logical reasoning framework. This work bridges ancient epistemology with modern AI to address the fundamental epistemic gap in LLMs: the inability to ground claims in traceable evidence sources, distinguish valid knowledge from pattern-matching, and express appropriate epistemic humility. Core Innovation Unlike generic chain-of-thought prompting which relies on implicit reasoning patterns, Pramana enforces structured 6-phase methodology: Samshaya (Doubt Analysis): Classifies uncertainty into 5 taxonomic categories Pramana (Evidence Sources): Mandates explicit grounding in 4 valid knowledge sources (Pratyaksha/perception, Anumana/inference, Upamana/comparison, Shabda/testimony) Pancha Avayava (5-Member Syllogism): Constructs formal arguments with universal rules (Vyapti) grounded in concrete examples (Drishtanta) Tarka (Counterfactual Testing): Verifies conclusions via reductio ad absurdum Hetvabhasa (Fallacy Detection): Systematically checks 5 reasoning error types Nirnaya (Ascertainment): Distinguishes definitive knowledge from hypotheses requiring verification This integration of logic and epistemology provides cognitive scaffolding absent from standard reasoning approaches, preventing conflation of evidence types, forcing explicit universal rule statements, enabling systematic error detection, and maintaining epistemic humility. Architecture & Training Models Developed: Stage 0 (Proof-of-Concept): Llama-3.2-3B-Instruct fine-tuned on 20 examples Stage 1 (Minimum Viable Reasoner): DeepSeek-R1-Distill-Llama-8B fine-tuned on 55 examples Training Methodology: QLoRA (4-bit quantization) for efficient training LoRA rank 64, targeting all attention + FFN layers Supervised fine-tuning with structured Markdown format Training costs: <$1.00 per stage, <0.32 GPU-hours (A100 40GB) Datasets span constraint satisfaction, Boolean SAT, multi-step deduction, transitive reasoning, and set operations Prompt Engineering: Explicit format instructions with skeletal template injection System prompt establishing Nyaya reasoning engine role Critical constraint enforcement via generation parameters Key Results Stage 1 Performance: 100% semantic correctness (10/10 examples) with 95% CI [0.510, 1.0] 40% format adherence (4/10 examples) with 95% CI [0.168, 0.687] Zero structure abandonment: Models consistently attempt all 6 phases Training loss: 0.350 (Stage 1) vs 0.691 (Stage 0), indicating improved model fit Critical Finding: Dissociation between semantic correctness (100%) and format adherence (40%) reveals models internalize reasoning content even when strict schema compliance fails. This suggests Nyaya methodology teaches genuine reasoning, not just template-filling. Ablation Studies: Format prompting and temperature interact differently across stages Stage 0 optimal: format prompting + temp 0.0 (30% semantic rate) Stage 1 optimal: format prompting + temp 0.7 (30% semantic rate) Base models show 0% format adherence, confirming Nyaya structure is learned through fine-tuning Failure Mode Analysis: Missing Hetvabhasa section (2 cases): fallacy detection perceived as optional Invalid doubt types (2 cases): partial schema learning Zero structural errors: strong syntactic learning, semantic constraints need reinforcement Evaluation Framework Three-Tier Validation: Tier 1 (Structural): Automated format compliance checking (NyayaStructureValidator) Tier 2 (Content Quality): LLM-as-judge with explicit Nyaya rubric (planned for Stage 2) Tier 3 (Ground Truth): Semantic similarity via sentence-transformers embeddings Tier 4 (Formal Verification): Z3 SMT solver integration (infrastructure exists, not yet applied) Theoretical Contributions Bridging Ancient Epistemology with Modern AI: First demonstration that Navya-Nyaya structures can be learned by neural networks through fine-tuning Unlike Western formal logic (divorced from epistemology), Nyaya integrates logic with explicit knowledge sources Addresses "epistemic gap" in LLMs: inability to distinguish valid knowledge from probabilistic associations Interpretability Advantages: Every reasoning step traceable to evidence sources (Pramana) Universal rules (Vyapti) grounded in concrete examples (Drishtanta) Built-in self-verification (Tarka) and error detection (Hetvabhasa) Explicit epistemic status (Nirnaya): knowledge vs. hypothesis Computational Epistemology: Token budget: ~1,250 tokens per solution (3-6× CoT overhead, justified by interpretability) Phase dependencies: weak Pramana → invalid reasoning → wrong conclusions Quality thresholds: minimum 2 complete syllogisms with universal rules required Open Science Release All artifacts publicly available on Hugging Face: Models: qbz506/nyaya-llama-3b-stage0, qbz506/nyaya-deepseek-8b-stage1 Dataset: qbz506/pramana-nyaya-stage1 (55 Nyaya-structured logical problems) Demo: qbz506/pramana-nyaya-demo (interactive HuggingFace Space) Training infrastructure: Complete codebase with callbacks, validators, evaluators Limitations & Future Work Current Limitations: Format adherence (40%) below target (≥90%), requires constrained decoding or format-specific rewards Limited to formal logic problems, domain expansion needed Small evaluation sets (Stage 0: 2 examples, Stage 1: 10 examples) Max new tokens truncation (256) affects format parsing Planned Extensions (Stages 2-4): Stage 2: Synthetic scaling to 500 examples with LLM-as-judge quality control Stage 3: Group Relative Policy Optimization (GRPO) with composite rewards Stage 4: Production deployment with constrained decoding (GBNF), rejection sampling, Z3 verification Future: Benchmark on LogicBench, ProntoQA, RuleTaker; frontier model comparison (o1, Claude extended thinking) Impact & Vision This work demonstrates that systematic reasoning frameworks can be taught to LLMs through fine-tuning, not just prompt engineering. The long-term vision is developing interpretable, trustworthy AI reasoning systems where every conclusion comes with an auditable trail of justification. As AI systems deploy in high-stakes domains (medical diagnosis, legal reasoning, safety-critical systems), Nyaya-structured reasoning provides explicit phases that can be validated, debugged, and improved—capabilities essential for trustworthy AI. Invitation for Community Research: This foundation opens pathways for integrating other epistemological frameworks (Mimamsa, Buddhist logic, Western formal logic) into neural architectures, advancing toward AI systems that reason systematically and transparently. Technical Details Paper: 52 pages + appendices, comprehensive treatment of Navya-Nyaya computational formalization Related Work: Extensive review of computational Indian logic (Matilal 1985, Burton 2020, Ganeri 2001), LLM reasoning (Wei et al. 2022, Lightman et al. 2023, DeepSeek-AI 2025), hallucination mitigation Implementation: Python, Unsloth fine-tuning framework, vLLM deployment, Weights & Biases observability Evaluation: Manual + automated validation, semantic similarity metrics, comprehensive failure mode analysis Citation Sathish, S. (2026). Pramana: Fine-Tuning Large Language Models for Epistemic Reasoning through Navya-Nyaya. Preprint, University of York. Keywords: Navya-Nyaya, epistemology, LLM reasoning, interpretability, structured reasoning, Indian logic, hallucination mitigation, computational philosophy

Sougandhika Tera -

This paper addresses the technical and regulatory challenges of building secure data pipelines to support federated learning (FL), where models train collaboratively across multiple organizations without sharing raw data. The paper explores privacy-preserving data engineering techniques such as differential privacy, homomorphic encryption, and secure aggregation within ETL frameworks. It outlines an architecture for orchestrating decentralized dataflows that comply with GDPR, HIPAA, and other regulatory standards while enabling cross-institutional AI innovation. By integrating secure connectors, encrypted model updates, and audit logging, the proposed pipeline design ensures both data protection and analytic utility, providing a blueprint for responsible AI deployment in healthcare, finance, and government sectors.

Charles Ubagaram, Venkat Garikipati, Narsing Rao Dyavani, Bhagath Singh Jayaprakasam · 6 authors

Blockchain asset management employs distributed ledger technology and smart contracts to facilitate secure, transparent, and automated transfers of assets free of middlemen. Blockchain delivers instant, tamper-proof transfers as opposed to the current systems relying on central institutions that are plagued by high fees, delayed processing, and risk of fraudFractional ownership and enhanced accessibility are facilitated by its heightened security and efficiency in sectors such as real estate, financial asset management, and tokenization of assets.As per research, blockchain enhances data integrity from 40% to 99%, raises overall security from 50% to 98%, and reduces fraud by 95% compared to 30% in legacy systems. Although it has its benefits, the adoption of blockchain is hindered by interoperability, scalability, and regulatory uncertainty. To gain broader acceptance, regulators, institutions, and developers need to collaborate.

Yihao Ning

With the deepening application of big data technology across various fields, data faces increasingly severe threats of privacy leakage and security risks throughout its entire processing lifecycle. Traditional protection mechanisms, which focus on static data or isolated stages, struggle to address the systemic risks arising from the continuity, dynamism, and complexity of big data processes. This paper aims to systematically investigate the collaborative mechanisms for privacy protection and data security within the big data processing pipeline. First, it analyzes the inherent vulnerabilities at each stage of data processing, as well as the limitations faced by key technologies such as anonymization, differential privacy, and secure multi-party computation when integrated into practical workflows. Next, it explores the evolution of process-oriented encryption strategies, including attribute-based encryption supporting dynamic policies, homomorphic encryption optimized for practical use, and verifiable computation and zero-knowledge proofs that ensure computational integrity. Finally, the paper constructs a dynamic balancing model for privacy, security, and utility, and proposes forward-looking systematic collaborative mechanisms such as distributed auditing based on trust chains and adaptive response. These contributions provide theoretical reference and technical pathways for building next-generation inherently secure big data processing architectures.

Sang-Joon Lee, Qiuying Chen, Sang-Joon Lee

With the rapid expansion of the global cryptocurrency market since the 2018 Bitcoin investment boom, the number of cryptocurrency users has increased significantly. Despite the emergence of various cryptocurrency wallets, issues such as users' inability to properly manage their assets and the growing number of security incidents and crimes continue to undermine trust in digital asset storage. In this context, there is an urgent need for a system that can ensure secure asset protection and address users' anxiety regarding cryptocurrency management. However, the concept of cryptocurrency security remains undefined, relevant laws and regulations are not yet institutionalized, and academic research in this area is still limited. To address these challenges, this paper proposes an Ethereumbased cryptocurrency wallet system that not only enhances the functional and security aspects of existing wallets but also enables cryptocurrency delegation and ownership transfer. We designed and implemented a DApp that allows users to securely store, delegate, and transfer cryptocurrencies through an escrow account implemented via smart contracts on the Ethereum blockchain. By presenting a practical implementation of a transferable and delegatable wallet, this study contributes to improving user trust and asset safety, while laying the foundation for legal and technological innovation in the digital asset ecosystem.

Future Tech Wisdom Research Institute of Interstellar Age (FTWRIIA) - Shuiquan System

This document presents the Haiyue AI System as the irreplaceable core underlying technical cornerstone that empowers three pivotal global initiatives—Global Social Reform, Global Unified Governance Framework, and Earth Civilization’s Fair & Free System (where everyone can be president). Designed to address the technical bottlenecks of these reform agendas, the system integrates multi-agent collaboration, quantum-secure identity authentication, adaptive evolution, intelligent resource allocation, and blockchain traceability to deliver stable, efficient, and secure technical support, ensuring the feasibility, fairness, and scalability of the reform plans. The system’s core value in supporting the three initiatives is reflected in four critical dimensions aligned with their core goals: 1) Quantum-Secure Identity & Rights Protection: Built on W3C DID/SSI standards with Dilithium-5 signature and Kyber-1024 key encapsulation, it enables tamper-proof global identity verification and interoperability—laying the technical foundation for borderless mobility, inclusive participation, and anti-corruption supervision in global unified governance; 2) Intelligent & Fair Resource Allocation: Its three-layer AI engine (assurance-optimization-learning) guarantees 99.5% basic needs satisfaction and a Gini coefficient ≤0.2, directly supporting social reform’s objectives of labor rights protection, balanced cultural industry development, and inclusive finance; 3) Transparent Governance & Supervision: Leveraging blockchain traceability and zero-knowledge proof, it realizes real-time monitoring of policy execution, fund flows, and violation detection, empowering cross-border law enforcement, whistleblower protection, and algorithmic audit in global social reform; 4) Universal Participatory Democracy: Through multi-agent consensus algorithms and AI proxy voting (supporting special groups via brain-computer interfaces), it lowers participation thresholds to achieve 100% inclusive decision-making—fulfilling the "everyone can be president" vision of the fair & free system. Validated through rigorous reproducible experiments (successfully upgraded to L3, zero-fusion latency 76.81ms, agent success rate 97.6%), the system supports phased rollout of the three reform plans—from small-scale pilots to global deployment. As the technical backbone integrating efficiency, fairness, and security, it bridges abstract reform visions with practical implementation, turning goals of social equity, unified governance, and universal democracy into actionable reality.

Vidyotma Thakur, A. K. Verma, Surjit Singh

No abstract is available for this record.

Yoshinobu Shijo, Nanami Miyanishi, Shogo Ochiai, Eiichiro Hashiba · 7 authors

Personal Health Records (PHRs) enable personalized and continuous healthcare services, but contain highly sensitive information, requiring strong security and privacy safeguards. Self-sovereign architectures, where individuals retain full control over their data, represent a promising model for secure PHR sharing. In our prior work, we implemented a blockchain-based system using Non-Fungible Tokens (NFTs) to represent data ownership and usage rights. While NFTs provide tamper resistance, NFT-only access control is vulnerable to wallet compromise and requires explicit user consent, making it unsuitable for emergency access when patients are unconscious or otherwise unable to consent. To address these limitations, we newly propose a hybrid PHR-sharing framework combining NFTs with Attribute-Based Encryption (ABE). Our new approach enforces cryptographic access policies beyond NFT possession and enables emergency access to predefined medical information without explicit user consent. We analyze representative attack scenarios and show that the scheme provides secure access control and rights management. We implement a prototype and evaluate its performance. For 1 MB of data, used as a practical upper bound for text-based PHR records based on wearable-device measurements, retrieval takes approximately 1 second, while registration and access granting take approximately 12 and 6 seconds on the Base testnet, a high-speed Ethereum-compatible test network. These results demonstrate practical feasibility, with further optimization possible through faster blockchain networks or reduced blockchain transactions.

Divya Upadhyay, Puneet Garg, Nandini Babbar

Abstract Product life cycle management (PLM) in large supply chains still suffers from limited transparency, manual record-keeping, and weak traceability of provenance and expiry, which often results in counterfeit products, delayed recalls, and unsafe items reaching consumers. After the advancements in blockchain technologies, immutable, decentralised and auto-generated smart contracts provide a secure, safe, and organised solution for consent to the agreement between two or more parties and help digital assets and transactions to occur efficiently This work proposes a Blockchain–Internet of Things (B-IoT) based smart-contract framework that automates three key phases of the product life cycle: purchase order creation, invoice generation at delivery, and expiry-driven discard management. The proposed system will auto-trigger the smart contract through a program. It will generate the smart contract for a product when the purchase order is placed, generate an invoice, and handle the expired and discarded products. IoT devices will record important parameters such as product ID scanning, recording storage temperature, GPS trackers during transportation, etc. These parameters will help to auto-trigger the smart contract. To tune threshold parameters (e.g., temperature bounds, delay limits) and minimize cost–latency trade-offs, we build a lightweight regression model whose hyper-parameters are optimized using nature-inspired Mayfly (MFA) and Honey Badger (HBA) algorithms. The model predicts gas usage and latency per phase, achieving an RMSE of 0.15 and R 2 ≈ 0.9 on simulated transaction logs, while the final configuration yields an average execution efficiency of 92.3% and accuracy of 94.9% in correctly auto-triggering contract phases. The prototype is implemented using Ganache Truffle Suite, Remix, and MyEtherWallet, and evaluated in terms of gas consumption, functional correctness, and automation benefits over traditional manual contracts. Results demonstrate that the proposed B-IoT smart-contract framework provides transparent, tamper-resistant, and fine-grained product life management suitable for industrial deployments.

Zhijun Xie, Lijuan Zheng

INTRODUCTION: Each node keeps the identical block data in the decentralized, tamper-proof distributed ledger known as the blockchain. OBJECTIVES: A blockchain network's working time lengthens, the amount of data that nodes must preserve and synchronize increases noticeably. METHODS: This brings up significant storage performance difficulties. We have started a study from a blockchain data storage standpoint to address this storage performance issue. We propose a distributed hash table (DHT)-based blockchain data archiving approach by analyzing the redundancy state caused by every node in the current blockchain network containing the same data. The block data is introduced in three ways: archived data building, lookup, and interaction with the underlying chain. RESULTS: This is done to ensure that blockchain data is not lost and can be accessed. This reduces storage redundancy and satisfies the practical requirements of storage and access in the blockchain's initial application. CONCLUSION: Experiments on energy transaction data show that the technique suggested in this article has a considerably lower storage occupancy increase rate than fabric storage.

Samer Abaddi

This chapter will focus on the basic concepts of blockchain: the decentralized nature of the network, cryptographic security, and an immutable ledger. This section shall discuss, from the literature review, how blockchain can disruptively promote transparency, efficiency, and security in business processes. Particular attention will be given to main areas of blockchain applications: financial, supply chain, healthcare, and verification of digital identity. In finance, blockchain is pointed out as allowing secure, low-cost transactions and decentralized finance (DeFi) platforms, while in supply chain management, it increases traceability and accountability. Later, this chapter looks at combining blockchain and other new technologies, like the internet of things (IoT) and artificial intelligence (AI), to make business processes even better. It also closely examines problems in adopting blockchain, including unclear regulations, scalability issues, and high energy use in some blockchain networks. Ethical concerns with blockchain use – like its potential to allow anonymous and untraceable actions – are also closely reviewed.

D Chandrakantham, D Gayathri Devi

The rapid growth of Cardiovascular Disease (CVD) data from heterogeneous sources, including diagnostic imaging systems, electrocardiography devices, wearable sensors, and public research repositories, has created major challenges in ensuring data confidentiality, integrity, controlled access, and scalable management. Conventional centralized data storage architectures are prone to security breaches, and limited audit transparency. To address these limitations, this paper proposes a secure and scalable Token-Based PPos Heart chain (TPPoSHChain) framework for the management of multi-source CVD datasets by integrating blockchain technology, decentralized identity, authenticated encryption, and token-based access governance. The framework employs a hybrid on-chain/off-chain architecture, where the Algorand blockchain with a Pure Proof of Stake (PPoS) consensus mechanism provides immutable audit logging and access control enforcement, while encrypted datasets are stored off-chain in the InterPlanetary File System (IPFS) to enhance scalability. ChaCha20-Poly1305 authenticated encryption is used to protect datasets prior to storage and transmission, ensuring both confidentiality and integrity. Decentralized Identifiers (DIDs) establish a self-sovereign identity layer for data contributors, and actors, eliminating reliance on centralized identity providers. A blockchain-supported token-based access control mechanism enables fine-grained authorization, usage traceability, and secure cross-institutional data sharing. Experimental evaluation demonstrates that the proposed TPPoSHChain framework significantly improves transaction throughput, reduces latency, lowers storage overhead, and achieves efficient encryption performance than other existing models, making it well suited for secure and scalable CVD datasets.