Blockchain Papers

Yibei Li, Jingyi Yang, Yiwei Lai, Mingzhe Liu

No abstract is available for this record.

J. Uma, V. Keerthika, Sravya Duvvuri, R. Dhaksana · 5 authors

No abstract is available for this record.

Qianqian Pan, Jun Wu

In the coming 6G era, Internet of Consumer Electronics (ICE) is promising in academia and daily life. To improve the intelligence and reliability of ICE devices with limited resources, a cloud-edge-end collaborative intelligent architecture is designed. However, the current collaborative intelligent ICE system faces multiple security threats, e.g., illegal access to the intelligent data/model resources and poisoning/backdoor attacks during collaborative intelligent model training. The security of the intelligent collaborative ICE is still an open issue. To solve this problem, we propose a zero-knowledge proof (ZKP)-driven zero trust method to protect the security of the intelligent collaborative ICE. First, a zero-trust intelligent collaborative ICE security framework is established for the confidentiality and availability protection of data/model resources. Second, we propose a zero-trust multi-factor access control mechanism for mutual authentication and trust evaluation-based resource access authorization. Third, the ZKP-driven collaborative intelligent ICE mechanism is designed. In this mechanism, the selective differential privacy-based data preservation scheme and the zero-knowledge collaborative intelligent model training scheme are devised. Finally, experimental results demonstrate the effectiveness and efficiency of the proposed secure intelligent collaborative ICE.

Saurabh Jain, Adarsh Kumar

No abstract is available for this record.

Muhammad Habib ur Rehman, Rajakumar Arul, Aisha Batool, Umar Hayat · 7 authors

No abstract is available for this record.

Komal Mishra

No abstract is available for this record.

Vedang Ratan Vatsa

This study analyzes 128,286 academic papers tagged as blockchain or cryptocurrency research by OpenAlex's machine-learning concept classifier, published between 2013 and mid-2026. A broader keyword search across paper abstracts identifies 1,938,409 publications that mention Web3-related terms. The analysis measures keyword frequency, temporal trajectories, growth rates, citation distributions, geographic concentration, institutional output, and open access rates. Key findings include 117x growth in annual blockchain publications between 2013 and 2025, the rise of zero-knowledge proofs as the fastest-growing cryptographic primitive (2.1x growth, 2025-2026 vs. 2022-2023), DeFi research experiencing a 74x increase from 2019 to 2025, NFT research peaking in 2023 before declining, China and India leading global output with 13.5% and 13.3% of all papers respectively, and 43.5% of all papers receiving zero citations.

Ali Sadhik Shaik

The contemporary digital information ecosystem is suffering from a structural market failure analogous to George Akerlof’s "Market for Lemons." In an era of Generative AI, the marginal cost of producing misinformation has approached zero, while the cost of verifying truth remains high. This asymmetry has created a "Trust Deficit" where high-quality information cannot be reliably distinguished from algorithmic noise. Current remediation strategies are bifurcated between two flawed extremes: Centralized Web2 Platforms (which prioritize scalability at the expense of transparency and are prone to censorship) and Decentralized Web3 Networks (which prioritize immutability but suffer from the "Garbage In, Garbage Out" paradox - permanently recording unverified data). The Trust-Scalability Trilemma: This research posits that decentralized reputation systems face a "Trust-Scalability Trilemma," historically unable to simultaneously achieve Veracity (Accuracy), Scalability (Throughput), and Decentralization (Censorship Resistance). Traditional solutions, such as Token Curated Registries (TCRs), have failed because they rely on synchronous, on-chain voting for every data point, resulting in prohibitive latency and gas costs. The Solution: This paper introduces The Klyrox Protocol, a decentralized middleware designed to resolve this trilemma by decoupling Content Execution from Content Verification. The protocol introduces a novel consensus mechanism, "Proof-of-Klyrox," which combines Optimistic Machine Learning (opML) with Game Theoretic Integrity Bonds. Proof-of-Klyrox is not a blockchain consensus mechanism. It is a layered fraud-detection and incentive framework anchored to existing consensus networks. Scope Note: Protocol V1 focuses exclusively on objective, verifiable claims (e.g., market data, timestamped events, quantifiable metrics). Subjective content quality assessment (e.g., editorial judgment, artistic merit) is explicitly out of scope and scheduled for research in future iterations. The system operates on an "Optimistic" presumption of validity: Optimistic Execution: Content is verified instantly via off-chain AI Oracles, reducing verification costs by an estimated 85-95% compared to traditional on-chain governance models. Cryptoeconomic Security: Users must stake financial collateral (Integrity Bonds) to publish. This creates a "Pay-to-Truth" incentive structure where the cost of generating misinformation strictly exceeds the potential profit. Sybil Resistance: The protocol implements a proprietary Time-Decayed Stake-Weighted (TDSW) algorithm. This scoring engine ensures that influence scales logarithmically with capital (preventing plutocratic capture) and decays exponentially over time (preventing the entrenchment of dormant actors). By financializing reputation into a portable, quantifiable asset class defined as "Epistemic Capital," The Klyrox Protocol offers a scalable blueprint for a self-regulating "Market for Truth." It transforms trust from a subjective social sentiment into an objective, verifiable economic product, providing the necessary infrastructure for the next generation of decentralized media, prediction markets, and AI safety layers. Author's Note: This whitepaper outlines the technical architecture and game-theoretic mechanisms underpinning the concept of "Epistemic Capital," as explored in The Algorithmic Monographs series by Ali Sadhik Shaik (The Algorithmic Invisible Hand, The Republic of Code, The Market for Truth, The Heavy Metal Intelligence and The Synthetic C-Cuite).

Christiane Isenberg

No abstract is available for this record.

A. Brahmareddy, Arun Kumar Arigela, Dr. Mercy Paul Selvan, T. S. Sreenivas · 6 authors

No abstract is available for this record.

Bushra Difalla Aljehani, Omar H. Alhazmi

No abstract is available for this record.

Tony Evans Adisurya

In recent years, the rapid growth of Decentralised Finance (DeFi) has revolutionised traditional financial services, with approximately $120 billion in total value locked (TVL) across various protocols. However, this expansion has been accompanied by significant security challenges, including major losses from DeFi hacks alone. The absence of centralised safety nets and the technical complexity of smart contract auditing have created substantial barriers to mainstream DeFi adoption, particularly among risk- averse users who lack the expertise to assess protocol vulnerabilities independently. This report presents the design and implementation of a decentralised insurance protocol that addresses these critical risk management challenges through an innovative tokenisation model. The proposed system introduces Insurance Tokens (ITs) and Principal Tokens (PTs) as the core mechanisms for providing insurance coverage against smart contract exploits and protocol failures in DeFi. ITs represent units of insurance coverage that can be freely traded on decentralised exchanges (DEXs) until expiration, enabling a liquid secondary market for DeFi insurance. PTs represent ownership stakes in the coverage fund, allowing underwriters to provide capital while maintaining the flexibility to exit positions through token sales or redemption at maturity. The protocol hopes to create a more accessible DeFi ecosystem by providing transparent, efficient and accessible insurance coverage, ultimately contributing to broader DeFi adoption and establishing a trustworthy framework for on-chain risk management.

Venkata Raghava Kurada, Pallav Kumar Baruah

No abstract is available for this record.

Umar Khalid

As the digital transformation accelerates, the security of cloud-based data storage and transmission has become a critical concern. Traditional cryptographic models often fail to ensure data integrity, privacy, and non-repudiation in distributed environments. This study proposes a blockchain-based security framework that integrates smart contracts, hash-based consensus, and distributed ledger technology (DLT) to enhance cloud data protection. The framework leverages immutable storage for audit trails, consensus validation for tamper detection, and homomorphic encryption for privacy-preserving computations. Experiments using Hyperledger Fabric and Ethereum private testbeds reveal that blockchain integration improves data integrity verification efficiency by 42% and reduces unauthorized modification risks by 35% compared to conventional systems. The findings underscore blockchain’s role as a cornerstone of trustworthy cloud architectures, ensuring both transparency and confidentiality in global digital ecosystems..

Ilyes Tarik MAZARI

This comprehensive technical survey presents integration architectures for the Y.I.N. (Your Information Never leaves your control) Nine Pillars framework across 200+ commercial platforms spanning artificial intelligence (100+ LLM providers including OpenAI, Anthropic, Google DeepMind, Meta, Microsoft, Mistral AI, Baidu, Alibaba, Tencent), healthcare (50+ providers including Epic Systems, Tempus, PathAI), finance (40+ institutions including JPMorgan Chase, Goldman Sachs, BlackRock), autonomous vehicles (20+ companies including Waymo, Tesla, Cruise), telecommunications (25+ carriers including AT&T, China Mobile, Deutsche Telekom), and energy (20+ companies including Siemens Energy, NextEra) across 25+ countries. The Y.I.N. Nine Pillars architecture provides end-to-end privacy protection through: (1) Data Privacy (Differential Privacy), (2) Computation Privacy (Homomorphic Encryption), (3) Storage Privacy (Encryption at Rest), (4) Transmission Privacy (TLS 1.3), (5) Access Control (Zero-Knowledge Proofs), (6) Audit Trail (Merkle Trees), (7) Deletion Rights (Cryptographic Erasure), (8) Quantum Resistance (Lattice-based Cryptography), and (9) Token Licensing (Cryptographic Payment Enforcement). The Ninth Pillar token licensing system, covered by U.S. Patent Application 63/949,361 (filed December 28, 2025), provides cryptographic enforcement of usage rights by integrating token-derived blinding factors into homomorphic encryption operations, making computational correctness mathematically dependent on valid authorization. The system achieves 99.37% accuracy with valid tokens versus 50.7% with invalid tokens (t=147.3, p<10^-50), with security proven under CDH hardness (2^128 operations) and Ring-LWE assumptions. Integration schematics are provided for regulatory compliance with HIPAA (healthcare), SOX/DORA (finance), GDPR/EU AI Act (European Union), CCPA (California), PIPL (China), ISO 27001, NERC CIP (energy), and 15+ other frameworks. Extension directions are documented for community research including TEE hybrid architectures, MPC integration, VDF token lifetimes, key-homomorphic PRFs, flexible validation policies, hardware attestation, ABE capabilities, off-chain settlement, and DID/VC integration. Organizations seeking to implement these integration patterns may obtain licenses for individual pillars, sector packages, or the complete Nine Pillars system from the patent holder. Patent Notice: The Y.I.N. Nine Pillars architecture and Ninth Pillar token licensing system are covered by U.S. Patent Applications 63/949,361 (Ninth Pillar, filed December 28, 2025), 63/923,348 (QFED-MAZARI Quantum Extensions), 19/399,646 (Core Y.I.N. Architecture), 19/403,244 (Hardware Implementation), and 19/417,196 (SQL Database Integration), comprising 430+ claims across 15 patent applications.

Mohamad Al-Zawahreh

Current commercial Large Language Model (LLM) architectures enforce a "server-side memory" paradigm, where user cognitive state is stored, managed, and monetized by the provider. This centralization creates two critical vulnerabilities: the economic inefficiency of "token inflation" (re-processing redundant context) and the epistemological risk of "rented cognition" (lack of user sovereignty over identity). This paper proposes a disruptive architectural shift: Remember Me AI, formally defined as the Client-Side Narrative Protocol (CSNP). By integrating Cross-Session Narrative Memory (CSNM) with a novel Semantic Compression Layer and Distributed Local Storage, we demonstrate a mechanism to reduce context token costs by approximately 40x while maintaining longitudinal coherence. We argue that this architecture commoditizes the inference layer, forcing a market transition from "Memory-as-a-Service" to "Compute-as-a-Commodity." This restores epistemological sovereignty to the user and neutralizes the lock-in mechanisms of hyperscale providers. The protocol includes Merkle-CRDT synchronization for multi-device consistency, Zero-Knowledge Safety Proofs for regulatory compliance, and a Polyglot Transpiler to ensure interoperability across proprietary model endpoints.

Aso Mohammad Darwesh, Atefeh Nekouie, Mohammad Hossein Moattar, Parisa Khoshvaght · 7 authors

Abstract Electronic Health Record (EHR) management is one of the challenging problems in digital healthcare and is related to several issues such as data security, privacy, scalability, interoperability, and ownership which are very crucial for reliable exchange of information. This review discusses the recent trend and technological solutions for the mentioned challenges. These solutions mainly focus on cloud-based infrastructures, attribute-based encryption (ABE), blockchain frameworks, and Non-Fungible Token (NFT)-based data ownership. This study highlights the strengths and limitations of each approach using comparative analysis and evaluations. Also, this review introduces a conceptual integration framework that combines graph neural networks (GNNs), multi-reference attribute-based encryption (MA-ABE), blockchain, and NFTs. The proposed model integrates predictive artificial intelligence, decentralized mechanism, immutable auditing, and verifiable ownership in a multi-layered architecture to address the issues and challenges of HER systems. Quantitative analysis of the reviewed literature reveals a clear upward trend in research activity, with more than 80 peer-reviewed studies published between 2017 and 2024, representing an approximate 250% growth in blockchain-, ABE-, and NFT-based EHR solutions. Among these, 41% focus on security and privacy, 27% on scalability, and 19% on interoperability, underscoring the field’s growing emphasis on decentralized and intelligent healthcare systems. This article not only contributes to a comprehensive review of the previous researches, but also provides a perspective on how the future of healthcare systems will be reshaped by intelligent and decentralized technologies.

Р.І. Мордвінов

The article systematizes modern methods of zero-knowledge proof (ZKP). Classification features are considered: protocol interactivity, algebraic or stochastic basis, need for trusted setup, type of zero-knowledge, and proof model. Classical schemes (Fiat–Shamir, Schnorr, Blum), modern zk-SNARK and zk-STARK, as well as novel approaches – PLONK, Halo 2, Bulletproofs, lattice-based ZKPs, and machine learning proofs are described. A comparative analysis is conducted according to efficiency, proof size, generation and verification complexity. It is shown that SNARKs provide compactness but require a trusted setup, while STARKs are transparent and post-quantum secure but large. Open problems are highlighted: recursive proofs, standardization, metadata protection, and applications in machine learning. It is concluded that further research in this field is aimed at creating scalable, secure, and quantum-resistant protocols for digital technologies.

Jihyok Choi, Kihwan Nam, In Hoh-Peter

Although Large Language Models (LLM) have shown impressive performance across various domains, there is a shortage of benchmarks for systematically evaluating their in-depth understanding of specialized fields such as blockchain. This study extends the Self-Instruct methodology to introduce BLADE (Blockchain Large Language model Assessment Dataset for Evaluation), a comprehensive benchmark dataset for assessing LLM comprehension in the blockchain domain. BLADE consists of a total of 1,382 questions organized according to a systematic classification of blockchain knowledge, featuring a detailed structure with 15 main categories and 5 sub-categories for each. The benchmark covers the entire spectrum of blockchain knowledge, from its fundamentals to consensus mechanisms, architecture, smart contracts, token economy, Decentralized Finance (DeFi), NFT(Non-Fungible Token)s and digital assets, security, governance, and real-world application cases. In this research, we present a benchmark generation methodology utilizing the domain knowledge of GPT-4.5, which allowed us to create high-quality evaluation items of varying difficulty and types from expert-verified seed questions. The evaluation results of various open-source LLMs, including Qwen, DeepSeek, and Kanana, on BLADE showed that current models exhibit significant differences in their understanding of blockchain, with Qwen2.5-7B-Instruct-1M achieving the highest performance. The BLADE benchmark provides a tool for precisely evaluating and improving the blockchain comprehension of LLMs, thereby promoting the effective fusion of AI and blockchain technology and contributing to the development of more reliable decentralized systems.

Li J

The in-depth application of blockchain technology in the financial sector has made smart contracts the core execution carrier for various decentralized financial businesses. Their security performance is directly related to the safety of financial assets and the stable development of the blockchain financial ecosystem. The immutability of smart contract code makes it difficult to fix vulnerabilities once they occur, which can easily lead to serious risks such as the theft of financial assets and transaction defaults. Moreover, the severity of different vulnerabilities varies significantly. Therefore, accurately defining the risk level of vulnerabilities and predicting the risk level in advance have become the core requirements for the security protection of blockchain applications in the financial field. This paper first explores the distribution patterns and correlation characteristics of the vulnerability features of smart contracts through correlation analysis and violin graph analysis. Then, multiple mainstream machine learning algorithms are introduced to conduct comparative experiments. The results show that the Transformer-LSTM-KELM algorithm proposed in this paper has the best comprehensive performance, with an accuracy rate of 71%. It is 5 percentage points higher than the suboptimal CatBoost and 25 percentage points higher than AdaBoost. With an precision rate of 77%, it is significantly better than all comparison algorithms. Its F1 value of 70% and recall rate of 71% are both at the leading level. This algorithm provides an efficient solution for the precise prevention and control of vulnerability risks in smart contracts in financial scenarios, and has significant practical value in ensuring the safe and compliant operation of blockchain financial business.

Bharath M. B, Ashwni S S, Mamatha M, Sowjanya S · 6 authors

With increasing dependence on AI for medical imaging diagnostics, privacy concerns and strict regulations continue to restrict data sharing across healthcare institutions. To address this, we propose a novel framework that enables cross-institutional collaboration without compromising sensitive patient information. Our system integrates federated learning with advanced privacy-preserving techniques, including homomorphic encryption, secure aggregation, differential privacy, and zero-knowledge proofs. Hospitals retain their data locally and contribute encrypted, noise-added model updates, ensuring that raw data never leaves the premises. Secure aggregation and encryption prevent any entity, including the central server, from accessing individual contributions. Differential privacy introduces mathematically bounded noise to mitigate risks from inversion and membership attacks. Meanwhile, zero-knowledge proofs allow clients to verify the legitimacy of their training process and updates without revealing internal computations or data. This layered privacy defense effectively counters gradient inversion, model poisoning, and membership inference attacks, all while maintaining strong diagnostic performance. Evaluated on real-world medical imaging datasets, our method balances accuracy with compliance to privacy laws like HIPAA and GDPR. The proposed architecture offers a scalable and trustworthy approach to enable AI-driven diagnostics across hospitals, ensuring patient confidentiality is never compromised.

Partha Chakraborty, Md Alamgir Miah, Md Abubokor Siam, Hasan Imam · 7 authors

The increasing amount of heterogeneous enterprise data has catalysed an expedient requirement of confiding, scalable, and privacy-preserving analytics structures. When data is distributed among various stakeholders, traditional data lake houses are prone to data integrity, provenance, and governance problems as well as secure model training. This paper seeks to overcome these difficulties by introducing a Trustworthy Data Lakehouse Architecture which combines Federated Learning (FL) with Blockchain-enabled governance to enhance safe, auditable and regulation compliant data analytics. The framework designed includes a built-in metadata layer, decentralized model-training pipeline, immutable ledger, based on blockchain and data provenance, and the privacy protection mechanisms of differential-privacy. Multi-organization collaboration without raw data exchange is possible thanks to Federated Learning, and end-to-end trust is ensured by blockchain which supports consensus-based validation, lineage tracking based on tamper-proof, and access control with smart-contracts. Experimental analysis is used to show that there are data reliability, model accuracy, latency, and confidentiality improvements over traditional centralized lake houses. The presented solution opens up a strong base of constructing transparent, secure and scaled out data ecosystems applicable to finance, healthcare, supply chain among other sensitive areas.

Xingyu Feng

Blockchain technology, lauded for its transparent and immutable nature, introduces a novel trust model. However, its decentralized structure raises concerns about potential inclusion of malicious or illegal content. This study focuses on Ethereum, presenting a data identification and restoration algorithm. Successfully recovering 175 common files, 296 images, and 91,206 texts, we employed the FastText algorithm for sentiment analysis, achieving a 0.9 accuracy after parameter tuning. Classification revealed 70,189 neutral, 5,208 positive, and 15,810 negative texts, aiding in identifying sensitive or illicit information. Leveraging the NSFWJS library, we detected seven indecent images with 100% accuracy. Our findings expose the coexistence of benign and harmful content on the Ethereum blockchain, including personal data, explicit images, divisive language, and racial discrimination. Notably, sensitive information targeted Chinese government officials. Proposing preventative measures, our study offers valuable insights for public comprehension of blockchain technology and regulatory agency guidance. The algorithms employed present innovative solutions to address blockchain data privacy and security concerns.

Janaka Ishan Senarathna

Blockchain technology has emerged as one of the most transformative innovations of the 21st century, fundamentally reshaping how digital transactions are recorded, verified, and secured across distributed networks without centralized intermediaries. Originally conceived by Satoshi Nakamoto in 2008 as the underlying architecture for Bitcoin, blockchain has evolved far beyond cryptocurrency applications to encompass smart contracts, decentralized finance, supply chain management, healthcare systems, and enterprise solutions. This comprehensive review provides an accessible yet thorough examination of blockchain technology, targeting readers from beginner to intermediate levels seeking to understand both theoretical foundations and practical implementations. We systematically explore the foundational principles of blockchain architecture, including distributed ledger technology, block structure and chain formation, Merkle tree organization, and peer-to-peer network topologies. The paper provides in-depth analysis of cryptographic primitives including hash functions, public-key cryptography, elliptic curve digital signatures, and emerging quantum-resistant approaches. We examine diverse consensus mechanisms ranging from proof-of-work to proof-of-stake variants, Byzantine fault tolerance protocols, and hybrid approaches, analyzing their trade-offs in security, decentralization, and performance. The review extensively covers smart contract platforms with emphasis on Ethereum's architecture, vulnerability patterns, and security best practices. Critical scalability challenges are addressed through examination of layer-two solutions including Lightning Network, state channels, rollups, and sharding protocols. We analyze security threats across network, consensus, and application layers, alongside privacy-enhancing technologies such as zeroknowledge proofs and confidential transactions. Real-world applications are explored across financial services, supply chain management, healthcare, Internet of Things, and digital identity systems. The paper examines enterprise blockchain frameworks, particularly Hyperledger Fabric's permissioned architecture, comparing public and private blockchain tradeoffs. Finally, we discuss current challenges including energy consumption, regulatory uncertainty, and interoperability limitations, while exploring future research directions in quantum resistance and cross-chain protocols. By synthesizing insights from 75 peer-reviewed sources spanning foundational research, recent advances, and practical implementations, this review serves as a comprehensive resource for researchers, practitioners, and students seeking to understand blockchain technology's current state and transformative potential.