In the digital economy era, the contradiction between data sharing and privacy protection is increasingly prominent. Traditional privacy protection technologies such as anonymization, differential privacy, and homomorphic encryption are difficult to meet the diverse privacy demands in multiple fields due to their flaws such as vulnerability to cracking, sacrificing data accuracy, and high computational complexity. Zero-knowledge proof (ZKP), with its core advantage of "data available but not visible", has become the key technical support to solve this contradiction. However, the current protocol types are complex and the demands in various fields vary significantly, leading to confusion in protocol selection and imbalance in resource allocation in practice. This paper systematically reviews the theoretical basis, technical system and mainstream protocol characteristics of zero-knowledge proof, integrates the advantages of demand-driven decision tree and protocol three-dimensional classification model, constructs a protocol selection framework of "demand-driven - feature matching - dynamic optimization", and clarifies the multi-stage decision-making process; Four typical fields, namely education, machine learning, finance, and healthcare, were selected to verify the effectiveness and universality of the framework, providing standardized tools for the large-scale application of zero-knowledge proof technology in various fields and direction guidance for the development of privacy protection technology in the post-quantum era.
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.
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).
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..
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.
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.
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.
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.
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.
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.
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.
Damilare E. Bakare, Adekemi Olawunmi Amoo, Mary T. Onifade
The health insurance sector has been facing many challenges recently, such as fraudulent activities in insurance claims, data breaches, and high transaction costs, particularly with existing systems built on the Ethereum network, which negatively affect its efficiency and effectiveness.These challenges undermine the trust and financials of insurance providers while compromising the privacy of the patient's health records.To address this issue, this study proposes a conceptual framework that uses zero-knowledge proof within the blockchain system and is deployed on the Polygon Network for its low transaction fees and higher throughput.The proposed model allows the verification of an insurance claim without revealing sensitive patient health records, ensuring privacy while preventing fraudulent activities.In this conceptual design, the hospital can issue verifiable proof of treatment, appointment, and bill that shows the validity of the insurance claim without revealing the underlying health record to the insurer.This study, therefore, contributes to supporting research in decentralized applications for healthcare insurance by presenting a conceptual model and comprehensively analyzing the feasibility, rather than a full-scale implementation.It also emphasizes the need to preserve privacy in sensitive domains and the potential benefits of blockchain and ZKP integration.In conclusion, the research's findings show that, in theory, integrating ZKP with blockchain technology can enhance healthcare insurance processes in terms of reliability, efficiency, privacy, and security.However, further research and practical development are required to realize and evaluate a fully operational system.
The article presents an empirical comparison of three contemporary Layer-2 scaling solutions for the Ethereum blockchain: Scroll, Linea, and Base, representing zk-rollup and optimistic rollup architectures. The study aims to evaluate the transaction processing speed and stability of selected Layer-2 networks using real-time data collected from blockchain explorers (Blockscout, Lineascan, Basescan). The dataset comprises 45,000 transactions processed in October 2025 and aggregated at one-second resolution (1 Hz). Statistical analyses include ANOVA, Kruskal–Wallis, Levene, and Brown–Forsythe tests, as well as ADF and KPSS stationarity diagnostics, used to assess diferences in throughput and operational stability across the examined networks. The results indicate that the Base network achieves the highest mean throughput (≈ 102 TPS) and the lowest temporal volatility, whereas Linea and Scroll exhibit non-stationary, highly variable transaction dynamics driven by periodic batching. The fndings confrm the persistence of the scalability trilemma—where improvements in performance may come at the cost of higher centralization and operational dependency. This research contributes to the quantitative assessment of rollup efciency and provides a reference point for further empirical studies on blockchain scalability.
Blockchain consensus mechanisms form the backbone of decentralized systems by ensuring agreement among distributed nodes without a central authority. At the core of these mechanisms lie number-theoretic foundations, including cryptographic primitives such as modular arithmetic, hash functions, elliptic curve cryptography, and zero-knowledge proofs. These mathematical constructs enable secure transaction validation, identity verification, and resistance against adversarial attacks. This paper presents a systematic review of number-theoretic foundations underpinning blockchain consensus mechanisms, focusing on methods, architectural implementations, and emerging research directions. The study analyses widely adopted consensus algorithms such as Proof of Work (PoW), Proof of Stake (PoS), and Byzantine Fault Tolerant (BFT) protocols, highlighting their dependence on number theory for ensuring security, randomness, and fairness. A comprehensive review of 30 studies published between 2018 and 2023 is conducted to examine advancements in cryptographic techniques such as verifiable random functions (VRFs), homomorphic encryption, and zero-knowledge proofs. These techniques play a crucial role in improving scalability, privacy, and efficiency of blockchain systems. The findings reveal that while number-theoretic approaches provide strong security guarantees, challenges such as computational overhead, scalability, and energy consumption persist. The paper concludes by identifying future research directions, including post-quantum cryptography, lightweight cryptographic protocols, and AI-assisted consensus optimization.
Mutiullah Shaikh, Uffe Kock Wiil, Ali Ebrahimi, Yumna Memon
Blockchain technology has revolutionized digital systems by ensuring trust, transparency, decentralization, and security. However, in the democratic nature of blockchain networks, there is a huge underlying dependency on consensus mechanisms, but the challenges associated with these, such as energy costs, network attacks, preservation of privacy, centralization, and limited scalability, hinder miners and stakeholders from adopting appropriate consensus mechanisms. In this paper, we present a conceptual literature overview of most consensus mechanisms by highlighting potential areas of exploration and considerations before adopting blockchain technology for various applications. This exploration turned our focus toward analyzing three prominent underlying aspects of consensus mechanisms, i.e. energy consumption, security, and decentralization. A simulation-based comparative analysis of five prominent blockchain consensus mechanisms, such as Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), Proof of Authority (PoA), and Proof of Capacity (PoC), is presented in various network load scenarios to further evaluate their performance metrics. The simulated metrics were cross-validated using empirical data from real blockchain networks (e.g., Ethereum, Bitcoin, VeChain, and Chia) collected between 2022 and 2025, ensuring alignment between theoretical performance models and observed on-chain behavior across diverse consensus mechanisms. Results overall indicate that PoW excels in decentralization and security while costing the highest energy, making it less scalable for high-throughput scenarios. PoS balances energy efficiency and moderate decentralization, while DPoS achieves scalability at the expense of decentralization. PoA and PoC are shown to be energy-efficient alternatives, but vary in their levels of centralization and security. Our findings constitute a comprehensive guide for researchers, miners, and practitioners aiming to optimize blockchain performance for diverse applications.
Raghavan Sheeja, Sherwin Richard R., Shreenidhi Kovai Sivabalan, Srinivas Madhavan
<p>The generational improvement has significantly converted several industries, and the area of intellectual property rights (IPR) isn’t any exception. IPRs, being as important as they are, need to be securely managed in some way. Blockchain, with its decentralized and immutable nature, gives a promising answer for enhancing the management of intellectual property (IP). This paper explores the strategic integration of blockchain generation for the control of IPR. The proposed system consists of a complete system, from registration and validation to predictive evaluation and royalty distribution, all facilitated through clever contracts. The use of zero-knowledge proofs guarantees the safety and confidentiality of sensitive information. The paper discusses the advantages and future implications of implementing this type of device.</p>
In the decentralized Internet environment, growing awareness of user data sovereignty has raised higher requirements for privacy protection in blockchain scenarios. To enhance the security and controllability of data authorization, this study develops a model integrating zero-knowledge proof (ZKP), field disclosure control, and multi-party joint verification. The ZKP ensures verifiable privacy, field disclosure control minimizes data exposure, and multi-party verification strengthens consistency and tamper resistance. Through this collaborative integration, the model forms a unified framework for secure and transparent data authorization. Experimental results on two blockchain datasets show that the model outperforms comparison approaches in authorization accuracy, field matching consistency, and verification efficiency, achieving a minimum verification loss of 0.248 and a true positive rate of 96.8%. Under simulation conditions, it maintains stable performance across different complexity levels, with authorization accuracy of 95.1% and field validation consistency of 96.5%. Compared with traditional single-mechanism methods, the model delivers comprehensive improvements in privacy strength, verification transparency, and collaborative trust, demonstrating strong potential for application in high-sensitivity blockchain privacy protection scenarios, particularly in privacy-critical domains such as healthcare record management, financial data exchange, and supply chain traceability.
With the increasing maturity of blockchain technology, its characteristics such as decentralization, data immutability, and consensus mechanisms can effectively address issues in supply chain finance, including high risk control costs, difficulties in credit endorsement for small and medium-sized enterprises, and cumbersome operational processes. By synthesizing research on the integration of blockchain technology into supply chain financial services and analyzing a case study of JD.com’s application of blockchain technology in supply chain finance ABS business, this paper proposes future development prospects for “blockchain technology + financial services”. The aim is to provide decision-making references for the modern financial services industry to expand operations, improve service performance, and reduce financial risks.
Despite the recognition of Blockchain Technology’s disruptive potential, there is ongoing debate about its ontological and axiomatic foundations. This study develops a theoretical framework to explain the underline structural principles of blockchain technology through the lens of Arthur’s theory of technology, and the framework is developed through adopting Narrative Literature Review. By integrating conceptual analysis with a structural examination of Ethereum, this study reveals that blockchain technology is not a single invention but a composite technological system developed through recursive interactions among sub-technologies. The proposed framework identifies three interrelated structural patterns—the Combinatorial Pattern of Components elucidating blockchain technology’s structural ontology, the Capturing Pattern of Algorithms revealing the operational source of its innovation, and the Recursive Pattern of Technologies characterizing its inner logical structure of components—that together explain blockchain technology’s generative and evolving nature. The study extends Arthur’s theory by clarifying the “technology within technology” dynamic that underlies blockchain technology innovation. The Ethereum case confirms the framework’s applicability and generalizability, showing that blockchain systems, despite their diversity, share a consistent structural logic. Beyond its theoretical contribution, the framework offers practical guidance for sustainable technological innovation. It provides analytical support for designing blockchain-based applications’ architectures that enhance transparency, efficiency, and adaptability, contributing to the sustainable evolution of digital technologies.
Deepika Dash, Bipin Raj C., B Jnyanadeep, Anala M R
The proliferation of decentralized finance (DeFi) has highlighted critical challenges in cross- chain oracle reliability and performance assessment. Traditional blockchain networks remain isolated from external data sources, creating the fundamental Oracle Problem that hinders institutional adoption of DeFi protocols. This paper presents DeFiLens, a comprehensive benchmarking framework that provides standard- ized performance metrics and real-time analytics across multiple blockchain ecosystems including Ethereum, Binance Smart Chain, Polygon, and Avalanche. Our framework addresses the gap between traditional finance’s seamless market data access and blockchain’s data isolation through systematic oracle assessment. DeFiLens implements a six-layer security scoring system encompassing cryptographic verification, attack detection, and network health monitoring. Through extensive evaluation of major oracle providers including Chainlink, Band Protocol, and Tellor, we demonstrate significant performance variations across chains, with response times ranging from 2.1 seconds to 8.7 seconds and reliability scores varying between 72% and 95%. Our statistical analysis reveals critical arbitrage opportunities with price discrepancies up to 2.3% across chains. The framework serves as a ‘‘Bloomberg Terminal’’ for oracle data, enabling financial institutions, DeFi protocols, and researchers to make data-driven decisions for oracle integration and risk management.
Smart contracts have been widely applied in various fields. Due to the immuta-bility of data on the blockchain, it is of great significance to conduct smart con-tract vulnerability detection before data is uploaded to the chain. To address the problems of low accuracy and single vulnerability type in traditional detection methods, a blockchain smart contract vulnerability detection method based on Graph Neural Network (GNN) is proposed. This method abstracts the functions and key code segments in smart contracts into nodes in a graph, and constructs edges by leveraging data and control dependencies during code execution, thereby accurately depicting the specific graph structures of reentrancy attacks and timestamp-dependent vulnerabilities. To further enhance the model’s sensi-tivity to key vulnerability patterns, the multi-head attention mechanism is in-novatively introduced, which can effectively screen out the nodes and edges that contribute the most to vulnerability detection, suppress irrelevant or noisy information, and significantly improve the accuracy and robustness of vulnera-bility detection. Experimental results show that the proposed method achieves an accuracy of 85.19% in reentrancy vulnerability detection and 82.37% in timestamp-dependent vulnerability detection, demonstrating excellent vulner-ability identification capability.
In the contemporary digital landscape, the demand for secure, private, and tamper-resistant communication has never been more critical. Conventional messaging platforms, which predominantly rely on centralized servers, are increasingly vulnerable to data breaches, unauthorized surveillance, and censorship. Even with the adoption of end-to-end encryption, these systems remain susceptible to single points of failure and metadata exposure, undermining user privacy and trust. Blockchain technology has emerged as a transformative solution to these challenges, offering a decentralized, immutable, and transparent infrastructure for secure data exchange. By leveraging distributed ledger technology, Blockchain-based messaging systems eliminate the need for trusted intermediaries, enhance resistance to censorship, and ensure data integrity through consensus-driven validation.