This paper proposes a universal post quantum privacy protection edge identity authentication framework to address the challenges faced by edge identity authentication in distributed cross domain networks, such as quantum attack threats, cross domain data privacy breaches, and difficulties in coordinating anonymity protection and compliance supervision. The framework adopts an optimized lattice based linkable ring signature protocol to meet the lightweight operation requirements of edge nodes and prevent the risk of leakage in identity data interaction; Design traceability constraints and controllable cross domain traceability mechanisms based on the linkability feature of signatures, balancing user privacy and regulatory requirements. Prove that the scheme possesses unforgeability, strong anonymity, and quantum resistance under the random oracle model. After optimizing the algorithm and interaction logic, the authentication efficiency is improved by 8% to 15% compared to similar solutions, and it is adapted to the low computing power and low latency characteristics of edge nodes. Combining zero knowledge proof to build a lightweight data collection mechanism and achieve privacy protection throughout the entire data process. This article uses the integrated aviation tourism system as a typical application case to verify that the proposed framework can be widely applied to various distributed cross domain networks and identity authentication systems.
Sasi Kala Rani K, Jeyasiba Ponmani Sami, R. Rajesh, Sridhar D · 5 authors
Abstract Sustainable development in the modern era depends on three major aspects: social, economic, and environmental sustainability. Evaluating the dimensions of environmental sustainability reveals that carbon emissions are a high-risk threat that significantly contributes to climate change and global warming. The dire need to curb the threat has led to the opening of many sustainable and mindful avenues, such as carbon credit trading. It is a major initiative to alleviate carbon emissions by the process of providing incentives. Traditional systems of carbon credit processing may lead to inefficiencies like lack of transparency and vulnerability. The drawbacks of traditional systems can be overcome by using Blockchain technology, which is decentralized and immutable in nature. The consensus algorithm Proof-of-Work (PoW) based blockchains consume high energy, which contradicts sustainability. To address the challenge, a hybrid mechanism of Proof-of-Stake (PoS) and Proof-of-Work (PoW) is proposed for carbon credit transfers. The hybrid mechanism is efficient for small to medium-scale applications. Hence, for large-scale applications, Osmosis, a decentralized finance (DeFi) platform built on the Cosmos blockchain, is explored. Experimental results show that the hybrid mechanism reduces energy consumption and carbon emissions by 47%, latency by 80% and increases throughput by 328%. Consequently, this performance enables the increase in transfer of carbon credits by 50%. In case of carbon credit trading of carbon credits, Osmosis exhibits greater energy efficiency, improving the throughput by 14–20 times and 12,500 times lower latency compared to the hybrid mechanism. Further Osmosis emits 200,000 times less CO₂ and transfers twice the number of carbon credits per hour compared to the hybrid mechanism.
The management and transfer of student archives in China constitute are mission-critical administrative processes governed by strict custodial regulations. However, the traditional paper-based "sealed-transfer" model is characterized by significant inefficiencies, risk of data loss, and limited mechanisms for verifying the data integrity during cross-institutional transitions. Although blockchain technology offers potential advantages in auditability and immutability, existing solutions often fail to balance privacy protection with high-performance requirements for large-scale archival data. This study proposes a decentralized, privacy-preserving framework that integrates the FISCO BCOS consortium blockchain, the InterPlanetary File System (IPFS), and Zero-Knowledge Proofs (ZKP). The system employs a multi-group architecture, leveraging IPFS for encrypted off-chain storage and zk-SNARKs generated via Circom to enable integrity verification without exposing sensitive data. Empirical evaluation was conducted using 30 archival samples ranging from 82 KB to 3.1 MB. Results indicate that the Paillier cryptosystem introduces significant performance bottleneck, with encryption latency exceeding one hour for files large than 2.3 MB. In contrast, a hybrid RSA+AES encryption scheme combined with ZKP archives stable, size-agnostic proof generation latency of approximately 850 ms and end-to-end transfer times under 3 seconds. These findings demonstrates that the proposed framework effectively replicates the traditional “sealed-transfer” mechanism through cryptographic means, providing a scalable and regulatory-compliant solution that aligns with the Archives Law of the People’s Republic of China and the Personal Information Protection Law (PIPL). This study provides a visible technical pathway for the digital transformation of national-level educational archive systems.
Blockchain technology has transformed digital transactions by providing decentralized, immutable, and transparent ledgers that eliminate the need for centralized intermediaries. However, the inherent transparency of blockchain networks often exposes sensitive transaction details, creating significant privacy concerns for users and organizations operating in sectors such as finance, healthcare, supply chain management, and digital identity management. Balancing transparency with confidentiality has therefore become a critical challenge in the evolution of blockchain systems. Zero-Knowledge Proofs (ZKPs) have emerged as a revolutionary cryptographic solution that enables one party to prove the validity of a statement without revealing the underlying confidential information. This paper proposes a comprehensive framework for integrating Zero-Knowledge Proof mechanisms into blockchain systems to enhance transaction privacy while preserving transparency, security, and verifiability. The framework incorporates advanced cryptographic protocols, including zk-SNARKs and zk-STARKs, together with decentralized consensus mechanisms to achieve secure and efficient verification of blockchain transactions. The proposed approach evaluates system performance in terms of privacy preservation, computational efficiency, scalability, verification accuracy, and transaction throughput. The findings indicate that Zero-Knowledge Proof-based blockchain architectures significantly improve user privacy, reduce information leakage, strengthen security against malicious attacks, and maintain the transparency and integrity required for decentralized trust. The proposed framework provides a scalable and secure foundation for next-generation blockchain applications requiring both confidentiality and public verifiability.
Hoda Naseri, Seyed Mohammad Mirhosseini, Ali A. Safaei
Abstract The rapid diffusion of artificial intelligence (AI) and advanced data analytics techniques across biomedical research, diagnostics and personalized medicine has established high-quality medical datasets as foundational resources. However, a significant impediment to progress is the reluctance of data owners to share these valuable resources because existing infrastructures provide no reliable guarantee of ownership or intellectual property preservation, in addition to the control over unauthorized usage. Consequently, vast quantities of recorded, high-potential data remain unused within private repositories. To overcome this barrier, a blockchain-based framework leveraging Non-Fungible Tokens (NFTs) is introduced in this paper to preserve ownership of medical datasets. The general workflow involves minting an NFT representing the dataset (stored encrypted off-chain), allowing users to request time-limited access via a smart contract. Access is granted using ephemeral decryption keys, enforcing fine-grained and revocable control. For forensic auditing, watermarking and perceptual hashing are integrated to enable the detection of post-access leakage without identifying the perpetrator directly. Technically, the framework integrates on-chain Merkle Tree-based integrity verification to ensure data fidelity upon retrieval. Practical applicability is demonstrated using a longitudinal multimodal neuroimaging dataset from OpenNeuro (DS007328). The proposed framework establishes a technically feasible and reproducible model for next-generation medical data governance, enabling verifiable ownership, controlled access, and reproducible auditing while keeping sensitive data off-chain.
Blockchain technology has evolved from its origins as the foundational ledger for cryptocurrencies to a disruptive paradigm for decentralized, transparent, and secure data management across numerous sectors. This review paper provides a systematic analysis of core blockchain architectures, consensus protocols, and smart contract functionalities that enable its diverse applications. We examine the transition from public, permissionless networks to private and consortium models tailored for enterprise needs. The paper surveys seminal and contemporary research across key domains including decentralized finance (DeFi), supply chain provenance, healthcare data exchange, electronic voting, and the Internet of Things (IoT). By synthesizing findings from foundational protocols to cutting-edge cross-chain solutions, we identify common technical motifs and domain-specific implementations. Furthermore, the review delineates persistent challenges such as scalability trilemmas, interoperability gaps, regulatory uncertainty, and significant energy consumption. This consolidated analysis aims to serve as a reference for researchers and practitioners, highlighting both the transformative potential and the critical limitations of blockchain techniques as a trustless infrastructure for the digital age.
Blockchain technology has evolved from a nascent peer-to-peer payment system into a paradigm-shifting digital trust infrastructure, fundamentally challenging conventional centralised models. However, a deep understanding of the fundamental technical aspects behind the popularity of crypto assets remains limited. This study aims to: (1) analyse the fundamental architecture of blockchain; (2) evaluate tokenisation mechanisms; and (3) conduct a comparative analysis of its characteristics against traditional database systems. The research employs a qualitative descriptive method utilising a Systematic Literature Review (SLR) approach to synthesise technical literature published between 2023 and 2025. The analysis focuses on consensus mechanisms, the architectural transition from monolithic to modular systems (Layer-2 scaling), and the measurement of decentralisation using the Nakamoto Coefficient. The results indicate that: (1) blockchain offers distinct advantages in data integrity (immutability) and censorship resistance through a distributed append-only ledger structure, standing in sharp contrast to the CRUD (Create, Read, Update, Delete) model of relational databases; and (2) recent innovations such as Zero-Knowledge Proofs and Optimistic Rollups serve as critical solutions to the "Blockchain Trilemma" (balancing scalability, security, and decentralization). This study concludes that blockchain is not an absolute replacement for conventional databases, but rather a specialised solution for ecosystems that require high transparency and "trustless" interactions without a central authority.
To map and synthesize original research on the Ethereum blockchain, emphasizing the dominant study themes, practical implications, recurring technical findings, and future research needs across security, performance, decentralized applications, markets, privacy, governance, and domain-specific implementations. The review uses 250 references and builds its evidence map from 488 original studies with 242345462 total participants/sample observations (topic-deduplicated ΣN). This review suggests that Ethereum is best understood not as a single application but as a programmable settlement layer whose real-world value is consistently conditioned by security, transaction-cost, and governance constraints rather than by immutability alone. The most recurrent result-level signal indicates that openness creates measurable adversarial surfaces, with blockchain extractable value reaching $540.54M over 32 months and censoring actors producing 46% of blocks while delaying affected transactions by an average of 85%. In parallel, machine learning and graph-based methods were repeatedly associated with strong fraud and phishing detection performance, with reported accuracies exceeding 96% in several settings. These patterns support a practical emphasis on gas-aware design, contract assurance, and continuous monitoring, often realized through hybrid on-chain/off-chain and permissioned architectures. Because most evidence comes from experimental and feasibility studies, future work should prioritize longitudinal, real-world deployment studies that measure security incidents, cost, and resilience beyond controlled testnets.
This paper underscores the vital role of blockchain technology in Industry 4.0, aiming to inspire researchers and industry professionals to recognize its transformative potential in creating decentralized, automated, and data-driven industrial settings. It explains core concepts, components, and varieties of blockchain systems, and assesses their uses across various sectors. The research delves into security and privacy issues, particularly relating to Ethereum platforms and smart contracts. It meticulously details common vulnerabilities of smart contracts and their implications for industrial systems. A critical comparison of existing vulnerability-detection techniques reveals current limitations. The paper also investigates the potential of artificial intelligence to enhance security analysis in blockchain contexts, systematically reviewing machine learning and deep learning strategies for identifying smart contract issues. A novel detection framework is introduced and tested against real-world datasets, showing improved accuracy and robustness compared to traditional methods. Ultimately, the paper aims to foster the development of secure and trustworthy blockchain infrastructure for applications in Industry 4.0.
As digital ecosystems continue to proliferate, the secure design, control, and management of data access have become increasingly critical. Since the generative artificial intelligence (GenAI) growth boom took shape in 2023, most of the organizations have used GenAI to manage a centralized control space to achieve global dominance in the future. The convergence of blockchain and GenAI represents a paradigm shift in decentralized computing and autonomous content generation. This chapter explores the foundational principles of both technologies and the synergistic potential they unlock when integrated. It will focus on their foundational principles and the role of consensus mechanisms in ensuring trust, transparency, and decentralization. We will analyze consensus mechanisms in the context of model training validation in decentralized AI systems, on-chain verification of AI-generated data integrity, and prevention of hallucinations and bias through distributed accountability. In the end, this chapter will identify key challenge areas like scalability and energy consumption and suggest approaches that use the strengths of both technologies to provide a comprehensive solution.
The rapidly expanding landscape of Web3 and the metaverse profoundly accentuates the escalating challenge of rigorously assessing and strategically selecting foundational Layer-1 digital blockchain platforms. Decision-makers frequently contend with the imperative of rational choice amidst a complex confluence of often conflicting technological attributes. This study directly addresses this critical exigency by utilizing robust benchmarking and validation for the comparative ranking of 10 prominent blockchain platforms. By applying a suite of five established multi-criteria decision-making (MCDM) methods, namely TOPSIS, ARAS, RAPS, RAMS, and RATMI, a comprehensive evaluation is undertaken, scrutinizing performance across three pivotal criteria categories: performance/scalability, security, and economic/activity. The weights for the entire criteria set were determined using the objective entropy method. Using the entropy approach to determine weights based on randomness, the criteria weights were determined as follows: Speed 12.9%, Market Cap 7.2%, Hash Rate 43.7%, Time to Finality 12.1%, Total Transactions 10.8%, and Number of Nodes 13.3%. The empirical analysis consistently identifies Bitcoin as the top-ranking platform, securing first position across all five MCDM methodologies. This finding validates its unparalleled robustness and security based on the defined criteria. Hyperliquid and Sui also emerged as exemplary performers, consistently exhibiting strong aggregate scores and securing second and third positions, respectively. Conversely, other blockchains, such as the BNB Chain and Tron, demonstrated significant ranking volatility across the different evaluation methods. This study provides a validated, data-driven benchmarking tool, offering stakeholders a transparent framework for strategic decision-making. This application contributes to the conceptual accuracy of evaluating sustainable digital infrastructure.
The paper presents the results of the authors' research for 14 months within a project funded by the Romanian Academy of Scientists. The design, implementation and testing of a decentralized web3 platform, based on Blockchain technologies – including smart contracts and Quantum, useful for education and diplomacy, is presented. The platform can also be used for knowledge management - for explicit knowledge flows. Our architecture allows quantum-enhanced authentication – offering an experimental, but future-oriented alternative to completely classical systems. It consists of a modular, layered structure, which includes the components: frontend, backend, quantum service integration, decentralized storage (IPFS) and blockchain registry. The platform is implemented and tested using several work scenarios. The paper demonstrates the efficacy of a decentralized academic infrastructure capable of harmonizing hybrid security paradigms with distributed storage technologies. Theoretically and technically, the major contribution of this work lies in the transition from a theoretical model to a fully operational system validated through an end-to-end workflow.
This work presents a comprehensive study of entropy-based metrics for evaluating blockchain systems, focusing on on-chain ledger immutability, off-chain data integrity, and computational dynamics within blockchain virtual machines (BVMs). We develop a unified framework that models blockchain states as probabilistic distributions, quantifying uncertainty through Shannon entropy and examining its evolution under varying adversarial fractions. Extensive simulations demonstrate that on-chain entropy exhibits near-exponential decay, reflecting the cumulative reinforcement of honest consensus, while off-chain entropy remains static, highlighting the limitations of conventional data storage. Furthermore, the BVM is analyzed in terms of computation entropy, establishing its Turing completeness and demonstrating that smart-contract state evolution mirrors the information dynamics of arbitrary Turing machines. Our results provide quantitative evidence that entropy serves as both a theoretical and operational measure of immutability, tamper evidence, and protocol resilience. The proposed entropy framework offers practical tools for monitoring ledger integrity, detecting tampering, and assessing computational complexity, bridging the gap between information-theoretic principles and distributed ledger applications. This study advances both the theoretical understanding and practical evaluation of blockchain security, providing a principled methodology for analyzing distributed systems under adversarial conditions.
Quantum computing poses a real, broad-based, but bounded and substantially mitigable threat to Bitcoin and Ethereum. We separate the two quantum algorithms that public discussion routinely conflates: Shor's algorithm breaks the elliptic-curve signatures (ECDSA over secp256k1, BLS over BLS12-381) that authorize spending, whereas Grover's algorithm does not meaningfully threaten proof-of-work mining, which is protected by a merely quadratic speedup, fault-tolerant per-operation costs, a square-root parallelization wall, and difficulty adjustment. Folding hardware scaling, the falling resource requirement, a fault-tolerance readiness lag, and expert surveys into a single Monte-Carlo forecast yields a wide, bimodal arrival distribution for a cryptographically relevant quantum computer: about a one-in-six chance by 2035, near 30% by 2040, and about 60% by 2050. Exposure is concentrated and mostly migratable: of Bitcoin's roughly six million quantum-exposed coins only about 2.3 million are irreducibly at risk, while 50 to 65% of Ether sits at key-revealed accounts that can adopt post-quantum signatures. A timely migration beats even an optimistic 2035 machine, so the binding constraint is governance, not technology. A survey of the top twenty cryptocurrencies finds none fully post-quantum. Reproducible models accompany every quantitative claim.
Despite the growing adoption of blockchains, their isolated architectures hinder seamless cross-chain communication, challenging applications that rely on integrated blockchain infrastructures, notably Blockchain-based Information Systems (BISs). Achieving interoperability while preserving privacy and regulatory compliance remains a core challenge, particularly when separate organizations operate different blockchain platforms and tokenized value must move across them without exposing transaction links that may reveal business relationships or payment behavior. Existing interoperability solutions often incur high computational overhead and rely on protocol-specific assumptions, limiting their applicability across heterogeneous blockchains. We introduce zkPACT, a privacy-preserving framework for compliant cross-chain token transfers across heterogeneous blockchains. Our framework combines Zero-Knowledge Proofs (ZKPs), oracle networks, and off-chain batching to support scalable transfers. It employs a coordinated oracle model in which validators process cross-chain burn events, while a rotating aggregator updates the shared off-chain Merkle tree after reaching consensus, enabling private and efficient token claims. To improve scalability and reduce gas costs, zkPACT batches claim requests off-chain and then submits a single succinct proof to the smart contract. To ensure validator accountability, the framework enforces an incentive mechanism and dynamic slashing. We also integrate a Know Your Customer (KYC) mechanism that enables users to demonstrate compliance without revealing sensitive data, preserving privacy and accountability in the event of abuse. We present a proof-of-concept implementation of zkPACT that achieves up to 95% lower gas costs and up to 94% lower off-chain memory usage than a non-batching approach, demonstrating its suitability for private, scalable cross-chain token transfers.
Jun 11, 2026·Universitatea Titu Maiorescu, the 19th international conference, education and creativity for a knowledge based society, informatics proceeding book
This paper presents a comparative study between two leading blockchain platforms—Hyperledger Fabric and Ethereum—with emphasis on their architectural design, performance characteristics, and security mechanisms in the context of enterprise applications. The study aims to identify key differences between permissioned and public blockchain models, focusing on scalability, consensus efficiency, and data confidentiality. A controlled experimental environment was developed using Docker-based deployments for both platforms, and performance was evaluated through Hyperledger Caliper using standardized workloads. Metrics such as transactions per second (TPS), latency, resource consumption, and failure rates were analyzed under varying network sizes. The results indicate that Hyperledger Fabric achieves significantly higher throughput (≈900 TPS) and lower latency (<200 ms) compared to Ethereum (≈25 TPS, ≈1 s latency), due to its deterministic Raft consensus and modular architecture. Ethereum, however, demonstrates superior decentralization and transparency suitable for public and decentralized applications. The findings highlight that both platforms are complementary: Hyperledger Fabric is optimized for controlled, high-performance enterprise use, while Ethereum excels in open, trustless environments.
Suresh Jaganathan, Venkatavara Prasad D, Aditya Krishna P, A Karthik
Health insurance claims processing and data storage pose challenges for security, efficiency, and transparency. Traditional distributed databases often rely on centralized management systems and enterprise-grade hardware, which can be costly and vulnerable. In contrast, Blockchain technology offers a decentralized approach to data management, ensuring transparency, security, and record immutability without requiring extensive hardware infrastructure. This paper examines the feasibility of leveraging blockchain, specifically the Internet Computer Blockchain (DFINITY), to automate health insurance claims processing and securely store insurance data. Additionally, a time-efficient algorithm is proposed to enhance querying and updating of insurance claims on the blockchain.
Data privacy concerns have become more critical than ever as machine learning and applied intelligence systems permeate sensitive industries such as healthcare, finance, national security, and personal services. This necessitates the development of privacy-preserving strategies for protecting private information while retaining the utility of intelligent models. This survey provides a comprehensive overview of privacy-preserving machine learning, with an emphasis on the cryptographic and statistical methods that are transforming how safe learning systems are built. The study starts by examining the most important components of the machine learning model and figuring out which of these may be protected to solve important privacy problems. The article then explores modern cryptographic techniques, including homomorphic encryption, zero-knowledge proofs, secure multiparty computations, and a statistical approach called differential privacy, that support contemporary privacy-preserving machine learning solutions. The study then explores how these strategies are applied independently and in hybrid systems to achieve accuracy, efficiency, and balance of privacy. This survey provides promising direction for protecting sensitive information during real-world model training and inference, offering insights into the design of trustworthy applied intelligence systems.
Blockchain oracles bridge on-chain smart contracts and off-chain data sources, but encrypted off-chain data still raises two practical challenges: how to verify retrieval integrity without exposing sensitive values, and how to keep verification information fresh when the off-chain data set changes. Existing oracle and outsourced-database retrieval mechanisms often rely on plaintext verification, heavy cryptographic proofs, or static authentication structures, which limits their applicability to latency-sensitive IoT and decentralized finance scenarios. To address these issues, this paper proposes a retrieval integrity verification mechanism based on CKKS approximate homomorphic encryption and an authenticated index named CKKS-Auth Tree. The proposed mechanism verifies encrypted query results through homomorphically aggregated metadata, while smart contracts record versioned verification commitments to detect stale or replayed results after updates. The scope of the mechanism is the integrity, completeness, privacy, and freshness of data after commitment and upload; verifying the physical authenticity of the original data source is outside the core threat model. Experimental results show that the proposed scheme reduces authentication and verification overhead compared with existing retrieval verification methods while supporting encrypted metadata updates and on-chain synchronization.
Archy Renaldy Pratama Nugraha, Yandra Arkeman, Irman Hermadi, Yani Nurhadryani
Digital identity verification in e-governance faces a trilemma between security, scalability, and regulatory compliance with Indonesia's Personal Data Protection Law (UU PDP). To resolve this, in this paper, we propose the ZMC-Framework, a blockchain-based hybrid architecture integrating Zero-Knowledge Proofs (ZKPs) for privacy-preserving verification and Merkle Trees for efficient, scalable data integrity on-chain. Its core innovation is a Legal Proof Protocol with 3+1 parameter augmentation, which cryptographically binds static identifiers to a user-controlled secret, ensuring compliance with UU PDP (data minimization) and UU ITE (authentication integrity) while aligning with key controls of the international ISO/IEC 27001:2022 standard. Evaluated on Polygon Mainnet, the framework demonstrates 29.9% lower operational costs for batch verifications and 50% better storage efficiency compared to pure ZKP systems. These results validate a practical solution to the verification trilemma, providing a secure, scalable, and legally sound foundation for public service identity management in Indonesia's digital governance ecosystem.
This chapter covers a couple of other technological developments worth mentioning: blockchain and non-fungible tokens (NFTs). This chapter serves a basic overview of what these technologies do and how they may, or may not, impact the music industry.