In this modern world, every field has transitioned from traditional written to digital records. Fields such as healthcare involve sensitive digital records; ensuring secure access to them while protecting patients’ privacy is crucial. This paper provides a novel approach for a secure access control system that employs blockchain with zero-knowledge proofs (ZKP) and facial recognition to enhance the security of sensitive records. Face verification is used as biometric-based identity verification, and hence, captured images need to be processed securely. By encrypting such data on the client side and storing user biometric data, the system’s design prioritizes user privacy. It ensures that biometric data is protected while maintaining standard access control features. The system utilizes ZKP to authenticate users without revealing their private keys, ensuring robust authentication. Incorporating blockchain technology decentralizes the proof verification process and acts as a potential way to achieve tamper-proof storage, thus reducing the possibility of unauthorized access. Experimental results show that the proposed system achieves face verification and secure access validation 2x and 1.5x faster than the previous approaches. By undergoing a detailed design, extensive evaluation, and validation of the proposed system, this paper demonstrates the system’s security and provides seamless access to authorized healthcare professionals.
Blockchain technology, renowned for its decentralized and secure nature, has gained substantial attention. Central to its functionality are consensus mechanisms, which are essential for validating transactions and upholding the integrity of the distributed ledger. However, the efficiency and scalability of blockchain are currently impeded by the resource limitations and excessive communication demands of existing consensus mechanisms. To address these challenges, we propose GT-BFT, a streamlined and lightweight blockchain consensus mechanism grounded in a global trust model. This model capitalizes on node behavior to form consensus groups and facilitate consensus achievement. GT-BFT integrates a novel approach of selective broadcasting along with a Byzantine threshold determination algorithm, significantly boosting both the efficiency and security of the network. Our extensive analysis and performance evaluation reveal that GT-BFT surpasses existing mechanisms in key areas such as security, system throughput, and transaction confirmation speed, marking a significant advancement in blockchain consensus technology.
The rise of blockchain has expanded the possibilities for asset representation, particularly through Non-Fungible Tokens (NFTs), which enable unique connections between digital and physical assets. Despite their potential, existing NFT systems face challenges such as high transaction costs and uncertainties related to the management and transfer of associated physical assets. This paper introduces a decentralized marketplace architecture designed to address these challenges, by making use of blockchain-based smart contracts, decentralized storage solutions, and trustless validation mechanisms. The proposed system separates ownership from possession, ensuring secure and transparent asset transfers. The architecture was then tested in a blockchain-based environment, demonstrating its economic viability. This work paves the way for decentralized asset management across various domains, such as rental services and remote logistics. By addressing existing challenges, the proposed architecture provides a usable and cost-effective solution that links digital and physical asset ecosystems while preserving the fundamental principles of blockchain technology.
Blockchain Technology Applications and Security
Cloud Data Security Solutions
Physical Unclonable Functions (PUFs) and Hardware Security
Barbara Guidi, Andrea Michienzi, Leonardo Pasquale
The InterPlanetary File System (IPFS) is among the most well-known distributed file systems applied in Web3 applications, where data storage is involved. Non-Fungible Tokens (NFTs) represent one of the main scenarios in which distributed content sharing is enacted via IPFS. Generally, NFTs are created, sold and bought via so-called marketplaces, easing the technical difficulties. They play a crucial role during the sale of NFTs because they act as intermediaries between users and underlying technologies. In this paper, we evaluate the impact of NFT marketplaces on the availability of NFT resources in the IPFS network. We focus on the relationship between resources and marketplaces, trying to understand whether access to some of the resources related to NFTs is transparent and consistent with the information present in the contract. We selected $\mathbf{1 0}$ collections and 3 NFTs for each collection and monitored these assets on IPFS. For the monitoring phase, we deployed our node and requested providers of the assets every six hours for two weeks. Our analysis shows that IPFS does not guarantee high availability and marketplaces refer to copies of NFTs, thus violating the integrity and security guaranteed by NFTs and IPFS.
Blockchain has become a cornerstone of trustworthy, decentralised information governance. Consensus protocols and cryptographic linkages guarantee data integrity, immutability, and verifiable provenance, eliminating reliance on a single trusted authority and mitigating data fragmentation. Within smart‑healthcare ecosystems, these capabilities enable the shift from siloed, centralised repositories to distributed, patient‑centric infrastructures. Because clinical data are highly sensitive and strictly regulated, robust assurances of integrity, confidentiality, and fine‑grained authorisation are essential. Integrating blockchain and smart contracts with technologies such as distributed off‑chain storage and the Internet of Medical Things (IoMT) creates a resilient, scalable, and interoperable foundation for next‑generation healthcare data management. This research introduces hChain, a four‑generation family of distributed‑ledger frameworks that progressively strengthen security, intelligence, and scalability in smart‑healthcare environments. hChain 1.0 lays the groundwork with a blockchain architecture that safeguards patient data, supports real‑time clinical telemetry, and enables seamless inter‑institutional exchange. Building on this foundation, hChain 2.0 integrates InterPlanetary File System (IPFS) storage and smart‑contract enforcement to deliver tamper‑proof, fine‑grained access control. hChain 3.0 embeds on‑chain deep‑learning analytics, providing proactive, automated decision support across the care continuum while preserving data integrity. Finally, hChain 4.0 introduces a highly scalable, permissioned ledger augmented by an Attribute‑Based Access Control (ABAC) layer, ensuring dynamic, context‑aware authorisation in complex organisational settings. The results demonstrate practical solutions for transforming data infrastructures from centralised to decentralised architectures, providing techniques that facilitate seamless integration with existing systems while enhancing blockchain scalability and privacy.
In the rapidly evolving landscape of cloud computing, ensuring secure user authentication and protection against cyber-attacks has become increasingly critical. This research proposes a novel security framework for cloud systems based on the Quantum Zero-Knowledge Proof (ZKP) technique, aiming to provide a privacy-preserving and quantum-resilient authentication mechanism. The core of the proposed model lies in leveraging photon polarization at specific quantum angles to implement secure and non-disclosive verification, effectively allowing users (provers) to prove their identity without revealing any sensitive credentials. The system's architecture integrates a Zero Knowledge Proof Engine (ZKE), which forms the backbone of the security protocol, enhancing resilience against Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks. The quantum properties of photons enable a high level of randomness and unpredictability, significantly improving the robustness of the system. A Python-based simulation environment has been developed to model the proposed engine and conduct experimental validations. Furthermore, a web-based application interface has been designed to facilitate seamless interaction between cloud users and the authentication system, demonstrating real-time threat detection and response. Experimental results, visualized through performance metrics and interface output, confirm the effectiveness and practicality of the proposed model. This approach not only enhances security but also offers a scalable and user-friendly solution for modern cloud environments, marking a significant step toward integrating quantum principles into mainstream cybersecurity infrastructures.
This work proposes a Fiat-Shamir linkable ring lattice-based signature scheme, building on Yuen et al. ‘s generic ring signature. It converts the MLWE-based Σ-protocol into a linkable ring signature through the Fiat-Shamir transformation, employing rejection sampling to avoid private key leakage. Linkability is achieved by attaching a public key structure label, avoiding complex zero-knowledge proofs and pseudorandom function calculations. Compared to other lattice-based schemes, our approach improves signature length. Security analysis under the random oracle model confirms correctness, anonymity, unforgeability, linkability, and non-slanderability.
Bhatt Vinayak Vishwanath, Nishanth Kumar Pathi, Shinu Abhi
Zero Knowledge Proof (ZKP) is a cryptographic method that allows a prover to demonstrate to a verifier that a statement is true without revealing any additional information. This is highly relevant in healthcare, where data privacy is critical. By integrating ZKP into healthcare applications, sensitive patient data can be securely verified without exposing unnecessary information. In healthcare, protecting sensitive data while enabling seamless collaboration between doctors, laboratories, and other entities is crucial. Sharing or verifying data often leads to privacy risks. The project aims to develop a ZKP protocol for a healthcare system, allowing secure and private verification of sensitive medical data between doctors and laboratories. The ZKP protocol is implemented using a hybrid system with cloud services and local machines for secure computation. AWS Key Management Service (KMS) handles encryption and decryption. The protocol creates cryptographic proof for verifiers to confirm data validity without accessing the data itself. Established cryptographic libraries and healthcare test data are used in the implementation. The system ensures scalability and security by leveraging AWS infrastructure and MySQL database integration for proof storage. The ZKP protocol successfully verified health care data without exposing sensitive information, reducing the risk of data breaches. This approach improves data security without compromising functionality, setting new privacy standards in healthcare data management.
The research proposes a privacy-preserving predictive analytics paradigm that combines Federated Learning (FL), Distributed Ledger Technology (DLT), and Multi-Factor Authentication (MFA) to strengthen data privacy and security. FL supports decentralized model training, avoiding data centralization risks, and DLT protects the integrity of data through immutable and transparent blockchains. MFA strengthens access control through multi-layered authentication, reducing unauthorized access risks. The suggested model performs better than existing solutions such as Trusted Execution Environments (TEEs) and Attribute-Based Encryption (ABE) with accuracy of 93%, efficiency of 92%, and recall of 94%. The hybrid model ensures secure, scalable, and privacy-focused predictive analytics and can be applied in healthcare, finance, and IoT-based environments. This framework provides a strong solution to improve trust, transparency, and operational effectiveness in predictive modelling in sensitive domains by resolving confidentiality issues without compromising high predictive accuracy. Optimization methods and post-quantum cryptography will be investigated in future work for further security.
Prasanna Ramakrisnan, Mohd Farhan Shah Ahmad Rusli, Mike Soon Tai Gan Hou
Online learning platforms have reshaped higher education, with digital certificates becoming a key qualification method. However, these certificates are vulnerable to forgery and falsification. This chapter examines the use of blockchain frameworks to enhance the security and verification of digital certificates at Universiti Teknologi MARA (UiTM), Malaysia. Blockchain technology, with its secure and decentralized nature, utilizes a distributed ledger to prevent tampering and unauthorized changes. By employing cryptographic hashes and interconnected blocks, the framework ensures transparency and tamper resistance. This decentralized approach revolutionizes certificate issuance and verification, securely recording transactions across a network of computers and enhancing the integrity of the process. The proposed framework offers a reliable solution for stakeholders to verify certificate authenticity confidently. This blockchain framework aims to instill trust in the certification process, making digital certificates symbols of security and transparency in online education.
The increasing cases of fake certificates in Bangladesh have seriously undermined the legitimacy of professional and academic qualifications and adherence to procedures. To address this issue, we designed a blockchainbased system that ensures secure storage and certification of certificates. The system encrypts the certificates before storing certificates on the InterPlanetary File System (IPFS) using an extremely low encryption technique. This indicates that the certificates are safe from unauthorized use and cannot be tampered with. One of its strongest advantages is the usercentric approach of the system, which grants certificate holders complete control over the individuals allowed to access their credentials. The fact that every verification request decrypts only after going through the approval from the owner of the certificate provides yet another security layer. The technique establishes an irreversible, permanent record of verified certificates on the Ethereum block chain to ensure that only genuine credentials are welcomed. This strategy not only halts the proliferation of counterfeit certifications but also works towards re-establishing trust in educational and professional credentials in Bangladesh.
Abdulrahman Alzahrani, Amin Y. Noaman, Ahmed A. A. Gad-Elrab, Fathy Eassa · 7 authors
Interoperability among heterogeneous blockchain platforms remains a significant challenge in decentralized ecosystems, especially with the growing adoption of blockchain across various industries. This research introduces the Data Standardization Module (DSM). This framework facilitates seamless communication between platforms like Ethereum and Hyperledger Fabric using a unified schema for data transformation and exchange. DSM leverages Concise Binary Object Representation (CBOR) encoding, which reduces data size by 60 % and achieves a compression ratio (CR) of 2.5. This outperforms conventional JSON-based methods, typically achieving only 20–30 % compression with lower CR values. The system ensures data security and integrity through end-to-end encryption, access control, and validation mechanisms. Performance evaluations show that DSM supports an average throughput of 250 transactions per second (TPS). In comparison, interoperability frameworks like Cosmos and Polkadot typically achieve 100–150 TPS, while early implementations of Hyperledger Fabric reported TPS as low as 300. However, optimized settings of Hyperledger Fabric now achieve over 100,000 TPS. DSM strikes a balance by providing high throughput with minimal resource overhead, making it suitable for real-world applications. This work advances blockchain interoperability by offering a lightweight, secure, and scalable framework, ideal for high-frequency use cases in healthcare, finance, and supply chains.
Jiahui Huang, Teng Huang, Changyu Dong, Sisi Duan · 5 authors
The expansion of blockchain applications, particularly on platforms like Ethereum, brings escalating security challenges as account anonymity provides breeding grounds for criminals to commit crimes and cause significant economic losses. As the mainstream architecture of de-anonymization technology, graph neural networks (GNNs) provide empirical tools for law enforcement agencies to investigate illegal activities. However, the limited expressiveness of current GNNs leads to performance degradation for Ethereum account de-anonymization. To address this challenge, we propose an innovative Local-Global Awareness (LGA) framework, which consists of a Local Structure-Aware (LSA) module and a Global Information-Aware (GIA) module. LSA integrates subgraph-level encoding strategies with local attention to enhance the capture of microscopic interactions. As a complementary measure, GIA introduces global attention to facilitate the understanding of macroscopic information. The LGA framework meticulously captures subgraph-level account behavior patterns at a granular level while simultaneously incorporating global contextual insights, demonstrating higher-level expressive power and receptive fields over conventional GNN. The efficacy of the LGA framework is corroborated by experimental evaluations conducted on the lw-AIG dataset. Our framework achieves exceptional performance, significantly outstripping state-of-the-art GNN-based methods in terms of the micro F1 score metric, with relative improvements ranging from 0.14% to 6.63%. Through its detailed and comprehensive analysis of account interactions, the LGA framework aims to provide a potent solution to the complex security challenges faced in the expanding blockchain landscape. The code for LGA is available at https://github.com/deepang-ai/LGA.
Abstract—File storage platforms face inherent challenges such as censorship, limited transparency, vulnerability to single points of failure, and restricted user control over data. To address these limitations, this paper proposes a decentralized file-sharing system that integrates the Ethereum blockchain with the InterPlanetary File System (IPFS). Our design leverages smart contracts to securely manage file metadata and enforce access controls, providing an immutable and tamper-resistant record of data ownership and permissions. IPFS is utilized for efficient, distributed file storage, enhancing scalability and availability. User authentication is handled through wallet-based cryptographic verification, eliminating reliance on centralized identity providers. Additionally, the system supports micropayment- based monetization via smart contracts, enabling direct and transparent transactions between content creators and consumers. The proposed platform delivers a secure, censorship-resistant, and user-empowered file-sharing environment consistent with the principles of Web3. Keywords- Blockchain, IPFS, Smart Contracts,
In supply chain finance (SCF), the long-standing issue of "difficult and expensive financing" has hindered SMEs' growth, with blockchain technology offering a novel solution.This study adopts a theoretical framework of supply chain internal and external financing to systematically analyze financing models: internal financing for upstream manufacturers, midstream distributors, and downstream e-commerce enterprises, and external bank financing via blockchain platforms.It compares decision-making differences between traditional and blockchain-enabled financing, revealing that blockchain technology reshapes the financing landscape through three core mechanisms: information sharing via distributed ledgers, credit transmission across supply chain tiers, and cost optimization through smart contracts.The study finds that blockchain reconstructs the trust system, optimizes banks' risk pricing, and alleviates financing constraints for end-tier enterprises.Additionally, platforms dominated by different entities (e.g., manufacturers, e-commerce companies, and banks) reshape supply chain pricing and profit distribution through differentiated governance rules.These findings provide theoretical support for integrating "blockchain + SCF" and guide supply chain members in optimizing financing decisions and technology adoption strategies.
Dumitrel Loghin, Shuang Liang, S. Liu, Xiong Liu · 6 authors
Zero-knowledge proofs (ZKP) are becoming a gold standard in scaling blockchains and bringing Web3 to life. At the same time, ZKP for transactions running on the Ethereum Virtual Machine require powerful servers with hundreds of CPU cores. The current zkProver implementation from Polygon is optimized for x86-64 CPUs by vectorizing key operations, such as Merkle tree building with Poseidon hashes over the Goldilocks field, with Advanced Vector Extensions (AVX and AVX512). With these optimizations, a ZKP for a batch of transactions is generated in less than two minutes. With the advent of cloud servers with ARM which are at least 10% cheaper than x86-64 servers and the implementation of ARM Scalable Vector Extension (SVE), we wonder if ARM servers can take over their x86-64 counterparts. Unfortunately, our analysis shows that current ARM CPUs are not a match for their x86-64 competitors. Graviton4 from Amazon Web Services (AWS) and Axion from Google Cloud Platform (GCP) are 1.6X and 1.4X slower compared to the latest AMD EPYC and Intel Xeon servers from AWS with AVX and AVX512, respectively, when building a Merkle tree with over four million leaves. This low performance is due to (1) smaller vector size in these ARM CPUs (128 bits versus 512 bits in AVX512) and (2) lower clock frequency. On the other hand, ARM SVE/SVE2 Instruction Set Architecture (ISA) is at least as powerful as AVX/AVX512 but more flexible. Moreover, we estimate that increasing the vector size to 512 bits will enable higher performance in ARM CPUs compared to their x86-64 counterparts while maintaining their price advantage.
Syeda Tasfia Tasnim, Md. Ariful Islam, Mohammed Shafiul Alam Khan
Certificate fraud remains a persistent issue, undermining trust in digital verification systems and imposing significant economic and social costs. Existing blockchain-based frameworks often suffer from high transaction fees, limited scalability, and complex operational models. In this paper, we propose a novel certificate verification framework built on the Sui blockchain, leveraging its object-centric architecture and parallel execution to address these challenges. Our solution improves upon prior models by achieving significantly lower transaction costs, faster execution times, and a streamlined smart contract design for issuer authorization, certificate issuance, verification, and revocation. We prototype and benchmark the framework against Ethereum-based implementations, demonstrating clear performance advantages. Future work envisions integrating de-centralized storage through Walrus and enhancing security and interoperability features to further strengthen the framework’s practical adoption.
Zhaoyang Xie, Haibin Zhang, Sisi Duan, Chao Liu · 11 authors
A key management service (KMS) is vital to modern mission-critical systems. At the core of KMS are the key generation process and the key refresh process. In this paper, we design and implement a purely asynchronous system for completely distributed KMS supporting traditional applications such as threshold cryptosystems and multiparty computation (MPC) as well as emerging blockchains and Web3 applications. In this system, we have built a number of new asynchronous distributed key generation (ADKG) protocols and their corresponding asynchronous distributed key refresh (ADKR) protocols. We have demonstrated that our ADKG and ADKR protocols in the standard model outperform existing ones of the same kind, while our protocols in the random oracle model (ROM) are more efficient than other protocols with small and medium-sized networks.
With the rapid advancement of technology, cloud computing has emerged as the most popular and promising service platform. A cloud user can delegate heavy computation tasks to cloud servers. To ensure the correctness of outsourced processing (e.g., machine learning and data mining), the cloud server must prove that the processing has been executed properly. However, even without malicious intent, it is possible for a cloud server to produce incorrect results. Consequently, clients may outsource the same task to multiple cloud servers and receive various results, aiding them in selecting the best outcome. To protect data privacy, the cloud server must encrypt the results before sending them back to the user. Yet, processing and verifying encrypted results remain significant challenges. To avoid the expensive computational overhead of decrypting ciphertexts from cloud servers one by one, clients prefer to use homomorphic encryption (HE) to obtain the combined output from a single server. However, existing schemes fall short of efficiently verifying the correctness of computations over encrypted data processed by multiple cloud servers, especially in extracting the results computed by each server. In this paper, we introduce a new framework for verifiable outsourced computing systems. In this system, each cloud server's computation result is protected by Paillier encryption, and the edge server can verify these results using zero-knowledge proofs and aggregate the verified ciphertexts. The client can extract the combined plaintext through the Base-3 conversion algorithm to identify each cloud server's results and any non-participating servers. We also prove the security of our scheme and analyze its performance from both theoretical and experimental aspects. Performance analysis shows that our system significantly reduces the client's workload and is userfriendly