This paper presents a Web3-based healthcare system integrated with the Republic of Korea's MyHealthWay platform for secure and user-controlled management of personal health data. The system combines decentralized identifiers, smart contracts, distributed storage, and the HL7 FHIR standard to support decentralized authentication, access control, and interoperability. A conceptual demonstrator, HealthCube, validates feasibility by enabling privacy-preserving health data processing through computation on encrypted data without exposing original information.
Ravisankar M, Raghunandhan V, Senthil Pandi S, T. Kalai Selvi ยท 6 authors
In the rapidly evolving healthcare sector, the accurate forecasting and secure management of health insurance claims are crucial for both insurance providers and their clients. This study introduces an integrated framework that merges machine learning algorithms with blockchain infrastructure to enhance fraud prevention and optimize the prediction and processing of health insurance claims. The primary objective is to accurately predict claim amounts using advanced regression modeling and to streamline the entire claims administration process via an open, transparent, and decentralized blockchain network. For the predictive component, the XGBoost Gradient Regression algorithm was implemented. To complement this forecasting capability , A blockchain-based system is proposed for managing health insurance claims. This system enables secure collaboration and data exchange among key stakeholders, including hospitals, insurance firms, laboratories, and third-party administrators. The blockchain layer ensures data integrity, transparency, and security, substantially reducing opportunities for fraud, processing delays, and administrative overhead. By uniting the predictive power of machine learning with the trust and efficiency of blockchain, this project offers a robust, intelligent, and secure solution for modern health insurance management.
AbstractโThe paradigm of digital identity is rapidly shifting from centralized monopolistic control toward decentralized user- centric frameworks to meet the demands of Web3, immer- sive computing and trustless interactions. Traditional identity systems expose users to privacy breaches, vendor lock-in and cross-platform incompatibilities, creating barriers for seamless adoption. To overcome these challenges DIGICRED introduces a decentralized identity and credential system built on Self- Sovereign Identity (SSI) principles leveraging Decentralized Iden- tifiers (DIDs) and blockchain-based cryptographic proofs as the foundation for trust. A verifiable credential layer enables selective disclosure of tamper-proof claims ranging from aca- demic certifications to government-issued IDs preserving privacy while ensuring interoperability. Complementing this a multi- dimensional reputation system fosters trust in anonymous en- vironments, mitigates Sybil attacks and incentivizes meaningful participation across decentralized applications. By integrating privacy-preserving technologies, compliance-aware architectures and scalable trust mechanisms DIGICRED redefines identity management for Web3. This shift marks the emergence of secure portable and user-controlled digital identities laying the groundwork for the future of decentralized applications and cross-platform digital ecosystems. Index TermsโSelf-Sovereign Identity (SSI), Decentralized Identifiers (DIDs), Verifiable Credentials (VCs), Blockchain, Web3, Digital Identity, Privacy Preservation, Reputation Systems, Trust Management, Cross-Platform Interoperability. .
The existing methods do not effectively meet the security and performance demands for Internet of Vehicles (IoV) applications. They also do not provide low-latency, secure edge-computing solutions for end-users in vehicular environments. The study presented in this paper proposes a blockchain-based edge computing framework that utilises Double Deep Q-Network (DDQN) for reinforcement learning and lightweight Practical Byzantine Fault Tolerance (PBFT) consensus for simultaneously optimising latency, energy consumption, and security. For efficient microservice orchestration and task off-loading, the containerised architecture utilises Kubernetes with Hyperledger Fabric. The experiments conducted in urban, suburban, and highway scenarios confirmed that the proposed framework outperformed baseline algorithms with end-to-end latency reduction of 30โ45% while also lowering energy consumption by up to 55% under moderate-to-heavy loads. With less than 1.2 seconds per block on the blockchain consensus, the system also maintained task completion rates exceeding 95% during peak conditions. The framework demonstrates consistent performance across various vehicular densities and consumes zero-knowledge proofs with attribute-based encryption for data against cybersecurity threats. These results confirm that the integration of DDQN and blockchain technology effectively tackles primary obstacles IoV faces by providing secure edge computing for next generation vehicular networks.
Wazir Zada Khan, Ayesha Siddiqa, Faisal Alanazi, Muhammad Khurram Khan
The Metaverse creates a 3D virtual environment similar to the real world, enabling immersive interactions across diverse fields such as education, healthcare, and gaming. A critical aspect of these interactions is digital identity authentication, which ensures secure and trustworthy user experiences. This paper proposes a novel Non-Fungible Token (NFT) based digital identity authentication framework for the Metaverse, leveraging blockchain technology and Elliptic Curve Cryptography (ECC) to enhance security and user trust. The framework is tested using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool, demonstrating its resilience against replay and man-in-the-middle (MITM) attacks. Our contributions include:(1)a secure NFT-based authentication mechanism,(2)a formal security analysis validating the frameworkโs robustness, and(3)a comprehensive discussion of practical implications. The proposed framework addresses key gaps in existing methods, offering a scalable and user-friendly solution for digital identity authentication in the Metaverse.
Qing Fang, Hong Su, Xi Wu, Haichuan Zhang ยท 5 authors
Smart contracts are essential tools for enabling interaction between blockchain and Internet of Things (IoT) systems. For example, in cold chain logistics, the blockchain can obtain the states of the logistics system through smart contracts. However, direct interactions between smart contracts and these systems introduce uncertainties, potentially leading to network forks or state inconsistencies, which can compromise the security and reliability of the blockchain. To address these challenges, a novel smart contract variable, ExState, is proposed, specifically designed to track and store the dynamic states of IoT systems. Additionally, a corresponding operational logic is defined to organize these states into sequential records, ensuring that the state sequences obtained by each node remain consistent, effectively mitigating state conflicts. In addition, a formal model is developed, accompanied by a theoretical analysis of its determinacy. Experimental results demonstrate that, in cross-chain scenarios, this method achieves a performance improvement of up to 50.41% compared to the traditional Oracle method.
The increasing prevalence of certificate forgery in Bangladesh poses a significant threat to the credibility of academic and professional qualifications. To address this issue, a blockchain-based platform has been developed for securely certifying and storing certificates. The system employs an ultra-lightweight encryption algorithm to safeguard certificates before storing them on the InterPlanetary File System (IPFS), ensuring both tamper resistance and protection against unauthorized access. A key feature of this system is its user-centric approach, granting certificate holders full control over access to their credentials. Data is decrypted only upon approval from the certificate owner for each verification request, adding an extra layer of security. The Ethereum blockchain is utilized to establish an immutable and permanent record of verified certificates, ensuring that only authentic credentials are recognized. By preventing the proliferation of fraudulent certificates, this approach enhances trust in academic and professional qualifications across Bangladesh.
This paper discusses the application of blockchain technology in document management for the library. With the characteristics of encryption algorithm, distributed ledger, distributed architecture and traceability, this technology is of great significance in enhancing the security of document resources, optimizing the copyright protection, improving the efficiency of resource sharing and collaboration, and optimizing the experience of reader services. Its application covers the joint collection and edit system of document resources based on blockchain, decentralized storage and distribution platform, knowledge graph co-construction and sharing, and evaluation and contribution incentive mechanism participated by readers, so as to promote more efficient and intelligent library services.
As a distributed approach to problem solving, crowdsourcing reduces costs and efficiently utilizes resources. While blockchain technology is introduced to solve the problem of over-centralization in traditional crowdsourcing platforms, its transparency brings the risk of privacy leakage. The traditional anonymous authentication can hide the userโs identity, but the anonymity is abused, and the worker selection gets more difficult. In this study, a decentralized accountable attribute-based authentication scheme is proposed and combined with blockchain to design a novel crowdsourcing scheme. Using decentralized attribute-based encryption and non-interactive zero-knowledge proof, the scheme protects the privacy of usersโ identities with linkability and traceability, and the requester can devise access policies to select workers. In addition, the scheme improves the security of the system by implementing attribute authorization authority and tracking groups through the threshold secret sharing technique. Through experimental simulation and analysis, it is demonstrated that the scheme meets the requirements of time and storage overhead in practical application.
Innovation in Digital Healthcare Systems
Internet of Things and Social Network Interactions
In todayโs digital landscape, safeguarding sensitive information during transactions has become increasingly critical. This project focuses on developing a secure data exchange framework by integrating cryptographic techniques, blockchain technology, and steganography. Inspired by blockchain-based asset management systems and decentralized transaction models, the study employs cryptographic methods like SHA-256 hashing, Proof of Work (PoW), and Proof of Stake (PoS) to ensure data integrity, authenticity, and confidentiality. To further enhance security, the project incorporates multimodal steganography and Least Significant Bit (LSB) techniques for embedding sensitive information within digital media. The combined strengths of blockchainโs immutable ledger and the covert nature of steganography create a system that prioritizes end-to-end security, privacy, and resistance to tampering. The methodology involves creating a decentralized peer-to-peer network for validating transactions, using advanced encryption techniques, and applying steganographic methods to conceal data within multimedia formats. Testing demonstrates the systemโs ability to prevent data breaches, support secure transactions, and withstand cyberattacks. This innovative hybrid model offers a reliable solution for secured digital communications and transactions across finance, healthcare, and e-commerce domains. Future advancements could explore quantum-resistant cryptographic approaches and AI-driven mechanisms to detect and mitigate fraud.
The insurance claim process is quite cumbersome; it is time-consuming, with high personnel costs from manual review. It may even take several months to complete the entire process. Therefore, how implementing insurance claim settlement automation to reduce costs, improve efficiency, reduce claim processing time, and increase client satisfaction is a common issue the insurance industry must face. This study explores the application of smart contracts in the casualty insurance settlement process to achieve the effect of automatic claim settlement and double protection for special accidents. When the insurance industry conducts insurance claim reviews through the characteristics of blockchain and smart contracts, such as openness and transparency, anonymity, and automation, the review process can be curtailed, and the premium can be directly transferred to the bank account of the insured. Thus, the purpose of automating casualty insurance claims is achieved through smart contracts.
The process of rendering authenticity to the Degree Certificate (DC) is known as Degree Attestation (DA). None of the prevailing works have focused on zero trust-based DA, verification, and traceability for secured DA. So, zero trust-based secured DA, verification, and traceability of degree credentials are presented in the paper. Primarily, to upload the DC of the student, the university registers and logs in to the Blockchain (BC). Subsequently, by utilizing radioactive decay-based elliptic curve cryptography (RD-ECC), the DC is secured. Next, by utilizing Glorot initialization-based Proof-of-Stake (GPoS), the data is stored in the BC. Further, to verify the traceability of the data, a Smart Contract (SC) is created. In the meantime, the student registers and logs in to the BC and gives attestation requests to the university. By utilizing rail fence cipher (RFC) RD-ECC hash-based message authentication code (RFCR-HMAC), the university authenticates the request. By utilizing a quadratic probing-based digital signature algorithm (QP-DSA), the university attests the DC after authentication. Lastly, by utilizing RD-ECC, the attested certificate is encrypted and sent to the student. Hence, the certificate is secured with an encryption time (ET) of 5971ms and DA is performed with a Signature Generation Time (SGT) of 6637ms.
Jie Yang, Yuta Kodera, Samsul Huda, Yasuyuki Nogami
In the distributed medical information management system, blockchain technology has more obvious advantages in terms of data tampering data traceability. However, considering the scalability issues of current Layer-1 blockchain, uploading massive medical information data onto the blockchain will add non-negligible space storage pressure. Patients are also unable to have a comprehensive grasp of their personal privacy information retained in these data, which can lead to concern about the systemโs privacy and security. To cope with these challenges, this paper introduced Layer-2 network to reduce the data space occupation in a single block. By storing unnecessary information in the off-chain channel, the transaction speed and throughput of the system would be improved. In addition, to strengthen the security within the process of data communication and storage, this paper also deployed the system based on consortium blockchain utilizing AWS cloud service. And the system integrated with the zero-knowledge proof algorithm zk-SNARKs to protect personal information privacy. According to the experimental simulation and analysis, the proposed scheme can reduce data storage space in the Lay-1 blockchain. Whatโs more, it also allows quick verification of on-chain data without disclosing personal privacy information.
In recent years, many researchers have demonstrated that privacy acumen can be applied to healthcare applications to enhance patientsโ life quality using Machine Learning (ML) and Deep Learning (DL). As far as we are aware, a tiny degree of patient privacy is contained in medical data, which necessitates extreme data protection against disclosure to intruders. In machine learning and deep learning, to build accurate system data pooling done at server, causes data breach, to overcome this issue, in 2016, Google proposed an idea to share learned models rather than sharing the data called Federated Learning (FL). In this systematic analysis, we lay out FL's primary objective concerning privacy. Afterwards, we presented the primary challenges in configuring and deploying federated learning networks and introduce algorithms within advanced federated learning architectures by integrating Convolutional Neural Networks (CNNs), a blockchain-based mechanism, and a non-fungible token (NFT) for the detection of various harmful diseases. moreover, we analysed the configuration settings, performances, Limitations of different algorithms against different datasets. Ultimately, we conclude this analysis by discussing optimization and communication challenges in future FL smart healthcare systems. We believe that reading this through can be beneficial for academicians, industry experts, and neoteric alike, providing them with direction and suggestion for their next endeavours.