Anitha Julian, Gerardine Immaculate Mary, S. Sharmila Deva Selvi, Mayur Rele · 5 authors
Securing sensitive user information and maintaining privacy during authentication and authorization processes is crucial in today’s interconnected digital world. This study investigates new approaches to tackle these obstacles by combining blockchain technology with Trusted Execution Environment (TEE). The proposed system introduces a new method that combines the advantages of both technologies to create a strong and privacy-focused framework for network security. The study assesses the effectiveness of the suggested system based on key parameters, focusing specifically on Transaction Throughput (TPS) and Latency. The combination of blockchain and TEE aims to improve privacy protection and security protocols, providing a dependable solution for privacy-focused authentication and authorization in network settings. The experiments confirmed the efficacy of the proposed model. The Transaction Throughput (TPS) reached 296 transactions per second, demonstrating the system’s scalability and capacity to manage a large number of transactions. The Latency was measured at 54 milliseconds, indicating a fast and responsive authentication and authorization process. The research enhances the development of privacy-preserving solutions by emphasizing the importance of the blockchain and TEE collaboration. The proposed model surpasses performance expectations set by traditional methods, positioning itself as a significant advancement in network security. The results highlight the system’s ability to significantly improve privacy protection in digital transactions and set the stage for advancements in secure, efficient, and privacy-focused authentication and authorization systems. between the quantity demand and the price.
As the volume of healthcare and medical data increases from diverse sources, real-world scenarios involving data sharing and collaboration have certain challenges, including the risk of privacy leakage, difficulty in data fusion, low reliability of data storage, low effectiveness of data sharing, etc. To guarantee the service quality of data collaboration, this paper presents a privacy-preserving Healthcare and Medical Data Collaboration Service System combining Blockchain with Federated Learning, termed FL-HMChain. This system is composed of three layers: Data extraction and storage, data management, and data application. Focusing on healthcare and medical data, a healthcare and medical blockchain is constructed to realize data storage, transfer, processing, and access with security, real-time, reliability, and integrity. An improved master node selection consensus mechanism is presented to detect and prevent dishonest behavior, ensuring the overall reliability and trustworthiness of the collaborative model training process. Furthermore, healthcare and medical data collaboration services in real-world scenarios have been discussed and developed. To further validate the performance of FL-HMChain, a Convolutional Neural Network-based Federated Learning (FL-CNN-HMChain) model is investigated for medical image identification. This model achieves better performance compared to the baseline Convolutional Neural Network (CNN), having an average improvement of 4.7% on Area Under Curve (AUC) and 7% on Accuracy (ACC), respectively. Furthermore, the probability of privacy leakage can be effectively reduced by the blockchain-based parameter transfer mechanism in federated learning between local and global models.
Federated Learning (FL) is a novel distributed, privacy-preserving machine learning paradigm. Conventional FL suffers from drawbacks such as single point of failure and client drift. Blockchain is a distributed computing architecture famous for decentralization, transparency, and traceability. Incorporating blockchain as the underlying basis for FL decentralizes the FL process and brings opportunities to resolve the drawbacks. However, there still remain challenges to fulfilling FL with blockchain, regarding effectiveness, efficiency, and security. In this paper, we propose a new blockchain system for FL, called FedChain. To mitigate client drift and accelerate training, we present a clustered semi-asynchronous method for model aggregation. To optimize the local training in FL, we introduce a knowledge transfer method using other clients on the peer-to-peer network of blockchain. Moreover, we implement an access control mechanism to store and transmit models safely and efficiently. Extensive experiments on various benchmark datasets show that FedChain achieves superior results in accuracy, convergence, throughput, and latency.
Existing multitask outsourcing computations struggle to guarantee the fairness for participants and the correctness of the computation results. Some solutions use blockchain to address the fairness issue in outsourcing computations. However, blockchain suffers from poor data privacy due to its public and transparent nature, as well as the latency because of limited scalability. To effectively confront these problems, we propose the Libras: a fair, secure, verifiable and scalable outsourcing computation scheme based on blockchain. In Libras, tasks are divided into multiple sub-task blocks, coupled with a deposit mechanism that enforces fairness throughout the process. Libras integrates a commitment mechanism with on-chain and off-chain collaboration for security, where the computation results are securely stored off-chain while proofs of these results are immutably recorded on-chain. Moreover, it employs a Directed Acyclic Graph (DAG)-based ledger architecture to significantly expedite transaction confirmations and facilitate elastic scalability. Furthermore, we devise a batch verification algorithm to simultaneously verify the accuracy of all computation results. Theoretical analysis and experiments demonstrate that Libras is fair, secure, verifiable, and scalable. The comparison results indicate that the verification time is 1.2× that of FVP-EOC.
Syed M. Aqleem Abbas, Muazzam A. Khan, Wadii Boulila, Anis Kouba · 6 authors
Unmanned aerial vehicles (UAVs) can be used as drones’ edge Intelligence to assist with data collection, training models, and communication over wireless networks. UAV use for smart cities is rapidly growing in various industries, including tracking and surveillance, military defense, managing healthcare delivery, wireless communications, and more. In traditional machine learning techniques, an enormous amount of sensor data from UAVs must be shared to central storage to perform model training, which poses serious privacy risks and risks of misuse of information. The federated learning technique (FL), which can be applied to UAVs, is a promising means of collaboratively training a global model while retaining local access to sensitive raw data. Despite this, FL is a significant communication burden for battery-constrained UAVs due to local model training and global synchronization frequency. In this article, we address the major challenges associated with UAV-based FL for smart cities, including single-point failure, privacy leakage, scalability, and global model verification. To tackle these challenges, we present a differentially private federated learning framework based on Accumulative Reputation-based Selection (ARS) for the edge-aided UAV network that utilizes blockchains to prevent single-point failures where we switched from central control to decentralized control, Interplanetary File System (IPFS) for off-chain model storage and their respective hash-keys on-chain to ensure model integrity. Due to IPFS, the size of the blockchain will be reduced, and local differential privacy will be applied to prevent privacy leakages. In the proposed framework, an aggregator will be selected based on its ARS score and model verification by the validators. After most validators approve it, it will be available for use. Several parameters are taken into consideration during evaluation, including accuracy, precision, recall, F1-score, and time consumption. It also evaluates the number of edge computers vs test accuracy, the number of edge computers vs time consumption for global model convergence, and the number of rounds vs test accuracy. This is done by considering two benchmark datasets: MNIST and CIFAR-10. The results show that the proposed work preserves privacy while achieving high accuracy. Moreover, it is scalable to accommodate many participants.
Distributed machine learning, and Decentralized Federated Learning in particular, is emerging as an effective solution to cope with the ever-increasing amount of data and the need to process it faster and more reliably. It enables machine learning models to be trained without centralizing user data, which improves data confidentiality and optimizes performance compared with centralized approaches. However, scaling up such systems can have limitations in terms of data and model traceability and security. To address this limitation, the integration of Blockchain has been proposed, forming a global system leveraging Blockchain, called Blockchain Based Decentralized Federated Learning (BDFL), and taking advantage of the benefits of this technology, namely transparency, immutability and decentralization. For the time being, few studies have sought to characterize these BDFL systems, although it seems that they can be broken down into a set of layers (blockchain, interconnection of DFL nodes, client selection, data transmission, consensus management) that could have a major impact on the operation of the BDFL as a whole. The aim of this article is therefore to respond to this limitation by highlighting the different layers existing in the architecture of a BDFL system and the solutions proposed in the literature that can be integrated to optimise both the performance and the security of the system. This could ultimately lead to the design of more secure and efficient architectures with greater resilience to attacks and architectural changes.
The rise of connected and autonomous vehicles (CAVs) within intelligent transportation systems has introduced new demands for real-time, scalable, and privacy-preserving authentication mechanisms. Traditional authentication methods, such as Public Key Infrastructure (PKI), are often insufficient in highly dynamic vehicular environments due to their reliance on static credentials and centralized control. This paper proposes an adaptive and context-aware authentication framework that integrates Edge Artificial Intelligence (AI) with blockchain technology to secure vehicular communication. The framework leverages edge- based AI models to assess driver behavior and contextual signals in real time, generating dynamic trust scores for authentication. These scores are verified and recorded through a permissioned blockchain, ensuring tamper-proof identity validation and decentralized access control. The proposed system addresses key challenges including low latency, dynamic trust evaluation, and conditional privacy. Through detailed architectural design and security analysis, this work highlights the potential of hybrid AI-blockchain models to enhance the security, scalability, and accountability of future vehicular networks.
Wireless body area networks (WBAN) are essential components of intelligent healthcare monitoring techniques. Especially, when the number and datatype inWBANincreases. InWBAN, secure multidimensional data aggregation received a lot of attention. However, the related schemes consume more computational and communication overhead to encrypt/decrypt the multidimensional health reports. In this paper, a blockchain-assisted scalable and secure multidimensional data aggregation scheme is introduced for fog-basedWBAN. The multidimensional health data are efficiently generated, encrypted, and decrypted by using the Paillier cryptosystem. Further, the batch verification method is used to achieve efficient authentication. The proposed system offers significant security attributes with less computation and communication overhead in comparison with competing systems. Further, it supports statistical analyses such as summation and variance to analyze the received health report.
Electronic health records (EHRs) represent an innovative approach to constructing a distributed data analysis framework for managing health data within its original location. The integration of blockchain technology into EHR systems offers substantial improvements in security, privacy, and transparency, thus enhancing overall management efficiency. Our research spanned from January 2017 to December 2022, conducting a meticulous systematic literature review across reputable databases such as Scopus, IEEE Xplore, Springer, PubMed Central, and ScienceDirect. This comprehensive search, finalized in December 2022, utilized stringent inclusion and exclusion criteria to ensure the selection of high‐quality articles, thereby guaranteeing transparency and unbiased results. Through this systematic review, our primary objective was to explore, assess, and analyze the diverse architectures, proposed models, limitations, and future trajectories of blockchain‐enabled EHR systems. Our emphasis was on highlighting blockchain’s adaptability and robustness in healthcare contexts while identifying potential challenges and areas for further investigation. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis guidelines, we scrutinized more than 600 scientific studies, culminating in the selection of 31 articles that met our rigorous inclusion standards. Our technical and architectural evaluations delved into critical aspects such as privacy, security, authentication, availability, data control, storage, and resource consumption. Our research outcomes underscore the effective resolution of security, privacy, and availability concerns in EHRs through blockchain integration. However, we observed potential trade‐offs, including impacts on performance, resource utilization, and regulatory compliance. To address these complexities, we introduce an integrated framework designed to mitigate key challenges and deliver substantial value within this domain.
As machine learning permeates sensitive domains such as healthcare, finance, and government, protecting individual privacy while leveraging large-scale data remains a paramount challenge. Privacy-Preserving Machine Learning (PPML) combines cryptographic techniques, decentralized training paradigms, and data governance policies to enable secure and compliant model development. This paper provides a comprehensive survey of fundamental PPML methods differential privacy, federated learning, homomorphic encryption and examines key data governance frameworks underpinning ethical AI adoption. We analyze technical trade-offs, including privacy-utility balance, scalability, and adversarial resilience. Finally, ongoing research directions and policy implications are discussed, emphasizing interdisciplinary collaboration for trustworthy AI deployment.
The quick progress of 5G networks has allowed for intelligent driving. The primary environment for intelligent driving is provided by vehicular ad hoc networks (VANETs), which relay real-time data and communications between moving vehicles and fixed infrastructure. Since the communication is open-access, the message exchanged is vulnerable to privacy and security attacks. To address with this challenge, several authentication schemes have proposed. Nevertheless, the complexity of current these schemes means that re-authenticating vehicle identities every time they reach a new area of infrastructure coverage significantly hampers the overall network’s efficiency. This paper has proposed a handover authentication, called HAFC scheme based on fog computing to achieve fastly re-authentication of vehicles via secure property transfer among infrastructures (fog servers) for 5G-assisted vehicular blockchain networks. The proposed HAFC scheme consists of both stages namely, initial-authentication stage and handover-authentication stage. In security analysis shows that the proposed HAFC scheme’s vehicle to fog server-for both stages is Computational Diffie-Hellma (CDH)-secure. According to the simulation results, the novel handover authentication stage takes only a fraction of the time required for the first one.
Wilson Valdez, Juan Marcelo Parra-Ullauri, Attila Kertész
The proliferation of Internet of Things (IoT) applications poses formidable challenges in managing data processing, privacy, and security. In response, technologies such as Fog Computing (FC), Blockchain (BC), and Federated Learning (FL) have emerged as promising solutions. Combining these technologies can broaden their scope, and impose novel challenges. This paper conducts a Systematic Literature Review (SLR) to investigate their integration within the IoT domain, systematically evaluating the current state-of-the-art by analyzing 40 papers against 38 extraction criteria, encompassing technical characteristics specific to FC, BC, FL, or their integration. The findings offer insights into the advantages, challenges, opportunities, and limitations of this integration, addressing data processing, privacy, and security concerns in IoT. By filling a research gap and directly examining FC, BC, and FL interoperability across architectural layers, this study contributes to knowledge expansion in the field. This paper proposes a novel framework for implementing FL and BC within FC environments for IoT applications, alongside a comprehensive synthesis of existing literature, distinguishing it from previous research efforts. Furthermore, it offers valuable insights into the current landscape, identifies research needs, and proposes future research directions. The framework and literature synthesis provided allow readers to access customized information on FC-BC-FL integration, aiding in designing and implementing robust IoT solutions.
Due to exponential demand in IoT based healthcare, the demand for robust mechanisms to ensure data privacy, security, and scalability with the increasing dependence on cloud-based healthcare systems is immensely felt. Current approaches to dealing with health-care data in cloud settings lack the potency to tackle challenges emanating from the distribution of non-IID data, dynamic access control requirements, and secure cross-chain data analysis. These methods could not provide a holistic solution to adapt with the heterogeneous nature of healthcare data while maintaining advanced privacy and security levels over the distributed networks. In this way, the present work proposes to offer a secure and scalable protocol that is based on the blockchain for healthcare cloud data samples. It integrates the following four new methodologies: Adaptive Federated Learning for Healthcare Data, Secure Homomorphic Blockchain Encryption, Dynamic Attribute-Based Encryption for Healthcare, and Proof of Healthcare Privacy (PoHP) consensus based cross-chain federated Analytics with Zero Knowledge Protocol (ZKP) for healthcare. AFL-HD would work with optimal model training over the distributed healthcare data and thereby handle the challenges that are non-IID in nature, while reducing the communication overhead by 30-40%. SHBE would ensure a 1.5x improvement in encryption and decryption times and also enable secure computations on encrypted data samples. Thus, DABE-HC enables dynamic access control policy management in blockchains, while ensuring access control precision in excess of 99%, with near-instant policy updating. CCFA-HC supports X-blockchain privacy-preserving analytics, thereby reducing the cross-chain communication overhead by 20-30%. In this protocol, therefore, cloud healthcare data management is made more scalable, secure, and private. It allows tackling challenges in the healthcare domain and gives a holistic solution supporting meaningful and secure, efficient, and collaborative healthcare data processing and analytics across distributed environments. The impact of this work is immense in providing a foundation for the next generation of secure healthcare data systems.
With the development of technology, the connected vehicle has been upgraded from a traditional transport vehicle to an information terminal and energy storage terminal. The data of ICV (intelligent connected vehicles) is the key to organically maximizing their efficiency. However, in the context of increasingly strict global data security supervision and compliance, numerous problems, including complex types of connected vehicle data, poor data collaboration between the IT (information technology) domain and OT (operation technology) domain, different data format standards, lack of shared trust sources, difficulty in ensuring the quality of shared data, lack of data control rights, as well as difficulty in defining data ownership, make vehicle data sharing face a lot of problems, and data islands are widespread. This study proposes FADSF (Fuzzy Anonymous Data Share Frame), an automobile data sharing scheme based on blockchain. The data holder publishes the shared data information and forms the corresponding label storage on the blockchain. The data demander browses the data directory information to select and purchase data assets and verify them. The data demander selects and purchases data assets and verifies them by browsing the data directory information. Meanwhile, this paper designs a data structure Data Discrimination Bloom Filter (DDBF), making complaints about illegal data. When the number of data complaints reaches the threshold, the audit traceability contract is triggered to punish the illegal data publisher, aiming to improve the data quality and maintain a good data sharing ecology. In this paper, based on Ethereum, the above scheme is tested to demonstrate its feasibility, efficiency and security.
Federated Learning (FL) has emerged as a revolutionary machine learning approach, enabling the training of algorithms across decentralized devices or servers while maintaining data privacy. Unlike traditional centralized methods that pool data into a single repository, FL keeps data localized, enhancing the protection of sensitive information and ensuring compliance with privacy standards like GDPR and CCPA. This paradigm shift is particularly relevant in today's data-driven world, where concerns over data breaches and regulatory compliance are paramount. FL allows organizations and individuals to collaboratively train powerful machine learning models without sharing sensitive data. By adopting FL approaches, leveraging distributed data and computing power across different sources while respecting user privacy becomes possible. The architecture of FL involves a central system coordinating updates from multiple sources to improve a global model. Edge devices, such as smartphones or IoT devices, perform local training using their unique datasets. Each edge device trains the model locally, sending only updates (like gradients) to the central server, ensuring sensitive data is never exposed1. Furthermore, privacy-preserving technologies like differential privacy and homomorphic encryption strengthen data confidentiality and compliance with regulations. Differential privacy introduces noise to data or model updates to prevent the reconstruction of individual information, while homomorphic encryption allows computations on encrypted data without decryption1. The rise of 5G networks will significantly enhance FL by reducing latency and improving communication between edge devices and central servers, enabling faster model training and real-time applications. Blockchain technology offers a decentralized and immutable ledger for tracking data usage and model updates, creating a transparent and tamper-proof mechanism, addressing trust issues in federated systems and further strengthening security
Internet of Things (IoT) is currently playing a major role in how intelligent devices are interconnected and deployed to automate services in transport and smart living sectors. However, IoT is facing challenges in terms of data protection and authentication due to the heterogeneous nature of IoT devices that do not exhibit a central authority. It is crucial to provide secure and trustworthy solutions for the increasing demands of decentralized IoT environments. To this end, this research proposes a novel integration of blockchain-technologies in IoT services to enhance security, data integrity, users privacy, system scalability and interoperability of devices. This is done by leveraging smart contracts to enforce authentication, access control and data exchange mechanisms for IoT devices. The proposed approach is verified by the construction and deployment of a smart contract over the Polygon blockchain network in a simulated real-world IoT scenario. The obtained results show that the proposed approach ensures fast and secure authentication in IoT networks by decreasing the risk of unauthorized access and data tampering.