With the rapid advancement and application of the internet of Medical Things (IoMT), personal health records (PHRs) are now increasingly comprised of data collected by internet of Things (IoT) devices and medical reco...
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ISBN:
(数字)9798331506209
ISBN:
(纸本)9798331506216
With the rapid advancement and application of the internet of Medical Things (IoMT), personal health records (PHRs) are now increasingly comprised of data collected by internet of Things (IoT) devices and medical records documented by healthcare professionals. Personal health record (PHR) sharing demonstrates great potential in improving the accuracy of disease diagnosis. However, PHR sharing also brings risks such as illegal access and personal information leakage. Some works explored using blockchain or attribute-based encryption (ABE) to solve these privacy leakage problems, but those solutions did not pay attention to the user’s attribute privacy. In this work, we combine a linear secret sharing scheme (LSSS) and zero-knowledge succinct non-interactive argument of knowledge (zkSNARK) scheme to design an efficient zero-knowledge proof protocol called zk-AHSNARK. It can verify the user’s attribute permissions while also hiding attribute information. Based on zk-AHSNARK, we propose a novel PHR sharing scheme that protects attribute privacy. Data security is ensured by storing encrypted data in the interplanetary file system (IPFS). In addition, we introduce keyword ciphertext search to achieve fast data retrieval, and we implement the search and verification algorithms via a smart contract, ensuring the trustworthiness and integrity of the execution. Finally, through a large number of simulations, we demonstrated the suggested scheme’s viability and security.
With the increasing intelligence of power IoT terminal devices, the massive volume of data transmission, and the widespread adoption of shared services, traditional terminal boundary security mechanisms are unable to ...
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ISBN:
(数字)9798350391367
ISBN:
(纸本)9798350391374
With the increasing intelligence of power IoT terminal devices, the massive volume of data transmission, and the widespread adoption of shared services, traditional terminal boundary security mechanisms are unable to keep up with the rapid development of network technologies and applications. Additionally, they fail to trace and source network attacks, leading to frequent organized and purposeful malicious attack incidents. To address these issues, this paper proposes a research method for risk modeling and traceability of boundary attacks on power IoT terminals based on complex networks. First, based on the node information of power IoT terminal devices and complex network theory, a terminal boundary attack network (TBAN) is established. Next, using indicators related to complex network theory, such as degree, in-degree, and out-degree, the terminal boundary attack network is analyzed. Furthermore, a staged TBAN is proposed to analyze the attack risks at different times. Then, combining causal theory, a method for tracing and sourcing terminal boundary attacks based on a causal Bayesian network is proposed to identify and trace malicious attacks. Finally, validation and testing analysis are conducted using the actual deployment of power IoT terminal devices. The proposed analysis method transforms the spatial characteristics of the power IoT terminal boundary into a temporally sequential TBAN, which not only visually reflects attack risks but also reveals the temporal relationships of terminal boundary attacks between lines.
In recent years, Electric Vehicles (EVs) have emerged as a sustainable alternative to internal combustion vehicles, noted for better efficiency, lower operational costs, and reduced carbon emissions. However, with the...
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ISBN:
(数字)9798350387414
ISBN:
(纸本)9798350387421
In recent years, Electric Vehicles (EVs) have emerged as a sustainable alternative to internal combustion vehicles, noted for better efficiency, lower operational costs, and reduced carbon emissions. However, with the growing adoption of EVs and limited charging infrastructure, challenges such as charging congestion arise. Traditional plug-in and in-Parking Vehicle-to-Vehicle (V2V) charging modes, constrained by fixed charging locations, lack flexibility and necessitate long charging times. Therefore, this paper introduces a novel in-Motion V2V charging mode, termed V2V (M) mode, allowing an EV as an energy Provider (EV-P) and an EV as an energy Consumer (EV-C) to form a V2V charging Pair (V2V-Pair). Then, the V2V-Pair can transfer energy via wireless V2V charging service while on-the-move. In this paper, the proposed V2V (M) management framework employs a Path Proximity-based V2V Pair matching algorithm and spatio-temporal cooperative path planning, to enhance charging efficiency and reduce charging trip duration. The urban environment simulation results demonstrate marked improvements of the proposed V2V (M) mode. It shorters the charging trip duration and enhances charging service efficiency, offering a viable solution to current EV charging constraints.
Federated Learning (FL) has emerged as a promising paradigm for training machine learning models across distributed devices while preserving their data privacy. However, the robustness of FL models against adversarial...
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Federated Learning (FL) has emerged as a promising paradigm for training machine learning models across distributed devices while preserving their data privacy. However, the robustness of FL models against adversarial data and model attacks, noisy updates, and label-flipped data issues remain a critical concern. In this paper, we present a systematic literature review using the PRISMA framework to comprehensively analyze existing research on robust FL. Through a rigorous selection process using six key databases (ACM Digital Library, IEEE Xplore, ScienceDirect, Springer, Web of Science, and Scopus), we identify and categorize 244 studies into eight themes of ensuring robustness in FL: objective regularization, optimizer modification, differential privacy employment, additional dataset requirement and decentralization orchestration, manifold, client selection, new aggregation algorithms, and aggregation hyperparameter tuning. We synthesize the findings from these themes, highlighting the various approaches and their potential gaps proposed to enhance the robustness of FL models. Furthermore, we discuss future research directions, focusing on the potential of hybrid approaches, ensemble techniques, and adaptive mechanisms for addressing the challenges associated with robust FL. This review not only provides a comprehensive overview of the state-of-the-art in robust FL but also serves as a roadmap for researchers and practitioners seeking to advance the field and develop more robust and resilient FL systems.
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