Cross-border data privacy protection often involves personal privacy data from different regions, where cross-border vehicle identity authentication requires a large amount of sensitive data. The cross-border movement of this sensitive data poses a significant threat to privacy. A distributed identity management blockchain model for cross-border data privacy protection is proposed to avoid the cross-border transmission of sensitive data through identity authentication. The model combines the SM2 and SM9 algorithms and blockchain technology to guarantee the security of stored data while providing a method to avoid sensitive data crossing borders and realizing cross-border identity authentication. The model was originally designed for the Northbound Travel for Macao scenario but can still be applied to other cross-border authentications. The generation speed of a Non-Fungible Token is verified through experiments, and the generation time and efficiency of Non-Fungible Tokens satisfy the actual needs of Internet of Vehicles authentication.
Abstract The precision of insulin dosage is essential in the process of diabetes treatment. The fact is providing precise dosage is almost impossible for clinicians since blood sugar levels are dynamically affected by many factors. Even though some auxiliary dosing systems have been proposed, the required real‐time physical data about the health situation of diabetics is still hard to synchronize to the end‐devices instantly. The traditional personalized drug delivery frameworks for accurate dosing of insulin always collect and transmit medical data in cleartext, which raises privacy problems. In this article, we propose a framework for an optimized insulin dosage via privacy‐preserving reinforcement learning to diabetics (OIDPR). In OIDPR, both the additive secret sharing and edge computing are deployed to achieve data confidentiality and performance optimization. The medical data is divided into multiple secret shares uniformly at random for outsourcing and operating at the edge servers. During the computation task of reinforcement learning, data is encrypted and processed via our proposed additive secret sharing protocol, where the privacy is reserved by the efficient encryption mechanism and the secret sharing system only incurs little workload. We provide comprehensive theoretical analyses and experimental results that demonstrate the supervisor functionality and high performance of our framework.
In cloud‐assisted electronic health care (eHealth) systems, a patient can enforce access control on his/her personal health information (PHI) in a cryptographic way by employing ciphertext‐policy attribute‐based encryption (CP‐ABE) mechanism. There are two features worthy of consideration in real eHealth applications. On the one hand, although the outsourced decryption technique can significantly reduce the decryption cost of a physician, the correctness of the returned result should be guaranteed. On the other hand, the malicious physician who leaks the private key intentionally should be caught. Existing systems mostly aim to provide only one of the above properties. In this work, we present a verifiable and traceable CP‐ABE scheme (VTCP‐ABE) in eHealth cloud, which simultaneously supports the properties of verifiable outsourced decryption and white‐box traceability without compromising the physician’s identity privacy. An authorized physician can obtain an ElGamal‐type partial decrypted ciphertext (PDC) element of original ciphertext from the eHealth cloud decryption server (CDS) and then verify the correctness of returned PDC. Moreover, the illegal behaviour of malicious physician can be precisely (white‐box) traced. We further exploit a delegation method to help the resource‐limited physician authorize someone else to interact with the CDS. The formal security proof and extensive simulations illustrate that our VTCP‐ABE scheme is secure, efficient, and practical.
In the mobile crowdsensing (MCS) with large-scale data collection and sharing environments, since a growing number of applications need to exploit multisource sensing information, it is almost indispensable to develop a generic mechanism supporting efficient and accurate multiple tasks allocation. Meanwhile, achieving the maximum service benefit, the cloud server allocates the multitask based on the user attribute preferences, but it will lead to the privacy leakage of sensing users (SUs). Motivated by the aforementioned challenges, we propose a privacy-preserving multitask allocation (PMTA) scheme for MCS in this article. Specifically, we exploit $K$ -means clustering and matrix multiplication to realize a secure and efficient grouping mechanism, which achieves the selection of high-quality and accurate target users set with privacy preserving. Based on the short group signature algorithm and 0–1 encoding technique, we construct a privacy-preserving matching mechanism to guarantee the anonymous authentication and achieve the matching for task requirements and user reputation levels in a privacy-preserving way. Finally, we give a security analysis, and we evaluate the computational costs and communication overhead, and the experimental result shows the efficiency of our proposed PMTA scheme.
Traditional collaborative filtering algorithms use user history rating information to predict movie ratings Other information, such as plot and director, which could provide potential connections are not fully mined. To address this issue, a collaborative filtering recommendation algorithm named a movie recommendation method based on knowledge graph and time series is proposed, in which the knowledge graph and time series features are effectively integrated. Firstly, the knowledge graph gains a deep relationship between users and movies. Secondly, the time series could extract user features and then calculates user similarity. Finally, collaborative filtering of ratings can calculate the user similarity and predicts ratings more precisely by utilizing the first two phases’ outcomes. The experiment results show that the A Movie Recommendation Method Fusing Knowledge Graph and Time Series can reduce the MAE and RMSE of user-based collaborative filtering and Item-based collaborative filtering by 0.06,0.1 and 0.07,0.09 respectively, and also enhance the interpretability of the model.
As an emerging concept in the intelligent transportation system (ITS), Opportunistic Autonomous Vehicle Platoon (OAVP) enables autonomous vehicles to self-organize a temporary platoon and travel together. Whereas, electing the platoon leader is a crucial issue to be solved in this scenario. The platoon leader not only suffers from more wind resistance but also spends more computation resources in dealing with tremendous information obtained from the surrounding environment. Hence, vehicles tend to be a follower rather than a leader. In this paper, we propose a reputation-based leader election system for OAVP named RLE, which contains two sub-systems: leader election and incentive mechanism. In the former one, a reputation-based election scheme is first constructed to elect a relatively trustful leader according to the reputation value recorded on the blockchain. The proposed scheme integrates previous experience with recommendations from other members. In the second sub-system, an incentive mechanism is designed to stimulate platoon members to participate actively in the process of election, which is based on the recorded real-time fuel economy among vehicles participating in a platoon. Security analysis shows that the system is sufficient to deal with potential security threats. Experimental results based on the simulated platform demonstrate the practicality and feasibility of our solution.
Federated learning is a popular framework designed to perform the distributed machine learning while protecting client privacy. However, the heterogeneous data distribution in real-world environments makes it difficult to converge when performing model training. In this article, we propose the federated gradient scheduling (FedGS), an improved historical gradient sampling utilization method for optimizers that utilize historical gradients in the federated learning to alleviate the instability problem of historical gradient information due to non-IID. FedGS consists of two main steps to improve federated learning performance. First, clustering uses clients' label distributions, which relabel clients and their submitting gradients. Second, sampling gradient clusters to generate an IID gradient set, which feeds to optimizers to derive valid momentum information. Besides, we introduce differential privacy to collaborate with FedGS to enhance clients' privacy protection strength. Compared to previous non-IID federated learning solutions, our method can achieve greater resistance to temporal non-IID. Moreover, experiments show that FedGS can achieve faster convergence and performance gain of up to 10% over existing state-to-art methods in some scenarios. FedGS can combine with existing methods easily to achieve better performance. We further verify that our method has robust performance gains in different non-IID scenarios, demonstrating the adaptability of FedGS for different scenarios.
In smart city contexts, traditional methods for semantic segmentation are affected by adverse conditions, such as rain, fog, or darkness. One challenge is the limited availability of semantic segmentation datasets, specifically for autonomous driving in adverse conditions, and the high cost of labeling such datasets. To address this problem, unsupervised domain adaptation (UDA) is commonly employed. In UDA, the source domain contains data from good weather conditions, while the target domain contains data from adverse weather conditions. The Adverse Conditions Dataset with Correspondences (ACDC) provides reference images taken at different times but in the same location, which can serve as an intermediate domain, offering additional semantic information. In this study, we introduce a method that leverages both the intermediate domain and frequency information to improve semantic segmentation in smart city environments. Specifically, we extract the region with the largest difference in standard deviation and entropy values from the reference image as the intermediate domain. Secondly, we introduce the Fourier Exponential Decreasing Sampling (FEDS) algorithm to facilitate more reasonable learning of frequency domain information. Finally, we design an efficient decoder network that outperforms the DAFormer network by reducing network parameters by 28.00%. When compared to the DAFormer work, our proposed approach demonstrates significant performance improvements, increasing by 6.77%, 5.34%, 6.36%, and 5.93% in mean Intersection over Union (mIoU) for Cityscapes to ACDC night, foggy, rainy, and snowy, respectively.
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