An important challenge facing cloud computing is how to correctly and effectively handle and serve millions of users’ requests. Efficient task scheduling in cloud computing can intuitively affect the resource configuration and operating cost of the entire system. However, task and resource scheduling in a cloud computing environment is an NP‐hard problem. In this paper, we propose a three‐layer scheduling model based on whale‐Gaussian cloud. In the second layer of the model, a whale optimization strategy based on the Gaussian cloud model (GCWOAS2) is used for multiobjective task scheduling in a cloud computing which is to minimize the completion time of the task via effectively utilizing the virtual machine resources and to keep the load balancing of each virtual machine, reducing the operating cost of the system. In the GCWOAS2 strategy, an opposition‐based learning mechanism is first used to initialize the scheduling strategy to generate the optimal scheduling scheme. Then, an adaptive mobility factor is proposed to dynamically expand the search range. The whale optimization algorithm based on the Gaussian cloud model is proposed to enhance the randomness of search. Finally, a multiobjective task scheduling algorithm based on Gaussian whale‐cloud optimization (GCWOA) is presented, so that the entire scheduling strategy can not only expand the search range but also jump out of the local maximum and obtain the global optimal scheduling strategy. Experimental results show that compared with other existing metaheuristic algorithms, our strategy can not only shorten the task completion time but also balance the load of virtual machine resources, and at the same time, it also has a better performance in resource utilization.

The tremendous growth of Internet of Medical Things has led to a surge in medical user data, and medical data publishing can provide users with numerous services. However, neglectfully publishing the data may lead to severe leakage of user’s privacy. In this article, we investigate the problem of data publishing in Internet of Medical Things with privacy preservation. We present a novel system model for Internet of Medical Things user data publishing which adopts the proposed multiple partition differential privacy k-medoids clustering algorithm for data clustering analysis to ensure the security of user data. Particularly, we propose a multiple partition differential privacy k-medoids clustering algorithm based on differential privacy in data publishing. Based on the traditional k-medoids clustering, multiple partition differential privacy k-medoids clustering algorithm optimizes the randomness of selecting initial center points and adds Laplace noise to the clustering process to improve data availability while protecting user’s privacy information. Comprehensive analysis and simulations demonstrate that our method can not only meet the requirements of differential privacy but also retain the better availability of data clustering.

The Internet of Things (IoT) connects billions of physical devices around the world to the Internet to collect and share massive data. Location privacy leakage has received considerable attention in the field of security. In order to implement the -anonymity location privacy protection mechanism, a previous work constructed an anonymous location set by retrieving historical request record database from the trusted third anonymous server, which advantageously protects user’s location privacy. However, the performance of location-based services (LBSs) is weakened by the time overhead of continual retrieving database. Moreover, with the increasingly flourishing of the positioning technique, user location can be accurate to the user’s altitude beyond the two-dimensional latitude and longitude coordinates. In this paper, we present a location privacy protection strategy in LBSs based on Alt-Geohash coding (LPP-AGC) method synthetically considering the altitude of user location and time overhead. Specifically, we give the Alt-Geohash coding algorithm (AGCA) to retrieve the historical request record database, which greatly reduces the time overhead and ensures the immediacy of LBSs. In addition, we propose the dummy location generating algorithm (DLGA) and location filtering algorithm (LFA) to provide users with autonomous -anonymity location privacy protection. Extensive simulations are performed to verify the performance and security of the proposed strategy.

Currently, due to the different distribution of data for each user, many personalized federated learning (PFL) methods have emerged to meet the personalized needs of different users. However, existing methods have two problems: 1) in the aggregation process, the contribution between the internal layers of the client model is not considered and 2) it is difficult to match the quantitative weight information of both user privacy protection and performance with their qualitative preferences during the training process. Therefore, we first propose a framework for federated aggregation with interlayer personalized contribution named FedIPC, which completes model aggregation based on the contribution of internal layers and improves client model performance. Based on the above framework, we design a multiobjective federated optimization method based on adaptive preference indicators named FedAPI-nondominated sorting genetic algorithm II (NSGA-II). This method can match quantitative weight with qualitative user preferences and adaptively select for Pareto optimal solutions during the optimization process. Extensive experiments on two image datasets and a tabular dataset show that our proposed method not only accelerates model convergence, but also achieves good improvements in model performance. In addition, our proposed method can accurately match the qualitative preferences of users, balancing the performance of the model and privacy protection based on preferences.

With the advancement of Internet technologies, fraudsters increasingly disguise their behavior to evade model detection, introducing heterophily that poses serious challenges to fraud detection systems and leads to substantial economic losses. Although graph neural networks (GNNs) have recently achieved notable success in fraud detection, most existing models overlook resource efficiency when addressing the heterophily problem. Therefore, it is of practical importance to design fraud detection models that are both effective and efficient. In this article, we propose subgraph-level graph neural network (SURE-GNN), a model that jointly considers classification effectiveness and resource efficiency in fraud detection tasks. Specifically, we design a heterophily-based graph partitioning method to construct subgraphs, which improves training performance by reducing the computational load. To further enhance classification performance, we design a spectral–spatial fusion mechanism that leverages both global structural signals and local neighborhood features within subgraphs. Experiments on one real-world transaction dataset and three public fraud detection datasets demonstrate that SURE-GNN outperforms existing baselines, achieving superior classification performance while significantly reducing training time and memory usage.