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In the era of edge computing, real-time data preprocessing on the edge node has the potential to improve computational efficiency and data accuracy. However, a significant challenge is private data disclosure, particularly in the case of location-based services. To address this challenge, in this paper, by leveraging differential privacy, we propose a privacy-aware framework for mobile edge computing called MEPA to protect the location privacy in which the edge node is regarded as an anonymous central server. The proposed framework can provide computing services without deploying special infrastructure. To be specific, in order to solve the problem of constrained computing resources in the edge nodes, the algorithm of Quadtree Differential Privacy based on Hilbert curve division (QTDP-H) two-dimensional spatial data query transmission is proposed.First, a noise quadtree is established and the privacy budget is divided according to the tree level.Then, the constructed quadtree is represented by quanternary, so that the partition based on Hilbert curve can be established and the two-dimensional data in the area can be converted into one-dimensional, which can greatly improve the retrieval efficiency. The effectiveness of the proposed algorithm in terms of time complexity and retrieval accuracy has been verified by extensive experimental results. Compared with traditional methods of (D, ) − LP, the average runtime can be reduced by 15%-20%, and the average relative error is reduced by 20%. KEYWORDS differential privacy, Hilbert curve, location-based service, mobile edge computing, privacy aware, quadtree INTRODUCTIONWith the development of the Internet of Things (IoT) and cloud computing, the amount of data on the edge network is rapidly growing. Therefore, it is more efficient to process the data at the edge of the network. However, the development of network bandwidth is slower compared with the powerful computing ability of cloud services. The amount of data is growing rapidly, and time consumption in data transmission has become the main challenge that restricts the cloud computing applications. In cloud computing model, the devices at the edge often only act as consumers; however, people often generate data from the devices they use. 1 This shift from data consumers to data consumers/producers requires more functionality on the edge node. However, at the edge of the network, user privacy and data security are among the most important requirements. If IoT is deployed in the home, some privacy information can be obtained from the user data, such as by reading user electric meter and water meter data to determine whether there are people in the room. By obtaining location data, people can estimate someone's home address, lifestyle, social relationships, and more. Therefore, the disclosure of this personal information to attackers can pose a serious threat to the privacy of users. The development of edge computing will promote a variety of intelligent applications, which were impractical in the past due to network ...

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Summary With the development of smart Internet of things devices, intelligent applications are expected to lead further innovation in smart city. However, although cloud computing infrastructure can be used to meet traditional challenges, the scheduling model for new big data intelligent application has still not matured. In this work, we proposed a two‐stage scheduling framework for smart city intelligent application. In the first stage, we propose a virtual machine selection algorithm for edge computing to enhance relative migration benefits. The algorithm defines the invalid virtual machine migration and relative migration benefits from the change in the overall computing resources of the cloud data center after the virtual machine migration. In the second stage, we proposed an energy efficient and resource‐constrained scheduling framework for edge computing. The historical data of the cloud and edge computing workload of the computing node are processed in a sliding window manner, and the median absolute deviation of the historical data is used as the base of the physical reserved resource constraint when the base also changes as the workload changes. The experimental results show that energy‐efficient and resource‐constrained can make the computer resource provide high‐quality services for users in a low‐energy state.

With the development of the Internet of Things (IoT), the delay caused by network transmission has led to low data processing efficiency. The emergence of edge computing can effectively reduce the delay of data transmission and improve data processing capacity. However, at the same time, the IoT faces important challenges, and edge computing uses a large number of distributed devices, making it difficult to perform centralized control. When an edge node is attacked, the attacker can continue to invade its connected nodes, thereby mining and stealing a user's private data and causing losses. Once the edge layer communication link is attacked or accidentally interrupted, the user's private information is likely to be leaked. To solve these problems, this paper proposes to protect user privacy by using differential privacy. First, according to the three-layer communication link structure of edge computing, a data query model is proposed; the main function is to capture the structure information and the data center connection weight and to query the connection relationship between the edge node and the client. Second, the edge node is regarded as the central server, and the differential privacy theory is used to realize the protection of location privacy. Finally, to reduce the data loss caused in the process of location protection, linear programming is adopted to realize the selection of the optimal location fuzzy matrix, and data loss and reconstruction methods are used to minimize the data uncertainty. In comparison to the existing differential privacy method, the method in this paper can achieve better privacy protection and can effectively reduce data loss.

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