Differential privacy–based location privacy enhancing in edge computing

Abstract

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 ...

Recent advances in cloud data centers toward fog data centers

“…Another work describes the provision of computing services without deploying appropriate infrastructure. In this architecture, the authors solve the problem of constrained computing resources at the edge nodes, and present an algorithm to tackle two‐dimensional spatial data query transmission between the edge nodes. They also present statistical data on moving objects, satisfying the differential privacy preserving model and guaranteeing that the count query of the moving object's position data satisfies differential privacy.…”

Data Loss and Reconstruction of Location Differential Privacy Protection Based on Edge Computing

“…However, considering the limitations of the geo-indistinguishable mechanism, the following premises are required for the inference of a relatively complete perceptual data matrix: (1) the users are uniformly distributed in the region; in other words, the probability that each user appears in the region is equal. Assuming that there are four regions, the probability that region 4 is mapped to another region is equal, p [4,1] = p [4,2] = p [4,3] = p [4,4] = 0; (2) the data reported by the user is accurate.…”

“…The amount of data is growing rapidly, and the time consumed in the data transmission has become the main challenge that restricts the cloud computing applications. In the cloud computing model, the devices at the edge often only act as consumers; however, people often generate data from the devices that they use [1]. This shift from data consumers to data consumers/producers requires more functionality on the edge node.…”

“…At present, the privacy protection for location data is mostly based on the k-anonymity model, which has the following disadvantages: (1) The k-anonymous model can reduce usability when it protects relatively sparse data. (2) The method based on the k-anonymous model needs to know which background knowledge the attacker possesses to design the corresponding privacy protection algorithm, which cannot guarantee privacy protection if the background knowledge is unknown.…”

On Differential Privacy-Based Framework for Enhancing User Data Privacy in Mobile Edge Computing Environment

“…In case the communication link between the user and the edge is broken or hacked, then the privacy for personal information is not assured. Due to these reasons, DP is also incorporated into edge computing where the security risk is large [11]- [13]. The existence of common challenges in the provisioning of DP here is,…”