Equipped with mobile devices, people relied on location-based services (LBSs) can expediently and reasonably organize their activities. But location information may disclose people’s sensitive information, such as interests and health status. Besides, the limited resources of mobile devices restrict the further development of LBSs. In this article, aiming at the fair meeting position determination service, we design a lightweight privacy-preserving solution. In our scheme, mobile users only need to submit service requests. A cloud server and a location services provider are responsible for service response, where the cloud server achieves most of the calculation, and the location services provider determines the fair meeting location based on the computational results of the cloud server and broadcasts it to mobile users. The proposed scheme adopts homomorphic encryptions and random permutation methods to preserve the location privacy of mobile users. The security analyses show that the proposed scheme is privacy preserving under our defined threat models. Besides, the presented solution only needs to calculate $n$ Euclidean distances, and hence, our scheme has linear computation and communication complexity.

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A Lightweight Privacy-Preserving Fair Meeting Location Determination Scheme

Location-based services, such as DiDi and bike sharing, are becoming increasingly popular. However, the use of these services raises privacy concerns. In the past few years, differential privacy technology has been applied to location information protection. However, most existing models largely fail to resist complex background-knowledge attacks. This paper proposes a novel privacy preservation method for trajectory data. It is based on a prefix tree and uses differential privacy. It should be noted that existing methods only consider either a certain location point or the entire trajectory. In the proposed prefix tree structure, the nodes of the tree store the trajectory segments. The parameter minimum description length method is combined with the Dijkstra method to select feature trajectory points that represent the entire trajectory, thus further reducing the (computational) complexity of data processing. To protect privacy, Laplacian noise is added to the location data of trajectory segments by using differential privacy. In addition, a background and contextual information attack model is proposed, and the corresponding protection method is provided. Finally, a Markov chain is used to limit the size of the noise added to the data. The proposed algorithm is compared with related state-of-the-art algorithms on a public dataset. The results demonstrate that our algorithm can ensure not only privacy but also data availability.

Novel trajectory privacy-preserving method based on prefix tree using differential privacy

A secure energy trading system for electric vehicles in smart communities using blockchain

This editorial introduces the special issue entitled “Applications of Internet of Things”, of ISPRS International Journal of Geo-Information. Topics covered in this issue include three main parts: (I) intelligent transportation systems (ITS), (II) location-based services (LBS), and (III) sensing techniques and applications. Three papers on ITS are as follows: (1) “Vehicle positioning and speed estimation based on cellular network signals for urban roads,” by Lai and Kuo; (2) “A method for traffic congestion clustering judgment based on grey relational analysis,” by Zhang et al.; and (3) “Smartphone-based pedestrian’s avoidance behavior recognition towards opportunistic road anomaly detection,” by Ishikawa and Fujinami. Three papers on LBS are as follows: (1) “A high-efficiency method of mobile positioning based on commercial vehicle operation data,” by Chen et al.; (2) “Efficient location privacy-preserving k-anonymity method based on the credible chain,” by Wang et al.; and (3) “Proximity-based asynchronous messaging platform for location-based Internet of things service,” by gon Jo et al. Two papers on sensing techniques and applications are as follows: (1) “Detection of electronic anklet wearers’ groupings throughout telematics monitoring,” by Machado et al.; and (2) “Camera coverage estimation based on multistage grid subdivision,” by Wang et al.

https://www.mdpi.com/2220-9964/7/9/334/pdf

Applications of Internet of Things

The popularity of the modern smart devices and mobile social networks (MSNs) brings mobile users better experiences and services by taking advantage of location-aware capabilities. Location sharing, as an important function of MSNs, has attracted attention with growing popularity. While the users get great benefits and conveniences from MSNs, they also have high concerns about the privacy of location. However, in the existing solution, the privacy of users can hardly be guaranteed without the assumption of full trust in the service provider (SP), and few previous research studies have discussed the individual requirement of mobile users in MSNs. In this paper, we propose a user-defined location-sharing scheme (ULSS) to achieve enhanced privacy preservation under different contexts. We present a coarse-grained proximity detection method and a lightweight order-preserving encryption- (OPE-) based method to provide the users with flexible privacy preservation at different privacy levels. The proposed scheme preserves user’s location privacy with respect to SP, friends, and other adversaries, getting rid of the introduction of fully trusted party (TTP). Extensive experiments were conducted to verify the effectiveness and efficiency of our proposed scheme.

/pdf/a-user-defined-location-sharing-scheme-with-efficiency-and-2z540jqllx.pdf

A User-Defined Location-Sharing Scheme with Efficiency and Privacy in Mobile Social Networks

Currently, although prevalent location privacy methods based on k-anonymizing spatial regions (K-ASRs) can achieve privacy protection by sacrificing the quality of service (QoS), users cannot obtain accurate query results To address this problem, it proposes a new location privacy-preserving k-anonymity method based on the credible chain with two major features First, the optimal k value for the current user is determined according to the user’s environment and social attributes Second, rather than forming an anonymizing spatial region (ASR), the trusted third party (TTP) generates a fake trajectory that contains k location nodes based on properties of the credible chain In addition, location-based services (LBS) queries are conducted based on the trajectory, and privacy level is evaluated by instancing θ privacy Simulation results and experimental analysis demonstrate the effectiveness and availability of the proposed method Compared with methods based on ASR, the proposed method guarantees 100% QoS

https://www.mdpi.com/2220-9964/6/6/163/pdf

Min Wu

Papers

Efficient Location Privacy-Preserving k-Anonymity Method Based on the Credible Chain