The burgeoning technology of Mobile Edge Computing is attracting the traditional LBS and LS to deploy due to its nature characters such as low latency and location awareness. Although this transplant will avoid the location privacy threat from the central cloud provider, there still exists the privacy concerns in the LS of MEC scenario. Location privacy threat arises during the procedure of the fingerprint localization, and the previous studies on location privacy are ineffective because of the different threat model and information semantic. To address the location privacy in MEC environment, we designed LoPEC, a novel and effective scheme for protecting location privacy for the MEC devices. By the proper model of the RAN access points, we proposed the noise-addition method for the fingerprint data, and successfully induce the attacker from recognizing the real location. Our evaluation proves that LoPEC effectively prevents the attacker from obtaining the user's location precisely in both single-point and trajectory scenarios.
Mobile Edge Computing (MEC) is a new technology which is currently being standardized in an ETSI Industry Specification Group (ISG) of the same name. Mobile Edge Computing provides an IT service environment and cloud-computing capabilities at the edge of the mobile network, within the Radio Access Network (RAN) and near mobile subscribers. The aim is to reduce latency, ensure highly efficient network operation and service delivery, and offer an improved user experience.The growth of mobile traffic and pressure on costs are driving a need to implement several changes in order to maintain quality of experience, the Internet of Things (IoT) is further congesting the network and network operators need to do local analysis to ease security and backhaul impacts [1].
Among all of the pervasive mobile and cloud-based services, the location-based service (LBS) and the localization service (LS) are the most suitable services for the decentralized deployment of the MEC scenario. On on hand, MEC is characterized by low latency, proximity and location awareness [2], these features of MEC are naturally fit with LBS and LS. On the other hand, in the vast majority of instances, the LBS and LS can be provided inside each subarea of MEC community independently, since most of the location-based information (e.g. the kNN queries, POI in proximity) are contained in the MEC scenario, data could be collected and processed based on location without being transported to cloud, this will block the LBS and LS provider from getting the location of user, which will preserve the location privacy better.
However, from the view of privacy preservation, in MEC scenario, the concern on the location privacy still exists. Although the location is avoided from being sent to the centralized cloud, the different threat model in MEC scenario is, to some extent, threatening the location privacy. As shown in Fig. 1, two main factors are involved in the MEC scenario.
First, in the traditional threat model of centralized LBS service, the privacy concerns are originated from the geolocation information, and the preservation efforts are focusing on preventing the LBS providers from knowing the user’s accurate locations while at the same time retaining the LBS functionality and service quality. However, in MEC scenario, such threat model is inapplicable, since the location awareness MEC is capable of generating location from the wireless signal space “fingerprint”. In MEC scenario, this fingerprint is equivalent to the location from the view of privacy-preserving, and the fingerprint of user needs to be protected.
Second, the basis of the MEC infrastructure is constructed by the edge smart devices with limited computational power and with well-known lacking of security. As a result, although the centralized LBS providers which are considered to be untrusted (or curiousbut-honest) are excluded, the privacy concerns due to the weak security of infrastructure is still severe.
In conclusion, we argue that the location privacy preservation in the MEC scenario should be performed in the very beginning of the generation of locations. The fingerprint information needs to be protected since it is equivalent to the location in MEC scenario. However, most of the state-of-the-art research on location privacy protection focuses on solving the privacy threat against LBS providers by investigating how to use the location safely or in a privacy-preserving way, they are incapable for the MEC scenario since the semantic content of wireless signal space fingerprint is different from the geolocation coordinates.
In this paper, we investigated the location privacy preservation in MEC scenario, and proposed a noise addition-based scheme named LoPEC to protect the fingerprint information of user. Specifically, we introduce the fundamental topology model of the MEC wireless infrastructures, based on this model, we designed a method to generate the “noise fingerprint”. Then, the noise addition scheme was given to protect fingerprint of the user. We consider the trajectory privacy and propose an enhanced algorithm, which can further generate trajectory-like noise fingerprints when using continuous location updates. To realize our scheme on smart devices directly without any additional system architectures, we propose a mechanism for the daily collection of a noise fingerprint candidate set. This mechanism also greatly enlarges the selectable range of noise fingerprint and enables the user to generate noise fingerprint beyond his current sensing range in real time.
Fig. 1 LBS and LS in MEC scenario. Our approach has no negative impact on the functionality of the upper LBSs. The evaluation we implemented on Android device verified the effectiveness while maintaining a reasonable time cost for today’s devices.
This paper makes the following main contributions.
(1) We propose a method for adding noise that can confuse the potential attackers and prevent it from recognizing
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