Abstract
Location-based services (LBS) form the main part of the Internet of Things (IoT) and have received a significant amount of attention from the research community as well as application users due to the popularity of wireless devices and the daily growth in users. However, there are several risks associated with the use of LBS-enabled applications, as users are forced to send their queries based on their real-time and actual location. Attacks could be applied by the LBS server itself or by its maintainer, which consequently may lead to more serious issues such as the theft of sensitive and personal information about LBS users. Due to this fact, complete privacy protection (location and query privacy protection) is a critical problem. Collaborative (cache-based) approaches are used to prevent the LBS application users from connecting to the LBS server (malicious parties). However, no robust trust approaches have been provided to design a trusted third party (TTP), which prevents LBS users from acting as an attacker. This paper proposed a symbiotic relationship-based leader approach to guarantee complete privacy protection for users of LBS-enabled applications. Specifically, it introduced the mutual benefit underlying the symbiotic relationship, dummies, and caching concepts to avoid dealing with untrusted LBS servers and achieve complete privacy protection. In addition, the paper proposed a new privacy metric to predict the closeness of the attacker to the moment of her actual attack launch. Compared to three well-known approaches, namely enhanced dummy location selection (enhanced-DLS), hiding in a mobile crowd, and caching-aware dummy selection algorithm (enhanced-CaDSA), our experimental results showed better performance in terms of communication cost, resistance against inferences attacks, and cache hit ratio.
Highlights
Under swift and mesmerizing developments in the world of technology and Internet networking, the commercial success of mobile devices, lives of people have become easier and more enjoyable
Focusing on cache-based approaches, a leader can decrease the connecting numbers to the Location-based services (LBS) server and optimize the quality of the RoQ stored in the cache at the same time
The cache is represented through a data base consisting of one table only, where the information about points of interests (POI), included in the cells, is stored through the queries that are answered by the LBS server
Summary
Under swift and mesmerizing developments in the world of technology and Internet networking, the commercial success of mobile devices, lives of people have become easier and more enjoyable. Location-based services (LBS) form a main part of the Internet of Things (IoT) [1,2,3,4,5,6], where a wide spectrum of IoT applications relies on LBS, including smart cars, wearable devices (smart watches, sleep tracker bracelets, clothes, etc.), and reward-based LBS applications [7,8,9]. A further advantage of LBS is enabling people to search for points of interests (POI) such as nearby restaurants, hotels, hospitals, and sport clubs. Geo-Inf. 2020, 9, 408 to search for points of interests (POI) such as nearby restaurants, hotels, hospitals, and sport clubs
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