Abstract
Location-Based Services (LBS) have recently gained much attention from the research community due to the openness of wireless networks and the daily development of mobile devices. However, using LBS is not risk free. Location privacy protection is a major issue that concerns users. Since users utilize their real location to get the benefits of the LBS, this gives an attacker the chance to track their real location and collect sensitive and personal information about the user. If the attacker is the LBS server itself, privacy issues may reach dangerous levels because all information related to the user's activities are stored and accessible on the LBS server. In this paper, we propose a novel location privacy protection method called the Safe Cycle-Based Approach (SCBA). Specifically, the SCBA ensures location privacy by generating strong dummy locations that are far away from each other and belong to different sub-areas at the same time. This ensures robustness against advanced inference attacks such as location homogeneity attacks and semantic location attacks. To achieve location privacy protection, as well as high performance, we integrate the SCBA approach with a cache. The key performance enhancement is storing the responses of historical queries to answer future ones using a bloom filter-based search technique. Compared to well-known approaches, namely the ReDS, RaDS, and HMC approaches, experimental results showed that the proposed SCBA approach produces better outputs in terms of privacy protection level, robustness against inference attacks, communication cost, cache hit ratio, and response time.
Highlights
The world has witnessed the birth of what is called the Internet of Things (IoT) [1, 2, 3], in which scientists have moved towards smart cities and smart systems that are supported by smart Location-Based Services (LBS) [4, 5]
It is worth mentioning that a high value of Cache Hit Ratio (CHR) means that most of queries are answered by the cache, which in turn leads to a high performance
Safe Side (SS)-based discussion: Under the threat of a location homogeneity attack, the increased number of LBS users in a step equals 50 and fixing the threshold of ENT to be 4 with k=6, we evaluate the resistance of the three approaches
Summary
The world has witnessed the birth of what is called the Internet of Things (IoT) [1, 2, 3], in which scientists have moved towards smart cities and smart systems that are supported by smart Location-Based Services (LBS) [4, 5]. We address the privacy protection of the LBS user by protecting the real location against the LBS server. The research questions are: How to ensure the privacy protection of the LBS user by protecting the real location [7, 8, 9]?. The contribution of this paper is as follows: In responding to the first research question, we propose a novel dummy-based approach to protect the location privacy of LBS users. Depending on the query probability, our proposed approach selects (or generates) dummy locations that ensure the highest privacy protection level according to an entropy privacy metric. In responding to the second research question, in terms of generating strong dummy locations, the proposed approach creates defenses against both the location homogeneity attack and the semantic location attack based on a safe cycle.
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