Abstract
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.
Highlights
As one of the most important forms of digital information, geographical location data play a critical role in various applications via big data processing, mobile communications and sensing technologies
Such methods can be divided into two categories: those based on the location privacy-preserving model with the trusted third party (TTP) and those based on the location privacy-preserving model without TTP
This paper proposes a location privacy-preserving method of k-anonymity based on the credible chain
Summary
As one of the most important forms of digital information, geographical location data play a critical role in various applications (e.g., smart cities, social networks and intelligent navigation) via big data processing, mobile communications and sensing technologies. To achieve k-anonymity, TTP expands the queried location into a broader anonymizing spatial region (ASR) that covers several other users (e.g., other k − 1 users) geographically As a result, it is difficult for an untrusted LBS server to determine a user’s real location from other k − 1 dummy locations [3]. It is difficult for an untrusted LBS server to determine a user’s real location from other k − 1 dummy locations [3] These approaches based on k-anonymity achieve high-level privacy protection while sacrificing service quality levels. Rather than forming a k-ASR similar to existing schemes, a TTP forms a fake trajectory that includes k-locations based on properties of the credible chain It can achieve 100% service accuracy while protecting user location privacy.
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