Abstract
With the rapid development of Internet services, mobile communications, and IoT applications, Location-Based Service (LBS) has become an indispensable part in our daily life in recent years. However, when users benefit from LBSs, the collection and analysis of users’ location data and trajectory information may jeopardize their privacy. To address this problem, a new privacy-preserving method based on historical proximity locations is proposed. The main idea of this approach is to substitute one existing historical adjacent location around the user for his/her current location and then submit the selected location to the LBS server. This method ensures that the user can obtain location-based services without submitting the real location information to the untrusted LBS server, which can improve the privacy-preserving level while reducing the calculation and communication overhead on the server side. Furthermore, our scheme can not only provide privacy preservation in snapshot queries but also protect trajectory privacy in continuous LBSs. Compared with other location privacy-preserving methods such as k-anonymity and dummy location, our scheme improves the quality of LBS and query efficiency while keeping a satisfactory privacy level.
Highlights
With the development of Internet services, mobile communications, and IoT applications, Location-Based Service (LBS) has become one of the popular electronic applications
This method ensures that the user can obtain location-based services without submitting the real location information to the untrusted LBS server, which improves the location privacy level and reduces computation and communication load on the server side
This paper proposed a solution for location privacy protection in both snapshot queries and continuous queries
Summary
With the development of Internet services, mobile communications, and IoT applications, Location-Based Service (LBS) has become one of the popular electronic applications. When a user lies in an unpopulated region, its cloaking area may be very large since it needs to contain the user itself and at least k − 1 other users These traditional k-anonymity schemes cannot be directly applied to the protection of location privacy due to their inherent flaws. To address the above problems, we propose a new privacypreserving method based on historical proximity locations This method ensures that the user can obtain location-based services without submitting the real location information to the untrusted LBS server, which improves the location privacy level and reduces computation and communication load on the server side. Our solution is more difficult for attackers to distinguish the user’s true position from historical locations, and at the same time it cannot generate unreasonable positions (3) compared with the existing schemes, performance analysis results show that our proposal can significantly improve the query efficiency while ensuring privacy protection.
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