Spatial Cloaking Revisited: Distinguishing Information Leakage from Anonymity

Abstract

Abstract. Location-based services (LBS) are receiving increasing popularity as they provide convenience to mobile users with on-demand information. The use of these services, however, poses privacy issues as the user locations and queries are exposed to untrusted LBSs. Spatial cloaking techniques provide privacy in the form of k-anonymity; i.e., they guarantee that the (location of the) querying user u is indistinguishable from at least k-1 others, where k is a parameter specified by u at query time. To achieve this, they form a group of k users, including u, and forward their minimum bounding rectangle (termed anonymizing spatial region, ASR) to the LBS. The rationale behind sending an ASR instead of the distinct k locations is that exact user positions (querying or not) should not be disclosed to the LBS. This results in large ASRs with considerable dead-space, and leads to unnecessary performance degradation. Additionally, there is no guarantee regarding the amount of location information that is actually revealed to the LBS. In this paper, we introduce the concept of information leakage in spatial cloaking. We provide measures of this leakage, and show how we can trade it for better performance in a tunable manner. The proposed methodology directly applies to centralized and decentralized cloaking models, and is readily deployable on existing systems.

Location Privacy Protection Research Based on Querying Anonymous Region Construction for Smart Campus

“…In all the above solutions, if the k users used to construct the anonymous regions are far away from each other, the anonymous regions may still be too large, and the service quality may be low. Tan et al [33] were the first ones to apply the idea of regionalization to the construction of anonymous regions, which divided the users in the anonymous regions into separate groups through the Hilbert space filling curve. When a user makes a server request, the anonymous server will use the locations of other users in the group to which it belongs to construct the anonymous region.…”

Optimizational Scheme for Constructing Anonymous Spatial Region under Continuous LBS Queries

“…The ASR generated by this scheme will expand rapidly as the number of continuous LBS queries increases. Tan [4] proposed a method for ASR segmentation, he segments the rectangular ASR which is oversized and single into a number of discrete small rectangular ASRs, the total number of users in these small ASRs should be not less than k, the sum area of these small ASRs should be not less than the minimum size of the ASR required by users and at least one small ASR should contain the user u who sent the continuous LBS queries. To solve the problem that ASR is oversized, Xu [5] et al first proposed a method based on users' historical footprint information, they choose k-1 of the nearest historical traces from the user who send continuous LBS queries to construct ASR.…”

Protecting query privacy in location-based services

“…Because of its simplicity, k-anonymity has been studied and refined in many ways. For instance, Tan et al define information leakage to measure the amount of revealed location information in spatial cloaking, which quantifies the balance between privacy and performance [46]. Xue et al [48] introduce the concept of location diversity to ensure generalised regions to contain at least semantic locations (e.g., schools).…”