A Location Privacy Preserving Method Based on Sensitive Diversity for LBS

Abstract

A user's staying points in her trajectory have semantic association with privacy, such as she stays at a hospital. Staying at a sensitive place, a user may have privacy exposure risks when she gets location based service (LBS). Constructing cloaking regions and using fake locations are common methods. But if regions and fake positions are still in the sensitive area, it is vulnerable to lead location privacy exposure. We propose an anchor generating method based on sensitive places diversity. According to the visiting number and peak time of users, sensitive places are chosen to form a diversity zone, its centroid is taken as the anchor location which increases a user's location diversity. Based on the anchor, a query algorithm for places of interest (POIs) is proposed, and precise results can be deduced with the anchor instead of sending users' actual location to LBS server. The experiments show that our method achieves a tradeoff between QoS and privacy preserving, and it has a good working performance.

A blockchain anonymity solution to prevent location homogeneity attacks

“…Reference 23 proposed an anchor selection algorithm based on the diversity of user‐sensitive locations, which used the temporal and spatial characteristics of user group visits to define and distinguish adjacent sensitive locations. Reference 24 introduced the concept of diversity of users' similar positions and considered the unreliability of some users.…”

A Reconstruction Attack Scheme on Secure Outsourced Spatial Dataset in Vehicular Ad-Hoc Networks

“…e second category is based on generalization and obfuscation [11][12][13][14][15][16][17]. Spatial concealment technology [11,12] forms a hidden area containing k real users for each user, making it difficult for service providers to determine the real identity and accurate spatial of the user from the hidden area.…”

“…But in this type of method, it is difficult to achieve a balance between privacy protection and service quality in data-sparse areas. Spatial offset and obfuscation technology [13][14][15][16] protect user's privacy by moving the real spatial in a small area or replacing the real spatial with a certain area. For example, Andrés et al [16] achieved the geo-indistinguishability by adding controlled random noise to user's spatial, which corresponds to differential privacy.…”

“…Guess Index Y←Get Index(L access2 , Ordered Record Y) (15) For each record in ordered Record (16) map index(recordI D, Y Index) (…”

“…If it is proved to be the last item, the row record only needs to be reversed. Finally, we sort the other encrypted records in this row one by one (lines: [13][14][15][16][17][18][19][20]. Utilizing the continuity of range query, we find the minimum superset of the determined record in the access pattern set, and the difference between the minimum superset and the record set in the Input: e set of access patterns of uniform query for a row/columnL access2 L access2 � (q 1 , R 1 ), .…”