DGS-HSA: A Dummy Generation Scheme Adopting Hierarchical Structure of the Address

Abstract

With the increasing convenience of location-based services (LBSs), there have been growing concerns about the risk of privacy leakage. We show that existing techniques fail to defend against a statistical attack meant to infer the user’s location privacy and query privacy, which is due to continuous queries that the same user sends in the same location in a short time, causing the user’s real location to appear consecutively more than once and the query content to be the same or similar in the neighboring query. They also fail to consider the hierarchical structure of the address, so locations in an anonymous group may be located in the same organization, resulting in leaking of the user’s organization information and reducing the privacy protection effect. This paper presents a dummy generation scheme, considering the hierarchical structure of the address (DGS-HSA). In our scheme, we introduce a novel meshing method, which divides the historical location dataset according to the administrative region division. We also choose dummies from the historical location dataset with the two-level grid structure to realize the protection of the user’s location, organization information, and query privacy. Moreover, we prove the feasibility of the presented scheme by solving the multi-objective optimization problem and give the user’s privacy protection parameters recommendation settings, which balance the privacy protection level and system overhead. Finally, we evaluate the effectiveness and the correctness of the DGS-HSA through theoretical analysis and extensive simulations.

Location-Query-Privacy and Safety Cloaking Schemes for Continuous Location-Based Services

“…In [34], a dummy generation scheme, which contemplates the hierarchical structure of the address (DGS-HSA), is described. DGS-HSA provides a novel meshing method splitting the historical location dataset taking into consideration the administrative region division.…”

Cross-Platform Strong Privacy Protection Mechanism for Review Publication

“…e dummy method [16,31,32] usually adds false users or false locations to achieve anonymity. For example, Li et al [16] added fake locations, and Niu et al [32] added fake users to achieve anonymity. However, in the review publication scenario, if a user has not visited a business, the user is not allowed to publish reviews on the business's services.…”

“…Moreover, although the schemes of literature [5,6] can protect users' privacy to some extent, they do not take into account the behavioral patterns of users. is results in the above approaches being unable to resist statistical inference attack (SIA) due to their inability to prevent adversaries from accessing long-term user behavior data [15,16].…”