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

Authors :
Mingzhen Li
Yunfeng Wang
Guangcan Yang
Shoushan Luo
Yang Xin
Hongliang Zhu
Yixian Yang
Yuling Chen
Fugui Luo
Source :
Applied Sciences (2076-3417); 1/15/2020, Vol. 10 Issue 2, p548, 25p
Publication Year :
2020

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20763417
Volume :
10
Issue :
2
Database :
Complementary Index
Journal :
Applied Sciences (2076-3417)
Publication Type :
Academic Journal
Accession number :
141573763
Full Text :
https://doi.org/10.3390/app10020548