GLPP: A Game-Based Location Privacy-Preserving Framework in Account Linked Mixed Location-Based Services

Zhuo Ma,Xiusheng Chen,Bo Liu,Shuai Xu,Jiuxin Cao,Yuntao Yang

doi:10.1155/2018/9148768

Abstract

In Location-Based Services (LBSs) platforms, such as Foursquare and Swarm, the submitted position for a share or search leads to the exposure of users’ activities. Additionally, the cross-platform account linkage could aggravate this exposure, as the fusion of users’ information can enhance inference attacks on users’ next submitted location. Hence, in this paper, we propose GLPP, a personalized and continuous location privacy-preserving framework in account linked platforms with different LBSs (i.e., search-based LBSs and share-based LBSs). The key point of GLPP is to obfuscate every location submitted in search-based LBSs so as to defend dynamic inference attacks. Specifically, first, possible inference attacks are listed through user behavioral analysis. Second, for each specific attack, an obfuscation model is proposed to minimize location privacy leakage under a given location distortion, which ensures submitted locations’ utility for search-based LBSs. Third, for dynamic attacks, a framework based on zero-sum game is adopted to joint specific obfuscation above and minimize the location privacy leakage to a balanced point. Experiments on real dataset prove the effectiveness of our proposed attacks in Accuracy, Certainty, and Correctness and, meanwhile, also show the performance of our preserving solution in defense of attacks and guarantee of location utility.

Highlights

In a platform with Location-Based Services (LBSs), a relevant position is submitted for a share or search, which connects the physical world with cyber world and social world together [1]
As a companion application separated from older Foursquare, Swarm allows users to share their current location with friends and develops into a Location-Based Social Network (LBSN) with the share-based LBS
We focus on a new circumstance where the information linked by accounts is fused by the adversary to make a more accurate inference attacks about user’s proposed location

Summary

Introduction

In a platform with Location-Based Services (LBSs), a relevant position is submitted for a share or search (e.g., check-in, local search), which connects the physical world with cyber world and social world together [1]. The WeChat (https://web.wechat.com) user, who uses the surrounding search to explore new friends nearby, may disclose his current location [2, 3]. Another example is for Foursquare (https://foursquare.com) users using local search at a relatively private place (e.g., the hospital, the bank). Location privacy protection is a need, while the user experience should be ensured in search-based LBSs. As representative LBS platforms, Foursquare and Swarm are adopted to be the research case to prove the effectiveness of our proposed attacks and protection. The AL-LPL problem potentially exists for all these linked accounts

Methods

Results

Conclusion