A Context-Aware Location Differential Perturbation Scheme for Privacy-Aware Users in Mobile Environment

Shan Huang,Qian Chen,Xuejun Zhang,Xiaogang Du,Haiyan Huang,Tao Ju

doi:10.1155/2018/9173519

Shan Huang, Qian Chen + Show 4 more

Open Access

https://doi.org/10.1155/2018/9173519

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Abstract

The proliferation of location-based services, representative services for the mobile networks, has posed a serious threat to users’ privacy. In the literature, several privacy mechanisms have been proposed to preserve location privacy. Location obfuscation enforced using cloaking region is a widely used technique to achieve location privacy. However, it requires a trusted third-party (TTP) and cannot sufficiently resist various inference attacks based on background information and thus is vulnerable to location privacy breach. In this paper, we propose a context-aware location privacy-preserving solution with differential perturbations, which can enhance the user’s location privacy without requiring a TTP. Our scheme utilizes the modified Hilbert curve to project every 2-d location of the user in the considered map to 1-d space and randomly generates the reasonable perturbation by adding Laplace noise via differential privacy. In order to solve the resource limitation of mobile devices, we use a quad-tree based scheme to transform and store the user context information as bit stream which achieves the high compression ratio and supports efficient retrieval. Security analysis shows that our proposed scheme can effectively preserve the location privacy. Experimental evaluation shows that our scheme retrieval accuracy is increased by an average of 15.4% compared with the scheme using standard Hilbert curve. Our scheme can provide strong privacy guarantees with a bounded accuracy loss while improving retrieval accuracy.

Highlights

As the indispensable parts of the communications and networks field, the green mobile networks are seen as a potential enabler to realize green communications and networks by minimizing energy consumption while guaranteeing the quality of service [1]
Our approach can be presented in two parts: (1) we use a modified Hilbert curve (MHC) based on points of interest (POIs) density in considered local map to achieve the contextual information of the user’s location and store it as bit stream; (2) we employ a carefully selected Laplace noise distribution to generate a reasonable perturbation and transmit the perturbed value as the user’s location to the LBS providers (LSP)
This is because when the location index is generated, the Standard Hilbert curve (SHC) perturbation is partitioned by the uniform granularity for all the regions; the MHC is divided according to the density distribution of the POIs

Summary

Introduction

As the indispensable parts of the communications and networks field, the green mobile networks are seen as a potential enabler to realize green communications and networks by minimizing energy consumption while guaranteeing the quality of service [1]. Local obfuscation and differential perturbation approaches [4, 6, 9] may be used to protect user’s privacy against an adversary with such side information, as they consider the adversary’s knowledge and capability to better make a trade-off between location privacy and LBS utility. (1) We propose a context-aware differentially private location perturbation scheme that does not require a TTP and can protect a user’s location privacy against an adversary with side information. (2) We construct a MHC according to the density distribution of POIs in the considered local map and design a differential location perturbation algorithm based on it to protect user’s location privacy in LBSs This scheme provides strong privacy guarantees through the differential privacy.

Related Works

Preliminaries

Location Differential Perturbation

Experimental Evaluation

Findings

Conclusion