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

Abstract EN

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.