Abstract

The growing popularity of location-based services is giving untrusted servers relatively free reign to collect huge amounts of location information from mobile users. This information can reveal far more than just a user’s locations but other sensitive information, such as the user’s interests or daily routines, which raises strong privacy concerns. Differential privacy is a well-acknowledged privacy notion that has become an important standard for the preservation of privacy. Unfortunately, existing privacy preservation methods based on differential privacy protect user location privacy at the cost of utility, aspects of which have to be sacrificed to ensure that privacy is maintained. To solve this problem, we present a new privacy framework that includes a semi-trusted third party. Under our privacy framework, both the server and the third party only hold a part of the user’s location information. Neither the server nor the third party knows the exact location of the user. In addition, the proposed perturbation method based on the Johnson Lindenstrauss transform satisfies the differential privacy. Two popular point of interest queries, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -NN and Range, are used to evaluate the method on two real-world data sets. Extensive comparisons against two representative differential privacy-based methods show that the proposed method not only provides a strict privacy guarantee but also significantly improves performance.

Highlights

  • The pervasive diffusion of GPS-enabled devices has provided tremendous opportunities for the development of locationbased services (LBSs)

  • A user queries an LBS with their current location, and the LBS returns the corresponding points of interest (POIs)

  • We propose a new Johnson Lindenstrauss transform based location privacy protection method

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Summary

INTRODUCTION

The pervasive diffusion of GPS-enabled devices has provided tremendous opportunities for the development of locationbased services (LBSs). As the user’s location coordinates are perturbed by matrix X , to guarantee the utility, the map should be perturbed by the same matrix, after which the relative distances between user and POIs can be preserved Both the service provider and the third party hold partial location information about the user. If the service provider knows the perturbed location vector and the transition matrix at the same time, the true location coordinates of the user would be disclosed This task is assigned to the third party. The third party samples a small map according to the received user’s safe region R and the queried POI type in Step 1 and perturbs it using received transition matrix X in Step 2. After getting the k anonymized POI sets POIsa from service provider : 5: Find the real POIs information according to the mapping function f

6: Encrypt the POIs information
UTILITY ANALYSIS
EVALUATION AND DISCUSSION
VIII. CONCLUSION
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