Deakin University
Browse

File(s) under permanent embargo

Intelligent pseudo-location recommendation for protecting personal location privacy

journal contribution
posted on 2020-01-25, 00:00 authored by Belinda HuangBelinda Huang, K Deng, Z Xie, J He
Individuals' right to privacy includes control over access to their location information. With the advent of location‐based services and personal transport services (such as ridesharing), the risk of location privacy breaches is increased greatly. The potential negative effects of location privacy leakages include spam location‐based service flooding, threats to personal safety (such as physical attacks), and intrusion related to access to private places (such as homes and hospitals). Therefore, protecting the privacy of users' real locations is becoming increasingly important. This is often achieved using a pseudo‐location near the real location, but existing pseudo‐location generators, such as NRand and the uniform random method, suffer from statistical inference, which can infer the obfuscation domain to cover the real location. In this paper, we propose an intelligent pseudo‐location recommendation (IPLR) method to reduce the risk of a statistical inference attack. In IPLR, we generate a random substitute of the real location to attract the adversary and thus hide the real location. Then, the pseudo‐location is generated in the neighborhood of the random substitute location following a normal distribution; the random substitute location is changed frequently to confuse attackers. In particular, we define three levels of location privacy, ie, address level, street level, and district level, to evaluate the effectiveness of the IPLR method. Our experimental study using simulation data demonstrates that the proposed IPLR method achieves lower risk of location privacy leakage and higher probabilities of safety in all three levels of location privacy than NRand and the random method. It also demonstrates the effectiveness of the proposed IPLR to balance location privacy and service quality.

History

Journal

Concurrency and Computation: Practice and Experience

Volume

32

Issue

2

Article number

e5435

Publisher

John Wiley & Sons

Location

Oxford, Eng.

ISSN

1532-0626

eISSN

1532-0634

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2019, John Wiley & Sons