A Robust Method for Averting Attack Scenarios in Location Based Services

The development of Location Base Services (LBS) poses new challenges for the protection of user's privacy. Users have to send their current location information to the service provider. The current location of the user can expose critical information such as home/work address, other sensitive information, etc. thus, it is important to protect the users private and sensitive information. As a solution to this problem, different techniques have been proposed over the past few years. Among them, one of the most widely used technique is dummy location generation. However, current dummy location generation methods hardly account for the factor that an attacker has prior knowledge and spatiotemporal information about the user. In this paper, we identify shortcomings and vulnerabilities of the existing techniques and provide a robust solution to maintain the user's data privacy. Furthermore, we propose a robust dummy location generation method capable of averting the negative effects of an attacker having prior knowledge and spatiotemporal information. Additionally, we present some attacker strategies and remedies to avert such attacks. Experiment results show that our proposed method successfully preserves the user's private information, where other state of the art techniques fails to do so.