An Adaptive Grid and Incentive Mechanism for Personalized Differentially Private Location Data in the Local Setting

Abstract

With the proliferation of wireless communication and mobile devices, various location-based services are emerging. For the growth of the location-based services, more accurate and various types of personal location data are required. However, concerns about privacy violations are a significant obstacle to obtain personal location data. In this paper, we propose a local differential privacy scheme in an environment where there is no trusted third party to implement privacy protection techniques and incentive mechanisms to motivate users to provide more accurate location data. The proposed local differential privacy scheme allows a user to set a personalized safe region that he/she can disclose and then perturb the user’s location within the safe region. It is the way to satisfy the user’s various privacy requirements and improve data utility. The proposed incentive mechanism has two models, and both models pay the incentive differently according to the user’s safe region size to motivate to set a more precise safe region. We verify the proposed local differential privacy algorithm and incentive mechanism can satisfy the privacy protection level while achieving the desirable utility through the experiment.