Group Coding Location Privacy Protection Method Based on Differential Privacy in Crowdsensing
Taochun Wang, Yuan Tao, Qiong Zhang, Nuo Xu, Fulong Chen, Chuanxin Zhao
Abstract
With the proliferation of mobile smart devices, such as smartphones, mobile crowdsensing (MCS) has gained significant attention and widespread application. However, the increasing risk of personal privacy breaches has become a significant concern in MCS. Typically, workers are required to disclose their location information to participate in task assignments, making the protection of sensitive data, like location, a crucial factor influencing worker engagement. To address the issue of location privacy leakage in the task allocation process, this article proposes a location privacy protection method (VGDP) based on local differential privacy. In VGDP, the server utilizes a clustering algorithm to construct a task map based on the Voronoi diagram using task locations. Each task location is then mapped to its corresponding task area to ensure the privacy of the location information. Encoding technology is employed to encode the relative locations of all workers within the area, while a double random response mechanism is utilized to obfuscate the relative location codes, thereby safeguarding their location privacy. Furthermore, a personalized privacy budget allocation mechanism is employed to enhance the effectiveness of privacy protection. Once workers upload their perturbed location information to the server, the server selects winners based on the perturbed locations to facilitate task allocation. Additionally, this article proposes a high-reward payment method to augment workers’ enthusiasm for participation. Experimental results demonstrate that the proposed method exhibits promising performance in terms of data availability and location privacy.