Can We Realize Data Freshness Optimization for Privacy Preserving-Mobile Crowdsensing With Artificial Noise?
Yaoqi Yang, Bangning Zhang, Daoxing Guo, Zehui Xiong, Dusit Niyato, Zhu Han
Abstract
By utilizing intelligent mobile terminals, mobile crowdsensing (MCS) can realize the sensing data collection effectively and economically. However, the privacy security and freshness quality of the obtained sensing data are two major concerns to be addressed in MCS, as they directly impact the system security and timeliness performance. In this regard, we focus on improving the data freshness performance and protecting sensing data content, sensing terminals' identification, and location information simultaneously. Accordingly, based on the artificial noise (AN)-based differential privacy and covert communication technologies, we aim to jointly minimize the Age of Information (AoI) metric and weighted privacy preservation budget in the single terminal scenario. Besides, we achieve the goal of average AoI optimization with data computing requirements in multiple terminal systems, where the privacy preservation budget is treated as the critical constraint. Furthermore, by using the backward induction (BI) method and block successive upper-bound minimization (BSUM) approach, we solve the above two optimization problems, respectively. Finally, compared with the listed baselines, the results evaluate the proposed schemes' effectiveness under various simulation settings.