Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing

Abstract

The rapid proliferation of handheld intelligent devices and the advent of 5G technology have brought about convenient and fast services for people. In perception-oriented application services, participating users will upload sensitive mobile data in order to obtain benefits. While devising privacy protection strategies to ensure data security, it is crucial to accomplish task perception related to data collection to the fullest extent possible. To address this challenge, this paper proposes a personalized data privacy protection algorithm based on an adaptive dynamic adjustment grid and the minimum wage task allocation strategy. According to the different levels of users’ needs for privacy protection, combined with the privacy budget allocation strategy, we design a different-level differential privacy protection mechanism and consider the reward mechanism in task allocation to balance the effectiveness and security of the location data uploaded by users. Experiments show that the strategy proposed in this paper can not only protect the data but also enable users to freely choose the level of privacy protection.

Keywords

• differential privacy
• personalized privacy protection
• task assignment
• mobile crowdsensing