A lightweight privacy protection scheme based on user preference in mobile crowdsensing

Aiming at the balance between user personalized privacy protection and task data practicability in mobile crowdsensing, this article proposes a lightweight privacy protection (LightPrivacy) scheme based on the matching of attribute preferences between users and tasks with analytic hierarchy process (AHP), bloom filter, binary confusion vector inner product protocol, and differential privacy. First, the fog nodes are introduced to divide the subtasks of sensing tasks, and the optimal subtask set is selected and published through AHP. The fog nodes construct task bloom filter according to task attribute requirement preference. The sensing users construct user bloom filter based on intention attribute preference, and filter target users by calculating the binary confusion vector inner product of two bloom filters. Furthermore, the LightPrivacy scheme perceives sensing users to localize sensitive data that needs to be disturbed according to privacy budget distributed equally by fog nodes. Finally, the fog node evaluates the quality of sensing data and defines task contribution of sensing users in combination with the binary confusion vector inner product, so as to effectively prevent malicious users from submitting false data or adding large data perturbations. Security analysis indicates that the LightPrivacy scheme is still security under the condition that fog nodes are semi‐trusted. Experimental results show that the LightPrivacy scheme is practical and the computational efficiency is significantly improved compared with the related representative schemes.