A lightweight privacy protection scheme based on user preference in mobile crowdsensing. Issue 5 (8th June 2020) - Xiong, Jinbo; Liu, Hui; Jin, Biao; Li, Qi; Yao, Zhiqiang
Abstract: 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. Abstract : The LightPrivacy scheme consists of the following four phases: sub‐task division, target users set selection, ASA‐DP algorithm and user task contribution definition. First of all, the first phase is to divide sensing tasks by considering the sizes and requirements of them, and selects an optimal sub task set by the AHP. The second phase is to build the TBF based on the attribute demand preferences of fog nodes, and build the UBF based on the attribute preferences by sensing users. Regarding the TBF and UBF as two binary vectors, fog nodes calculate the two binary vectors by the BCVIPP. Furthermore, the third phase is to define sensitive data based on their own attribute preferences. The remaining data is divided into direct sensitive data and indirect sensitive data based on the degree of association with sensitive data. According to the degree of sensitivity, different privacy budget perturbation is added into different sensitive data, and the total privacy budget is unchanged. Finally, the fourth phase is the definition of task contribution of sensing users. The task contribution of the sensing users is defined by data quality submitted by the sensing users and the value of the BCVIPP, and corresponding rewards and punishments are given by fog nodes.