User-Driven Privacy-Preserving Data Streams Release for Multi-Task Assignment in Mobile Crowdsensing

Multi-task assignment is widely used in mobile crowdsensing (MCS) to efficiently utilize limited resources such as shared user pool, user capability constraints and so on. In MCS, users need to submit data streams to perform sensing tasks, which involve a large amount of private information. However, the privacy leakage when users perform tasks across different types and submit multimodal data streams in multi-task assignment has not been fully addressed in current works. Privacy requirements vary for users with different activity levels in multi-task assignment. Specifically, users with higher activity levels tend to handle more task types and submit more data types, which poses more serious consequences of privacy leakage. Meanwhile, the privacy requirements of users are dynamic due to the user’s changing activity. In this work, we propose a user-driven local differential privacy framework for multi-task assignment called UD-LDP. First, we design a flexible privacy model called <inline-formula><tex-math notation="LaTeX">$w$</tex-math></inline-formula>-adjacent-event privacy to provide accurate privacy protection for users with different activity levels. Then, we introduce information entropy to quantify privacy requirements of user’s activity in real-time. After that, we propose a privacy-aware budget allocation method to dynamically allocate personalized privacy budgets for each user. At last, we design a variance-optimized selection method that chooses rational privacy budgets and users for release to improve data utility. The effectiveness of our framework is supported by experiments conducted on both real-world and synthetic datasets.