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Optimizing Task Location Privacy in Mobile Crowdsensing Systems
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AI
DOI：10.1109/TII.2021.3109437.png)
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Abstract
En 中文
The location information for tasks may expose sensitive information, which impedes the practical use of mobile crowdsensing in the industrial Internet. In this article, to our knowledge, we are the first to discuss the privacy protection of task locations and propose a codebook-based task allocation mechanism to protect it. Considering the cost of system utility caused by privacy protection technology, the tradeoff between local privacy and system utility is formalized a multiobjective optimization problem. The optimal solution is theoretically derived, and the optimal task allocation scheme is obtained. In addition, the selected allocation codebook (SAC) method is introduced to solve the problem of high computational resource consumption in the task allocation process and protect the task location privacy to some extent. The experimental results show that the SAC method sacrifices system utility but improves the privacy protection for task locations by 60% on average.
Keywords:
Task analysis
Privacy
Sensors
Resource management
Data privacy
Crowdsensing
Mutual information
Information theory
location privacy protection
mobile crowdsensing (MCS)
privacy exposure measure
task allocation
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