Abstract:

In view of mobile crowdsensing, the existing privacy protection schemes use an unified privacy protection strategy for all perceived data because of the lack of effective privacy measurement methods, which leads to the problem of excessive protection or insufficient protection of the privacy information of the perceived data, and the accuracy of the perceived data is low, this paper proposes an user privacy measurement method for mobile crowdsensing and constructs a personalized privacy protection scheme based on this method. Firstly, according to the historical and spatio-temporal data of the sensing users, by using the fuzzy reasoning technology, combining the public attribute of the location and the different personality attributes of the location to a user, the privacy measurements of the user in different locations are obtained. Furthermore, the sensing platform selects a sensing user with low privacy measurement to participate in the sensing task in each location according to the different privacy measurement that user uploads, which ensures that users can contribute to the perceived data with high accuracy under the premise of privacy security. The simulation results show that the scheme ensures the accuracy of the perceived data while improves the level of privacy protection.

Key words: mobile crowdsensing, privacy measurement, spatio-temporal data, privacy protection