In the crowd sensing application, a slice-based trajectory privacy protection method (STPP) is proposed for the privacy of mobile object trajectory data with rich spatiotemporal information. The trajectory data is first sliced with the position (GPS) as the smallest tuple so that the trajectory is converted into a corresponding trajectory tuple set, and each trajectory tuple is attached with a unique identifier. The user generates three random numbers $(lpha, lambda, eta)$ for each trajectory tuple, and sends the trajectory tuple together with $(lpha,lambda, eta)$ to the next user who then sends the trajectory tuple and the changed three random numbers to another randomly selected user. In this way, when the threshold of the set random number is reached, the user uploads the trajectory tuple to the server. Thus, the attacker cannot distinguish the location of the user. Due to the loss of the clustering relationship, the uploaded trajectory tuple becomes a set of anonymous and non-contiguous data sets, so that the server can not recognize the individual motion trajectory at all. Finally, the user can reconstruct the trajectory on the client based on the unique identifier. The experimental results and theoretical analysis show that STPP protects the privacy of the user’s trajectory and improves the practicability of the data.
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