Privacy preserving trajectory data publishing algorithm based on multilayer spatial fuzzy subtraction clustering algorithm

Trajectory data, such as the user's movement trajectory, is becoming more and more important in many current daily applications. The user's daily moving track will be collected and released for researchers to carry out data mining and other research. Even after the user identification information is deleted, other data can reveal the identity of the individual whose movement is recorded in the track, so it should be processed through privacy protection before being released. However, the existing trajectory data anonymization methods ensure the privacy of users, but the data availability is low. Therefore, this paper proposes a multi-layer spatial fuzzy subtraction clustering algorithm to study the trajectory data publishing algorithm of word segmentation privacy protection, and through PSR & ppm_ According to the information loss degree of trajectory data released after kg method, the fuzzy reasoning method of subtractive clustering is used to construct the database index function, and the cluster center vector is adjusted by changing the scale. On the basis of experimental analysis, the error reduction rate is increased by 45.7%. For the release time, the PSR&PPM_KG method is basically stable, the volatility also changes within a reasonable range, and the reduction rate of release time is 75.6% in general.