Track data anonymity removing method based on deep learning

Abstract

The invention discloses a track data anonymity removing method based on deep learning, which comprises the following steps: firstly, under preset division precision, dividing a space into a pluralityof subspaces similar to a grid shape; dividing the trajectory data into two groups, one group being a known user trajectory and the other group being an anonymous user trajectory; splitting the two groups of trajectories into trajectory segments, and mapping the trajectory segments into grid data to obtain a group of grid identification sequences corresponding to the trajectories; secondly, inputting the set of grid identification sequences in the previous step into an improved DenseNet model, taking a known user track as a training set, and taking an anonymous user track as a test set; finally, classifying the tracks through a model, enabling the anonymous data to correspond to data in a known user data set, and finding out real user information corresponding to the anonymous user track data. According to the invention, the de-anonymization of the private trajectory data is realized to verify the effectiveness of the attack, so that the privacy risk of the anonymous trajectory data isrevealed.

Description

technical field [0001] The invention belongs to the technical field of computer big data processing, and relates to a method for de-anonymizing trajectory data, in particular to a method for de-anonymizing trajectory data based on deep learning. Background technique [0002] The development of mobile terminals and positioning technology makes it possible to obtain the precise location of moving objects anytime and anywhere. Mobile trajectories usually contain rich spatio-temporal information, and valuable information can be obtained through reasonable mining and analysis. Internet service providers (ISPs) are increasingly collecting anonymous user movement trajectories. Detailed location tracking contains sensitive information about individual users (such as home and work locations, personal habits), and malicious attackers can use illegally obtained movement trajectories It is speculated that the user's personal privacy information, such as life cycle or sensitive location...

AI Technical Summary

Problems solved by technology

However, existing research based on real data sets is insufficient, and relies too much on the discovery of feature engineering and classification rules, which usually requires manual intervention, and ignores the impact of time attributes on classification results.

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