Dummy trajectory generation scheme based on generative adversarial networks

Dummy trajectory is widely used to protect the privacy of mobile users’ locations. However, two main challenges remain: (1) Map background information has not been modeled by machine learning methods in existing schemes, and (2) it is difficult to generate a good quality dummy trajectory that is similar to the real one. Focused on these two challenges, in this paper, we propose a dummy trajectory generation scheme with conditional generative adversary network (GAN), where the map features are extracted using convolutional neural network, which is regarded as a prior restriction of conditional GAN. Then, the movement pattern of the real trajectory is deduced by an auto-encoder and is involved in the dummy trajectory generation. Our model is trained and evaluated with two real-world datasets. Experimental results demonstrate that our scheme addresses these challenges well and defends against various attacks effectively.