可穿戴设备流数据的隐私保护发布.

This paper proposed a privacy-preserving data publishing method based on autoencoder and time-frequency trans-formation to avoid privacy disclosure from the stream data of wearable devices. Firstly, this method transformed slide window data into frequency spectrum by the block discrete cosine transformation. Secondly, it desensitized frequency spectrum by autoencoder. Finally, it obtained slide window data from reconstructed frequency spectrum by the inverse block discrete cosine transformation. This paper applied pre-trained activity and identity classifiers to evaluate the privacy and utility of autoencoder' s output, then updated the weights of network by multi-objective loss function and back propagation. The experimental results on the Motion-Sense dataset show that the F1 -score of activity recognition on the reconstructed data is reduced from 0.944 to 0.940, the F1-score of identity recognition is reduced from 0.908 to 0.673, and the mean square error between reconstructed acceleration data and original data is 0.27. Compared with similar algorithms, this proposed algorithm can better retain the utility of data and improve data security. [ABSTRACT FROM AUTHOR]