Privacy computing using deep compression learning techniques for neural decoding

The brain–computer interface supports a variety of applications with the help of machine learning technology. However, existing edge-cloud infrastructure requires subjects to send their sensitive neural signals to the cloud for training the model which brings privacy concerns. Although the recent distributed learning technology is used to help protect subjects’ privacy, it brings high communication costs and cannot avoid privacy reconstruction attacks. In this paper, we propose deep compression learning techniques in the privacy computing infrastructure that can be used for neural decoding while preserving privacy and largely reducing the communication cost. Specifically, we first perform heterogeneous neural signals processing and convert them to resized functional brain connectivity images. Then, a semantics structure-based unsupervised deep compression learning network is trained and generates a neural hash locally for each image. Each hash value is irreversible that cannot be used to reconstruct the user's original neural signal. After that, the cloud end receives the uploaded neural hashes and corresponding labels and filters the abnormal ones. Finally, these neural hashes are used for training neural decoding models. Since a single hash value can correspond to different types of labels, it only needs to be uploaded once with a very small size and then reused for different training tasks, which largely reduce communication cost. Our experiment results show that the proposed privacy computing techniques can be applied to heterogeneous neural signals for training different neural decoding models where the relative accuracy can achieve above 83%.