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Deep learning methods can play a crucial role in anomaly detection,prediction, and supporting decision making for applications like personalhealth-care, pervasive body sensing, etc. However, current architecture of deepnetworks suffers the privacy issue that users need to give out their data tothe model (typically hosted in a server or a cluster on Cloud) for training orprediction. This problem is getting more severe for those sensitive health-careor medical data (e.g fMRI or body sensors measures like EEG signals). Inaddition to this, there is also a security risk of leaking these data duringthe data transmission from user to the model (especially when it's throughInternet). Targeting at these issues, in this paper we proposed a newarchitecture for deep network in which users don't reveal their original datato the model. In our method, feed-forward propagation and data encryption arecombined into one process: we migrate the first layer of deep network to users'local devices, and apply the activation functions locally, and then use"dropping activation output" method to make the output non-invertible. Theresulting approach is able to make model prediction without accessing users'sensitive raw data. Experiment conducted in this paper showed that our approachachieves the desirable privacy protection requirement, and demonstrated severaladvantages over the traditional approach with encryption / decryption

Dropping Activation Outputs with Localized First-layer Deep Network for Enhancing User Privacy and Data Security