Salted Inference: Enhancing Privacy while Maintaining Efficiency of Split Inference in Mobile Computing

In split inference, a deep neural network (DNN) is partitioned to run the early part of the DNN at the edge and the later part of the DNN in the cloud. This meets two key requirements for on-device machine learning: input privacy and computation efficiency. Still, an open question in split inference is output privacy, given that the outputs of the DNN are observable in the cloud. While encrypted computing can protect output privacy too, homomorphic encryption requires substantial computation and communication resources from both edge and cloud devices.