Split inference partitions a deep neural network (DNN) to run the early part
at the edge and the later part in the cloud. This meets two key requirements
for on-device machine learning: input privacy and compute efficiency. Still, an
open question in split inference is output privacy, given that the output of a
DNN is visible to the cloud. While encrypted computing can protect output
privacy, it mandates extensive computation and communication resources. In this
paper, we introduce "Salted DNNs": a novel method that lets clients control the
semantic interpretation of DNN output at inference time while maintaining
accuracy and efficiency very close to that of a standard DNN. Experimental
evaluations conducted on both image and sensor data show that Salted DNNs
achieve classification accuracy very close to standard DNNs, particularly when
the salted layer is positioned within the early part to meet the requirements
of split inference. Our method is general and can be applied to various DNNs.
We open-source our code and results, as a benchmark for future studies.

通过分割推理和 Salted DNNs 方法，满足了边缘计算中保护输入和输出隐私的要求，并在保持了类似标准 DNN 的准确性和效率的同时，为未来研究提供了一个基准。