  Splitting network computations between the edge device and a server enables
low edge-compute inference of neural networks but might expose sensitive
information about the test query to the server. To address this problem,
existing techniques train the model to minimize information leakage for a given
set of sensitive attributes. In practice, however, the test queries might
contain attributes that are not foreseen during training. We propose instead an
unsupervised obfuscation method to discard the information irrelevant to the
main task. We formulate the problem via an information theoretical framework
and derive an analytical solution for a given distortion to the model output.
In our method, the edge device runs the model up to a split layer determined
based on its computational capacity. It then obfuscates the obtained feature
vector based on the first layer of the server model by removing the components
in the null space as well as the low-energy components of the remaining signal.
Our experimental results show that our method outperforms existing techniques
in removing the information of the irrelevant attributes and maintaining the
accuracy on the target label. We also show that our method reduces the
communication cost and incurs only a small computational overhead.
