  We propose a method to revise the neural network to construct the
quaternion-valued neural network (QNN), in order to prevent intermediate-layer
features from leaking input information. The QNN uses quaternion-valued
features, where each element is a quaternion. The QNN hides input information
into a random phase of quaternion-valued features. Even if attackers have
obtained network parameters and intermediate-layer features, they cannot
extract input information without knowing the target phase. In this way, the
QNN can effectively protect the input privacy. Besides, the output accuracy of
QNNs only degrades mildly compared to traditional neural networks, and the
computational cost is much less than other privacy-preserving methods.
