  Speech emotion sensing in communication networks has a wide range of
applications in real life. In these applications, voice data are transmitted
from the user to the central server for storage, processing, and decision
making. However, speech data contain vulnerable information that can be used
maliciously without the user's consent by an eavesdropping adversary. In this
work, we present a privacy-enhanced emotion communication system for preserving
the user personal information in emotion-sensing applications. We propose the
use of an adversarial learning framework that can be deployed at the edge to
unlearn the users' private information in the speech representations. These
privacy-enhanced representations can be transmitted to the central server for
decision making. We evaluate the proposed model on multiple speech emotion
datasets and show that the proposed model can hide users' specific demographic
information and improve the robustness of emotion identification without
significantly impacting performance. To the best of our knowledge, this is the
first work on a privacy-preserving framework for emotion sensing in the
communication network.
