  Multimodal Sentiment Analysis (MSA) aims to identify speakers' sentiment
tendencies in multimodal video content, raising serious concerns about privacy
risks associated with multimodal data, such as voiceprints and facial images.
Recent distributed collaborative learning has been verified as an effective
paradigm for privacy preservation in multimodal tasks. However, they often
overlook the privacy distinctions among different modalities, struggling to
strike a balance between performance and privacy preservation. Consequently, it
poses an intriguing question of maximizing multimodal utilization to improve
performance while simultaneously protecting necessary modalities. This paper
forms the first attempt at modality-specified (i.e., audio and visual) privacy
preservation in MSA tasks. We propose a novel Hybrid Distributed cross-modality
cGAN framework (HyDiscGAN), which learns multimodality alignment to generate
fake audio and visual features conditioned on shareable de-identified textual
data. The objective is to leverage the fake features to approximate real audio
and visual content to guarantee privacy preservation while effectively
enhancing performance. Extensive experiments show that compared with the
state-of-the-art MSA model, HyDiscGAN can achieve superior or competitive
performance while preserving privacy.
