arXiv:2404.11938v1 Announce Type: new
Abstract: 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.
Multimodal Sentiment Analysis and Privacy Preservation
In the field of multimedia information systems, Multimodal Sentiment Analysis (MSA) has gained significant attention. It involves the analysis of multimodal data, such as audio, visual, and textual information, to identify the sentiment tendencies of speakers in video content. However, the use of multimodal data raises privacy concerns, particularly with the use of voiceprints and facial images.
One approach that has shown promise in preserving privacy in multimodal tasks is distributed collaborative learning. This paradigm allows for learning models to be trained across multiple devices without exchanging sensitive data. However, existing distributed collaborative learning methods often overlook the privacy distinctions among different modalities, leading to a trade-off between performance and privacy preservation.
This paper introduces a novel approach called the Hybrid Distributed cross-modality cGAN framework (HyDiscGAN) to address the privacy concerns in MSA tasks. Unlike previous methods, HyDiscGAN considers the privacy preservation of each modality separately, specifically audio and visual data. By leveraging the fake audio and visual features generated by the framework, HyDiscGAN approximates the real content while preserving privacy.
The core objective of HyDiscGAN is to strike a balance between performance enhancement and privacy preservation. By using shareable, de-identified textual data, the framework learns to generate fake audio and visual features that align with the original content. This approach guarantees privacy preservation while still achieving competitive or superior performance compared to existing state-of-the-art MSA models.
As a multi-disciplinary concept, the research presented in this paper combines aspects of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The use of multimodal data in MSA tasks touches upon various multimedia technologies and techniques, ranging from audio and visual processing to natural language processing and machine learning.
The HyDiscGAN framework not only showcases the potential of distributed collaborative learning in privacy preservation but also offers insights into the future development of MSA models. The modality-specified privacy preservation approach can be extended to other multimodal tasks, allowing for improved performance and privacy protection across different applications.