{"type":"ScholarlyArticle","title":"HyDiscGAN: A Hybrid Distributed cGAN for Audio-Visual Privacy Preservation in Multimodal Sentiment Analysis","authors":[{"name":"Zhuojia Wu","slug":"zhuojia-wu","given":"Zhuojia","family":"Wu","affiliation":"Tongji University , Shanghai , China"},{"name":"Qi Zhang","slug":"qi-zhang","given":"Qi","family":"Zhang","affiliation":"Tongji University , Shanghai , China; DeepBlue Academy of Sciences , Shanghai , China"},{"name":"Duoqian Miao","slug":"duoqian-miao","given":"Duoqian","family":"Miao","affiliation":"Tongji University , Shanghai , China"},{"name":"Kun Yi","slug":"kun-yi","given":"Kun","family":"Yi","affiliation":"Beijing Institute of Technology , Beijing , China"},{"name":"Wei Fan","slug":"wei-fan","given":"Wei","family":"Fan","affiliation":"University of Oxford , Oxford , United Kingdom"},{"name":"Liang Hu","slug":"liang-hu","given":"Liang","family":"Hu","affiliation":"Tongji University , Shanghai , China; DeepBlue Academy of Sciences , Shanghai , China"}],"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.","doi":"10.24963/ijcai.2024/724","url":"https://ijcai.org/proceedings/2024/724","pdf":"https://ijcai.org/proceedings/2024/0724.pdf","markdown":"https://ijcai.org/proceedings/2024/724.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/724","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Natural Language Processing","track":"Main Track","number":724,"pages":{"first":6550,"last":6558},"keywords":["Natural Language Processing: NLP: Sentiment analysis, stylistic analysis, and argument mining","Multidisciplinary Topics and Applications: MTA: Security and privacy","Machine Learning: ML: Multi-modal learning"],"videos":[],"references":[{"position":1,"raw":"[Almahairi et al., 2018] Amjad Almahairi, Sai Rajesh- war, Alessandro Sordoni, Philip Bachman, and Aaron Courville. 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