【摘要】 随着元宇宙的迅猛发展，视线交互技术作为元宇宙核心交互方式受到广泛关注，视线隐私问题愈发引起人们的担忧。视线不仅可以表征凝视方向，还能用于个体身份识别，以及识别一系列敏感的软生物特征，如年龄、性别、种族等，甚至可以用于推断个体的情绪、认知状态和决策过程。因此，研究元宇宙视线交互中的隐私保护策略变得极为关键。此外，元宇宙中很多基于视线交互的新功能需要利用特定的个体隐私属性以提供更好的服务，然而目前尚无主动控制视线隐私进行选择性流通的方法。为此，首先围绕视线隐私泄露问题展开了分层次、定量的实证研究；接着创新性地提出了一种融合联邦学习与拆分学习的视线隐私保护框架，有效降低了隐私泄露的风险；进一步地，提出了一种基于对抗训练的主动隐私控制策略，不仅实现了针对性的隐私过滤，而且提高了视线模型的泛化能力；最后进行了严谨的实验验证，所提方法在视线数据的隐私保护和交互性能方面展现出了双重优势，为元宇宙环境中视线交互的隐私保护提供了创新的解决路径和技术方案。更多还原

【Abstract】 In the rapidly evolving metaverse, gaze interaction has emerged as a pivotal mode of communication.However, gaze data encompasses more than mere gaze orientation and ocular mobility.It can also be applied for identification and recognition of soft biometrics, including age, gender, and ethnicity.Furthermore, it has the potential to disclose an individual’s emotion, cognitive processes, and decision-making patterns.Given its sensitive nature, the development of robust gaze data privacy protection mechanisms has become imperative, attracting considerable interest.Additionally, numerous gaze-driven applications necessitate specific privacy attributes for functional support, yet active selection and protection of gaze privacy remains unexplored in current research.To this end, this study initially conducts hierarchical and quantitative analyses to uncover the severe state of gaze privacy breaches.Subsequently, it introduces an innovative gaze privacy safeguarding framework that integrates federated learning with split learning, significantly mitigating leakage risks.Moreover, this research proposes an active privacy protection strategy employing adversarial training and information bottleneck technique, which ensures targeted privacy filtration alongside enhancements in model generalization.Comprehensive experiments confirm that the devised APSFGaze approach excels in both privacy protection and performance.This study offers a novel pathway and technological framework for privacy preservation in metaverse gaze interactions.更多还原