Study on Active Privacy Protection Method in Metaverse Gaze Communication Based on SplitFederated Learning

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.