Virtual Reality(VR) applications utilise many sensor-tracked user movements to interact with the immersive environment and drive the avatar movements. These innocently seaming movement data are capable of revealing and predicting user behaviours when combined with machine learning algorithms. Even though these findings could be beneficial in areas like education or healthcare, unwanted and unauthorised processing and utilisation of these data could pose significant user privacy risks.

To address this issue, this research focuses on introducing and evaluating methodologies to balance user privacy without affecting the user experience in VR-mediated applications. To evaluate the proposed methodologies, we use the most researched usage of these behaviour data, user identity detection, by employing machine learning-based classification algorithms.

We begin this PhD research by exploring the nature of machine learning classification models used for user identity detection, analysing their strengths and weaknesses. Based on this analysis, we identify the overall best-performing model and use it to propose and evaluate two types of behaviour filters, assessing their effectiveness in concealing user identity. To validate the effectiveness of the proposed filters, we proposed a new multi-faceted validation model and explored the best ways to evaluate human-based identity recognition in avatars. We then examine the impact of these filters on the overall VR experience. Finally, to gain insights into how actual VR users perceive and prefer to share their behaviour data, we conduct and analyse user interviews. Through these stages, we critically discuss the research implications, suggest future research directions, and provide guidelines for developing and evaluating VR privacy solutions.

Overall, this research addresses the question, "How do We Balance User Privacy and User Experience in VR Mediated Experiences?" through a comprehensive research approach that yields promising insights into novel strategies for enhancing VR user privacy. We also identify and highlight several major misconceptions and mistakes common to many existing privacy solutions, offering practical suggestions to overcome these challenges. We hope that the results, observations, and implications presented in this thesis will contribute to the development of more robust, multi-faceted privacy solutions to address the complex behaviour privacy challenges in VR environments.