Chulalongkorn University Memorial Hall is an important historic landmark in Thailand that collects and displays valuable and interesting documents and items from the history of the Chulalongkorn University, which is the first university established by the Thai dynasty. Many notable historical relics from Chulalongkorn University have been displayed for visitors inside the museum, including a miniature of King Chulalongkorn's equestrian statue, a coronet (Phra Kiew), and a graduation gown. Over the past two years, due to the COVID-19 situation, the number of visitors to the museum has been restricted. Therefore, we propose to solve this problem by introducing a virtual tour using virtual reality to cope with this challenging situation. This paper presents a description related to the Chulalongkorn University memorial hall in terms of 3D models through interactions in a virtual reality world, in which users gain in-depth experience and knowledge about the history of the Thai Royal and the Chulalongkorn University. Both the internal and external of the Chulalongkorn University Memorial Hall building are reproduced in 3D models, including furniture and belongings in the hall, for simulating the entire Chulalongkorn University Memorial Hall building. In addition to visiting the historical objects in the virtual museum, these objects are interactable with multimedia descriptions which are text, images, videos, and audio commentary. It makes visitors enjoy the ambiance while visiting the virtual Chulalongkorn University Memorial Hall. In addition, it is also the preservation of valuable cultural and historical knowledge resources.

VR empirical data include the sensitive affective and behavioral data (e.g. gaze patterns, physiological signals, facial expressions), placing a person at the risk of re-identification. Tailored privacy-preserving approaches to immersive data have not, however, received much attention in the recent affective-computing investigations. In the present paper, a differentially-privacy (DP) framework is presented as that allows anonymizing VR-based studies of emotion without losing scientific value. The first one is the quantification of re-identification risks through empirical linkage and inference attacks and then the mitigation of the risk through DP mechanisms such as Laplace noise, that sought to reduce the re-identification risks by 80%. At the same time, the strategy returned an 85% retention with respect to utility relating to emotion-classification. The results can allow sharing VR-collected affective data safely to use in research without infringing GDPR/HIPAA provisions and thus offer practical procedures in implementing DP in VR-based empathy research and developing ethical data collection practices in the field. Finally, on the one hand, the framework mediates between immersive technology and information privacy, and on the other hand, it enables the development of emotional AI and psychological research by providing anonymity of the participants. Among its contributions, VR-specific parameter optimizations and a risk-utility assessment metric, there are specific findings towards the higher priority of effectiveness of affective datasets in the VR-based therapy and social-emotional learning.