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 soug... More >