Preserving Personal Space: Differentially Private Cybersickness Detection in Immersive Virtual Reality Environments

Cybersickness is a common problem that users often encounter during virtual reality (VR) experiences. Several automated methods exist based on machine learning (ML)/deep learning (DL) to detect cybersickness. However, the sensitive nature of data used by these ML/DL models (e.g., eye-tracking, head-tracking, etc.) introduces significant privacy risks since adversaries could exploit this data to infer and leak sensitive personal information, track individuals, or manipulate user experiences. Our research seeks to address this gap, underscoring the necessity for a private approach to cybersickness detection to protect user privacy and ensure a better VR experience. Thus, this paper proposes a privacy-preserving mechanism for DL-enabled cybersickness detection modeled. Specifically, we employ differential privacy (DP) to develop four private DL cybersickness detection models: long short-term memory (LSTM), grated recurrent unit (GRU), convolutional neural network (CNN), and multilayer perceptron (MLP) using Simulations 2021 and Gameplay, two open-source datasets. Our proposed models show high cybersickness detection accuracy for the proposed private cybersickness models. For instance, the private LSTM model shows the cybersickness detection accuracy of up to 92% and 91% for the Simulations 2021 and Gameplay datasets, respectively. Our experimental results also exhibit the privacy-preserving nature of private cybersickness detection. For instance, the private LSTM model reduces the membership inference attack’s success rate by up to 32% and 45% for the Simulations 2021 and Gameplay datasets compared to the baseline/non-private LSTM model for the same datasets.