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A machine learning approach for data protection in virtual reality therapy applications

Health information is a protected asset that should be kept private. The tradability of personal data brings risks to the health information shared by users online. Authentication is a crucial first step when working to keep personal information private. Virtual Reality applications usually bypass application-specific authentication in favor of provider-specific authentication (e.g. Steam, etc). This approach is not ideal for health applications. Virtual Reality secure authentication can be difficult because most methodologies are not user-friendly. Previously proposed Virtual Reality authentication systems use PINs, Patterns, or 3D object sequences. We propose an authentication method based on dynamic movements (i.e. dance moves). Machine learning-based models are employed to determine if the received movement matches the user’s previously chosen movement. The proposed method thus balances security with a better versatility than the one provided by traditional methods.