Privacy-Preserving Real-Time Gesture Recognition using Cloud-Trained Neural Networks

This paper presents a novel approach to privacy-preserving gesture recognition using a remotely trained neural network. Our method ensures the protection of sensitive user data from potential threats, thereby mitigating concerns about data privacy and security. By utilizing encryption techniques, we enable organizations to train complex machine learning models on large-scale datasets without compromising data integrity. We demonstrate the feasibility of this approach through the implementation of proposed models for gesture classification, which achieved high accuracy in both encrypted and plain modes. Our results show that these models are suitable for embedded devices, making them a viable option for commercial or industrial applications such as smart car navigation systems.