In response to the rise of identity theft, continuous authentication based on user behavior (e.g., background sensor data) is emerging as a promising solution. However, in the context of distributed mobile applications, the processing of sensitive data raises serious concerns about user privacy. Existing methods employing homomorphic encryption address this, but face issues with increased network traffic and latency. Therefore, we introduce a novel approach that extends homomorphic encryption-based authentication systems to ensure efficient, continuous, and privacy-preserving authentication. It uses a modern homomorphic encryption scheme and an analysis process that leverages machine learning methods. In the first step, behavioral data is preprocessed on the clients’ mobile devices prior to encrypting it and sending it to the server. The server then performs an analysis with neural networks on the encrypted data, which serves as the basis for the authentication decision. We conducted an experiment using real mobile devices and a public dataset to validate our approach’s effectiveness, demonstrating competitive authentication accuracy, 32% reduction in network traffic, and over 68% reduction in latency compared to existing research.