An Anti-Noise, Transferable, and Privacy-Preserving Real-Time Mobile User Authentication System With High Accuracy

The authentication technology of mobile device users has been studied for decades. To balance security, privacy, and usability, motion sensors-based user authentication methods are widely investigated in recent years. However, existing studies meet the problems such as scarcity of training samples, underutilization of data, poor de-noising ability, insufficient transferability, privacy leakage, and low accuracy. To overcome these difficulties, we propose a system, called for Transferable, Real-time, Anti-noise, and Privacy-preserving mobile user COGnition., with the following capabilities: 1) In the phase of data collection, can eliminate man-made noise (mislabeling) through differential training based on down-sampling. 2) In the model training stage, the siamese neural network with Long Short-Term Memory (LSTM) as the sub-network is used to achieve sufficient coverage of sample patterns and the transferability of the model. 3) In the phase of real-world authentication, the privacy of the user is tremendously protected through end-side model deployment and local authentication. Experimental results on a dataset composed of 1,513 users with real-world noise show that TRAP COG has high accuracy and strong transferability, which is much better than state-of-the-art studies.