Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

Abstract

With the development of hardware devices and the upgrading of smartphones, a large number of users save privacy-related information in mobile devices, mainly smartphones, which puts forward higher demands on the protection of mobile users' privacy information. At present, mobile user authentication methods based on humancomputer interaction have been extensively studied due to their advantages of high precision and non-perception, but there are still shortcomings such as low data collection efficiency, untrustworthy participating nodes, and lack of practicability. To this end, this paper proposes a privacy-enhanced mobile user authentication method with motion sensors, which mainly includes: (1) Construct a smart contract-based private chain and federated learning to improve the data collection efficiency of mobile user authentication, reduce the probability of the model being bypassed by attackers, and reduce the overhead of data centralized processing and the risk of privacy leakage;(2) Use certificateless encryption to realize the authentication of the device to ensure the credibility of the client nodes participating in the calculation; (3) Combine Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) to analyze and model the motion sensor data of mobile devices to improve the accuracy of model certification. The experimental results on the real environment dataset of 1513 people show that the method proposed in this paper can effectively resist poisoning attacks while ensuring the accuracy and efficiency of mobile user authentication.