A Generic Model for Privacy-Preserving Authentication on Smartphones

With the increasing use of biometrics for user authentication especially on mobile devices, its privacy and resource requirements are becoming big challenges to consider. In this paper, we propose a generic model for privacy-preserving yet accurate authentication on smartphones using the mobile matching on card (MMOC) technique and transfer learning. MMOC technique takes advantage of SIM cards as a secure element (SE) on smartphones to increase the security and privacy of user verification with low performance overhead. In order to improve the performance accuracy of the system, we use transfer learning and fine-tune a network suitable for implementation on off-the-shelf SIM cards available on smartphones. The classification sub-network is migrated to the SIM card for a lightweight and secure user verification. However, the implementation of classification sub-network on constrained resource smart cards with high accuracy and efficiency is a challenging task. We propose log quantization scheme and an on-card optimization architecture to speed-up the forward pass of the sub-network and retain the system’s accuracy close to the original model with low memory footprint and real-time verification response. Using a public mobile face dataset, we evaluate our privacy-preserving verification system. Our results show that the proposed system achieves Equal Error Rate (EER) of 0.4%-2% in real-time, with response time of 1.5 seconds.