Generating Privacy-Preserving Faces for Multi-Party Secure Authentication

Summary

Researchers propose a facial privacy framework that lets users authenticate for scenarios such as smart building access without exposing their real face or identity in plaintext. The design uses a bidirectional mapping mechanism with an identity mapping module that generates a virtual identity (VID), an identity transfer model that produces a virtual face, and a VIEM module that compares identities in the encrypted domain via homomorphic encryption. Experiments reportedly show a balance between privacy protection, attribute preservation and authentication accuracy.