Fingerprint Digital Twin for Secure and Privacy Preserving Biometric Authentication

Rishabh Shukla, Harkeerat Kaur, Isao Echizen, Pritee Khanna

Abstract: This work proposes a novel application of digital twins in the field of biometric security. Biometric systems have become widespread but their use carries risks of privacy invasion attacks due to the sensitive nature of biometric data. To address these concerns, we propose creating biometric clones for digital access and authentication systems. A user's fingerprint can act as a virtual representation or cyberproxy, allowing users to exist in the digital world with a unique, changeable, and privacy-preserving identity. The digital twin or clone fingerprint is generated using deep neural networks combined with a user-specific token/key. This approach allows third parties to process and store the proxy biometrics without putting the user's personal information at risk. We suggest that this approach could provide a safer and more secure alternative to traditional biometric security systems.