A lattice-integrated AES framework for ultra-secure biometric protection on resource-constrained edge devices.

In recent technological advancements, consumer electronics such as mobile phones and edge devices have expanded rapidly. Still, critical security issues related to the confidentiality and integrity of user data is persistent. Over 1.2 billion consumer devices are in use, with varying user data. Still the secrecy of user biometric information, such as fingerprints, voice, and facial mapping is being compromised, leading to significant losses for consumers and their valuable savings. To overcome this, the study introduces a fingerprint-template protection framework to keep user biometric information safe on consumer electronic devices, preventing tampering with user data. The proposed methodology is lightweight and runs smoothly on resource-constrained devices, with an effective feature extraction and normalization of user fingerprint data. The model utilizes AES-256 Galois counter mode (GCM) with PIN oriented keys and a lightweight lattice-based randomness generator to secure user information. The proposed framework achieves a fake fingerprint detection accuracy of 98.69% with minimum turnaround for encryption and decryption, compared with other state-of-the-art methods such as RC5 (59.6%), ElGamal (91.1%), and RSA (92.35%). The experimental results show that the proposed framework is superior in both security and data accessibility. Integration of lightweight lattice-based randomness generators in consumer electronics improves security and enables more reliable on-device applications.