Touch of Privacy: A Homomorphic Encryption-Powered Deep Learning Framework for Fingerprint Authentication

Deep learning and fully homomorphic encryption (FHE) are integrated for privacy-preserving fingerprint recognition. Convolutional neural network (CNN) extract fingerprint features encrypted using the Cheon-Kim-Kim-Song (CKKS) FHE scheme. TenSEAL ensures all computations occur in the encrypted domain, preventing raw biometric data exposure during authentication. A subset of the SOCOFing dataset, comprising 7,200 altered fingerprints from 90 individuals across three difficulty levels, is used for training (80%), validation (10%), and testing (10%). One real fingerprint per user is encrypted and stored for authentication, reducing computational complexity. The CNN classifies encrypted features without decryption, ensuring secure authentication. The system, utilizing Euclidean similarity, achieves 99.06% test accuracy with a loss of 0.1692, a True Acceptance Rate (TAR) of 99.19%, a False Rejection Rate (FRR) of 0.81%, a False Acceptance Rate (FAR) of 0%, and a True Rejection Rate (TRR) of 100%, with a minimal Equal Error Rate (EER) of 0.40%. Encrypted templates require only 31 MB for 90 users, averaging 344 KB per fingerprint per user. Despite a $157.26\times $ encryption overhead, remains feasible for real-time applications, completing an encrypted comparison in 0.025 seconds with a total processing time of 0.136 seconds per fingerprint. Adhering to ISO/IEC 24745 biometric protection standards, the system ensures irreversibility, unlinkability, and renewability of biometric templates. Decryption occurs only on the client side, keeping raw fingerprint data inaccessible to the server. The results confirm the feasibility of FHE for secure fingerprint authentication in cloud-based environments, benefiting applications in healthcare, banking, and access control.