Homomorphic Encryption with Didit: Securing Biometric Data

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Homomorphic encryption enables computations on encrypted information so that decrypted results match those produced from unencrypted data, allowing biometric comparisons without exposing raw facial or fingerprint templates. This approach addresses risks in conventional biometric systems, where plaintext, hashed, or tokenized templates can be vulnerable to breaches, reverse engineering, brute-force attacks, or exposure during matching operations; because biometric identifiers cannot be changed after compromise, such risks can have lasting consequences. Although homomorphic encryption has historically imposed substantial computational costs that complicated real-time, large-scale verification, advances in encryption schemes, optimized algorithms, hardware acceleration, and cloud infrastructure have improved its commercial practicality. Didit describes integrating these techniques into its AI-native, modular identity platform, particularly for 1:1 Face Match and Face Search, alongside liveness detection, document verification, AML screening, and age estimation, with APIs and a no-code console intended to make privacy-focused identity verification more accessible.

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