Advanced Biometric Template Protection: Tokenization & Homomorphic Encryption

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Biometric authentication offers convenience and security but creates significant privacy risks because biometric identifiers are unique, immutable, and potentially vulnerable even when conventionally hashed or stored. The passage presents tokenization as a way to replace biometric templates with non-sensitive tokens while keeping original information separately secured, and homomorphic encryption as a method for comparing or processing encrypted biometric data without exposing it in decrypted form. Used together, these technologies are described as a layered defense that protects data both at rest and during verification, supports compliance with privacy regulations such as GDPR and CCPA, and may enable privacy-preserving capabilities such as biometric search. Didit positions its AI-native, developer-focused identity platform as integrating or developing these measures for products including ID Verification, 1:1 Face Match, and passive and active liveness detection, while offering APIs, documentation, a free KYC tier, and demonstration access.

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