The Privacy Problem with Standard Face Recognition
Standard face recognition requires the server to receive and process your face image (or at minimum your face embedding). This creates a privacy exposure: the server has your biometric data during the processing window, and if the server is compromised or malicious, that data could be misused. For high-sensitivity applications, this exposure is unacceptable.
Privacy-preserving computation offers an alternative: performing the matching computation on encrypted data, so neither the server nor any eavesdropper has access to the raw biometric data at any point.
Homomorphic Encryption for Biometrics
Homomorphic encryption (HE) allows mathematical operations to be performed on ciphertext (encrypted data) and produces an encrypted result that, when decrypted, equals the result of performing the operation on the plaintext. For face matching, this means: encrypt your face embedding on your device, send the encrypted embedding to the server, have the server compute the similarity with encrypted celebrity embeddings, and return an encrypted result that only you can decrypt.
Current HE implementations are 100–1000x slower than plaintext computation, making real-time face matching via HE a research goal rather than a deployed reality. But for lower-frequency high-stakes applications (document verification, identity proofing), the performance trade-off is acceptable.