System and method for implementing efficient private identity

Abstract

A private identity system is configured to invoke an embedding layer that constructs encoded embeddings from plaintext identification inputs using pre-trained models. Identification inputs can be captured (e.g., biometric, face image, retinal scan, fingerprint, voice, health data, behavioral, etc.) in plaintext versions and transformed into encoded embeddings. The embeddings can be produced to be homomorphic one-way encryptions of the plaintext identification instances. According to some embodiments, the encoded embeddings can be used to identify an entity. The encoded embeddings can be stored in a vector database during enrollment, retrieved by querying the vector database during prediction, and used to establish a match to an identifier, identity, and/or entity. In further embodiments, vector indexes can be used to speed queries executed on the vector database, and achieve improved computation, reduced query speed, and improved identification latency relative to multiple AI model architectures (e.g., embedding and classifier architectures).

Dependencies are inferred from the claim text.