Practical Privacy-Preserving Face Identification Based on Function-Hiding Functional Encryption.

Leveraging on function-hiding Functional Encryption (FE) and inner-product-based matching, this work presents a practical privacy-preserving face identification system with two key novelties: switching functionalities of encryption and key generation algorithms of FE to optimize matching latency while maintaining its security guarantees, and identifying output leakage to later formalize two new attacks based on it with appropriate countermeasures. We validate our scheme in a realistic face matching scenario, attesting its applicability to pseudo real-time one-use face identification scenarios like passenger identification.