CPC G06F 21/32 (2013.01) [G06F 18/214 (2023.01); G06N 3/04 (2013.01); G06V 10/772 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 10/993 (2022.01); G06V 40/12 (2022.01); G06V 40/16 (2022.01); G06V 40/40 (2022.01); H04L 63/0236 (2013.01); H04L 63/1416 (2013.01); H04L 63/1466 (2013.01)] | 18 Claims |
1. An authentication system for privacy-enabled authentication, the system comprising:
at least one processor operatively connected to a memory;
an authentication data gateway, executed by the at least one processor, configured to filter identification information used in enrollment, identification, or authentication functions of subsequent neural networks, the authentication data gateway comprising at least a plurality of pre-trained validation helper networks, including:
a first pre-trained validation helper network associated with identification information of a first type, wherein the first pre-trained validation helper network is configured to:
evaluate an unknown identification sample of the first type, responsive to input of the unknown identification sample of the first type to the first pre-trained validation helper network, wherein evaluation is based on evaluation criteria that is independent of a subject of the identification information seeking to be enrolled, identified, or authenticated;
wherein the authentication data gateway is further configured to:
validate the unknown information sample for use in subsequent enrollment, identification, or authentication, responsive to a determination that the unknown identification sample meets the evaluation criteria;
reject the unknown identification sample for use in subsequent enrollment, identification, or authentication, responsive to a determination that the unknown identification sample fails the evaluation criteria; and
as part of validation or rejection, execute at least an evaluation of the unknown identification sample that includes generation of an output probability by the first pre-trained validation helper network that the unknown identification sample is valid or invalid;
wherein the plurality of pre-trained validation helper networks are associated with respective identification information types and the plurality of validation helper networks are trained to generate an evaluation of an unknown identification sample of the respective identification information type and output a probability the respective unknown identification samples is valid or invalid.
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