Systems and processes for lossy biometric representations
Systems and processes for improved processing of biometric data may include a hash controller including a processor, a server, and a registry. The hash controller can receive biometric information, such as a biometric scan, and apply an EGH transformation to convert the biometric information into an irreversible, unlinkable, and revocable EgHash. The EGH transformation can include blending biometric information with non-biometric information and permuting the biometric representation for additional security. The permuted biometric representation can be projected based on a randomly generated matrix and the output permuted to obtain an EgHash. The resultant EgHash can be lossy such that the EGH transform causes an irreversible loss of biometric information between the original biometric information and the EgHash. The EgHash can be compared and retrieved at speed and scale by the processor to support operations including, but not limited to, verification, identification, and database deduplication.
1. A process comprising:
obtaining a biometric representation of a subject, wherein the biometric representation was encoded from a biometric sample via a neural network;
projecting the biometric representation based on one or more lossy transformation parameters to generate an anonymized vector representation;
receiving a second biometric representation associated with the subject;
generating a second anonymized vector representation based on the second biometric representation; and
performing a comparison between the anonymized vector representation and the second anonymized vector representation to determine if the second anonymized vector representation is within a similarity threshold of the anonymized vector representation.
2. The process of claim 1 , wherein a vector dimension of the anonymized vector representation is less than a vector dimension of the biometric representation.
3. The process of claim 1 , wherein a vector dimension of the anonymized vector representation is equal to a vector dimension of the biometric representation.
4. The process of claim 1 , further comprising:
generating a random seed; and
prior to projecting the biometric representation, generating the one or more lossy transformation parameters based on the random seed.
5. The process of claim 4 , wherein the random seed is generated based on a key.
6. The process of claim 5 , wherein the key is a unique key received from a user.
7. The process of claim 5 , wherein the key is a common key associated with a particular organization and is received with the biometric representation.
8. A system comprising:
at least one processor; and
a non-transitory, machine-readable memory device comprising instructions stored thereon that, when executed by the at least one processor, cause the at least one processor to:
obtain a biometric representation of a subject, wherein the biometric representation was encoded from a biometric sample via a neural network;
project the biometric representation based on one or more lossy transformation parameters to generate an anonymized vector representation;
receive a second biometric representation associated with the subject;
generate a second anonymized vector representation based on the second biometric representation; and
perform a comparison between the anonymized vector representation and the second anonymized vector representation to determine if the second anonymized vector representation is within a similarity threshold of the anonymized vector representation.
9. The system of claim 8 , wherein a vector dimension of the anonymized vector representation is less than a vector dimension of the biometric representation.
10. The system of claim 8 , wherein a vector dimension of the anonymized vector representation is equal to a vector dimension of the biometric representation.
11. The system of claim 8 , further comprising:
generating a random seed; and
prior to projecting the biometric representation, generating the one or more lossy transformation parameters based on the random seed.
12. The system of claim 11 , wherein the random seed is generated based on a key.
13. The system of claim 12 , wherein the key is a unique key received from a user.
14. The system of claim 12 , wherein the key is a common key associated with a particular organization and is received with the biometric representation.
15. A non-transitory, computer-readable medium comprising instructions that, when executed by a computer, cause the computer to:
obtain a biometric representation of a subject, wherein the biometric representation was encoded from a biometric sample via a neural network;
project the biometric representation based on one or more lossy transformation parameters to generate an anonymized vector representation;
receive a second biometric representation associated with the subject;
generate a second anonymized vector representation based on the second biometric representation; and
perform a comparison between the anonymized vector representation and the second anonymized vector representation to determine if the second anonymized vector representation is within a similarity threshold of the anonymized vector representation.
16. The non-transitory, computer-readable medium of claim 15 , wherein a vector dimension of the anonymized vector representation is less than a vector dimension of the biometric representation.
17. The non-transitory, computer-readable medium of claim 15 , wherein a vector dimension of the anonymized vector representation is equal to a vector dimension of the biometric representation.
18. The non-transitory, computer-readable medium of claim 15 , wherein the instructions, when executed by the computer, cause the computer to:
generate a random seed; and
prior to projecting the biometric representation, generate the one or more lossy transformation parameters based on the random seed.
19. The non-transitory, computer-readable medium of claim 18 , wherein the random seed is generated based on a key.
20. The non-transitory, computer-readable medium of claim 19 , wherein the key is a unique key received from a user.