Secure Tokenized Credentials with Compressed Biometric Verification
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Solution Overview
Problem
Traditional authentication methods provide either too much information, making them vulnerable to forgery, or too little, lacking secure validation mechanisms, and often require additional verification steps due to the lack of cryptographic obfuscation.
Innovation Solution
The use of digitally signed secure tokens that incorporate biometric verification credentials, such as 2D/3D facial representations and speech patterns, combined with cryptographic keys, to enhance authentication by reducing data storage and processing requirements while improving replication difficulty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional authentication methods (e.g., driver's license with photo) are used to provide verification credentials, then the verifier receives sufficient information for validation, but the credentials become vulnerable to forgery and require additional verification steps
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing cryptographic proofs (zero-knowledge proofs, digital signatures) and biometric templates in the credential during issuance. This allows the verifying device to authenticate credentials through simple local verification without requiring complex validation processes or additional verification steps.
Solution Approach 2:
The patent introduces cryptographic intermediaries (zero-knowledge proofs, digital signatures, hash functions) that mediate between the credential holder and verifier. These cryptographic mechanisms enable secure verification without revealing sensitive information, eliminating the need for additional verification steps while maintaining high security.
2Measurement precision
If detailed biometric data is stored in credentials to improve verification accuracy, then authentication precision increases, but storage requirements and processing complexity increase
Solution Approach 1:
The system extracts only the essential biometric features needed for verification (e.g., facial landmarks, gait parameters) and stores them as compressed templates rather than full biometric datasets. This extraction approach maintains verification accuracy while significantly reducing storage requirements and processing complexity.
Solution Approach 2:
The patent transforms biometric data into different parameter representations through cryptographic hashing and feature extraction. By changing the parameter space from raw biometric images to compressed feature vectors, the system achieves both high verification accuracy and efficient storage with reduced data dimensions.
3Reliability
If cryptographic obfuscation is applied to protect credential data, then security against forgery improves, but processing and validation complexity increases
Solution Approach 1:
The system performs preliminary cryptographic operations during credential issuance, pre-computing zero-knowledge proofs and digital signatures. This shifts the computational complexity to the issuance phase, allowing the verifying device to perform simple local verification without requiring complex cryptographic processing during authentication.
Solution Approach 2:
The patent uses cryptographic copying mechanisms where the original credential data never leaves the holder's device. Instead, cryptographic proofs (copies of verification capability) are generated and presented to verifiers. This approach maintains strong forgery resistance while simplifying the verifier's processing requirements.
Data Source
AI summary
Systems, devices, methods, and computer readable media are provided in various embodiments having regard to authentication using secure tokens, in accordance with various embodiments. An individual's personal information is encapsulated into transformed digitally signed tokens, which can then be stored in a secure data storage (e.g., a “personal information bank”). The digitally signed tokens can include blended characteristics of the individual (e.g., 2D/3D facial representation, speech patterns) that are combined with digital signatures obtained from cryptographic keys (e.g., private keys) associated with corroborating trusted entities (e.g., a government, a bank) or organizations of which the individual purports to be a member of (e.g., a dog-walking service).