Error Tolerant Verification for Secure Product Identifiers
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Solution Overview
Problem
Existing authentication systems face errors when humans or optical recognition is used for reading or transcribing non-machine readable codes, particularly due to similarities between characters, leading to potential security breaches.
Innovation Solution
A system that allows for automatic correction of certain errors by defining subsets of similar characters and using transformation to generate identities, verifying user-entered codes against true codes, while maintaining security through predetermined error thresholds and probability scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If strict adherence to character recognition is enforced for security, then authentication security is improved, but error rate increases when humans or optical recognition are used
Solution Approach 1:
The system performs preliminary error correction by defining equivalence classes of similar characters and applying transformation rules before final verification. This anticipates potential recognition errors and corrects them in advance, allowing the system to accept both strict and lenient interpretations of character codes.
Solution Approach 2:
The invention changes the verification parameter from exact character matching to equivalence class matching. By transforming the verification criterion to allow certain types of errors (within defined equivalence classes), the system maintains security while accommodating human and optical recognition limitations.
2Reliability
If similar characters are allowed to be entered, then error tolerance is improved, but the number of possible combinations decreases
Solution Approach 1:
The character set is segmented into equivalence classes where certain characters are grouped together based on similarity. This segmentation allows the system to tolerate errors within each class while maintaining distinct verification across different classes, preserving overall code space.
Solution Approach 2:
The equivalence class transformation acts as an intermediary between the entered code and the verification process. This intermediary layer allows flexible interpretation of similar characters while maintaining the integrity of the verification system through defined transformation rules.
3Reliability
If error correction is implemented, then authentication reliability is improved, but system complexity increases
Solution Approach 1:
The system establishes equivalence classes and transformation rules in advance during system initialization. This preliminary setup allows the verification process to simply apply pre-defined rules rather than performing complex real-time analysis, reducing operational complexity.
Solution Approach 2:
The verification process is simplified by changing from complex real-time error analysis to application of pre-defined transformation rules. This parameter change from adaptive complex verification to rule-based verification reduces system complexity while maintaining error correction capabilities.
Data Source
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AI summary
The invention relates to a system of automatic correction where certain errors are accepted while others are recognized as errors. This applies specifically to non-machine readable inputs or to optical character recognition when reading or transcribing codes. When entering certain characters, this system would allow certain similar character combinations and number of errors from that set while still maintaining integrity of identification codes.