Sequential Authentication Using Continuous Error Scores
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Solution Overview
Problem
Existing user authentication techniques often rely on binary decisions, lacking the ability to provide continuous-value scores for biometric and knowledge verification, which limits their adaptability and integration with multiple verification methods.
Innovation Solution
A sequential authentication system that uses log likelihood ratios and error probabilities to generate continuous scores for each challenge, allowing for cumulative authentication results and adaptation based on field data and security breaches, integrating with biometric and possession-based authentication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If binary decision rules are used for authentication challenges, then the decision-making process is simple and fast, but the system lacks adaptability and cannot provide continuous-value scores for integration with multiple verification methods
Solution Approach 1:
The patent changes the parameter type from binary (0/1) to continuous values (log-likelihood ratios). Instead of simple accept/reject decisions, the system computes continuous scores that can take any real value, enabling fine-grained differentiation between authentication results and facilitating integration with multiple verification methods through weighted combination of continuous scores.
Solution Approach 2:
The patent introduces dynamic adjustment capabilities where error rates and challenge characteristics can be adapted based on field data and security breach information. The system dynamically updates authentication parameters and weights to respond to changing security conditions, improving adaptability without requiring complete system redesign.
2Reliability
If multiple authentication challenges are presented sequentially, then authentication accuracy and security are improved, but the authentication process becomes more time-consuming and complex
Solution Approach 1:
The patent maintains continuous authentication evaluation throughout the challenge sequence. Instead of discrete binary decisions at each step, the system continuously accumulates log-likelihood ratio scores, allowing for smoother, more efficient authentication flow that can stop early if confidence is high or continue adaptively based on running totals rather than fixed steps.
Solution Approach 2:
The system incorporates feedback mechanisms where authentication results from each challenge inform subsequent challenge selection and weighting. The continuous scoring system provides running feedback on authentication confidence, allowing the system to adjust the number and type of challenges dynamically based on accumulated evidence rather than following a fixed rigid sequence.
3Reliability
If error rates are adapted based on field data and security breaches, then authentication robustness is improved, but system complexity and data management requirements increase
Solution Approach 1:
The system performs self-adjustment by automatically updating error rate parameters based on observed field data and security breach patterns. Rather than requiring manual reconfiguration, the system self-calibrates its authentication parameters using accumulated operational data, improving robustness while minimizing operational complexity through automated processes.
Solution Approach 2:
The patent changes static error rate parameters into dynamic, adaptively updated parameters. Error rates are no longer fixed values but evolve based on field observations and security incident data, allowing the system to automatically adjust to changing threat landscapes and improve robustness without requiring complex manual intervention for each adjustment.
Data Source
AI summary
Methods and apparatus are provided for sequential authentication of a user that employ one or more error rates characterizing each security challenge. According to one aspect of the invention, a user is challenged with at least one knowledge challenge to obtain an intermediate authentication result; and the user challenges continue until a cumulative authentication result satisfies one or more criteria. The intermediate authentication result is based, for example, on one or more of false accept and false reject error probabilities for each knowledge challenge. A false accept error probability describes a probability of a different user answering the knowledge challenge correctly. A false reject error probability describes a probability of a genuine user not answering the knowledge challenge correctly. The false accept and false reject error probabilities can be adapted based on field data or known information about a given challenge.