Keystroke Biometric User Identification via Composite Statistical Analysis
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Solution Overview
Problem
Existing methods for user identification on electronic devices using keystroke biometrics are limited by their reliance on first-order statistical techniques and require explicit initialization with a fixed set of keys, making them inconvenient and less effective for free text entry.
Innovation Solution
A system that uses a combination of first- and second-order statistical techniques, including Chi-Square analysis, to infer the identity of a user by monitoring and characterizing keypress activity, such as timing between key presses, and matching it against user biometric models, allowing for incremental and ambiguous text input without explicit initialization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If first-order statistical techniques are used for keystroke biometrics, then the system is simpler to implement, but the identification accuracy is reduced
Solution Approach 1:
The patent segments the keystroke analysis into multiple independent statistical components (first-order statistics like mean and variance, second-order statistics like autocorrelation). Each component captures different aspects of typing behavior, and their combination provides comprehensive user identification without requiring overly complex algorithms.
Solution Approach 2:
The patent combines multiple statistical techniques (first-order and second-order methods) to create a composite biometric analysis system. This composite approach integrates different statistical perspectives to achieve higher identification accuracy than any single method could provide alone.
2Measurement precision
If explicit initialization with fixed keys is required, then the biometric model is more accurate, but the ease of operation decreases
Solution Approach 1:
The system performs preliminary biometric model creation during an initial setup phase where users type fixed keys. This preliminary action establishes accurate baseline models that will be used for all subsequent identification operations, separating the accuracy-critical modeling phase from the convenience-critical usage phase.
Solution Approach 2:
The biometric model is made dynamic and adaptive. After initial creation using fixed keys, the model can be updated and refined as the user continues to interact with the device, allowing the system to maintain accuracy while becoming more convenient over time.
3Ease of operation
If free text entry is allowed without fixed key sequences, then the ease of operation improves, but the reliability of biometric identification decreases
Solution Approach 1:
The biometric system is designed to be universal and work with any text input method (fixed sequences, free text, sentences, words). The statistical analysis framework extracts identifying patterns regardless of the specific input format, making the system reliable across multiple usage scenarios.
Solution Approach 2:
The system adapts its analysis parameters based on the input type. For free text entry, it adjusts the statistical parameters and thresholds to account for the greater variability in typing patterns, maintaining identification reliability while accommodating flexible input methods.
4Measurement precision
If multiple statistical techniques are combined, then the identification accuracy improves, but the device complexity increases
Solution Approach 1:
The complex statistical analysis is segmented into distinct, manageable components (first-order statistics, second-order statistics, feature extraction, pattern matching). Each segment can be implemented and optimized independently, reducing overall system complexity while maintaining comprehensive analysis capabilities.
Data Source
AI summary
Methods of and systems for selecting and presenting content based on user identification are provided. A user-interface method of selecting and presenting content items in which the presentation is ordered at least in part based on inferring which user of a collection of users is using an input device includes providing a set of content items, providing a set of preference information for each user indicating content item preferences of a corresponding user, and providing a set of user keypress biometric models representing expected keypress activity for the corresponding user. User keypress activity to identify desired content items is monitored to biometrically characterize the user and analyzed to find the closest match to one of the keypress biometric models. Based on the closest match, which user of the collection of users entered the input is inferred and the corresponding preference information is used to select, order, and present content items.