Context Analysis for Dynamic Authentication Timeout Management
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Solution Overview
Problem
Information handling systems, especially portable devices like smartphones and tablets, are vulnerable to unauthorized access due to short power timeouts, allowing thieves to access sensitive information even when the user is temporarily away, as the system remains authenticated for an extended period.
Innovation Solution
A system and method that utilize context analysis through integrated sensors to dynamically adjust security settings, enforcing selective locking of access based on user presence, location, and type of information, transitioning between nervous states to enhance security without interfering with user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the information handling system uses a short power timeout to conserve power, then power consumption is reduced, but security is compromised because thieves can access sensitive information during the authenticated period
Solution Approach 1:
The patent implements dynamic timeout adjustment based on context analysis. The system transitions between different nervous states (calm, alert, panicked) that correspond to different timeout values. When sensors detect normal conditions, the system maintains longer authenticated periods to conserve power. When sensors detect abnormal conditions (movement, light, proximity to other devices), the system dynamically shortens the timeout period, forcing re-authentication and preventing unauthorized access even if the device is stolen.
2Object-affected harmful factors
If the system locks access after a timeout period to secure information, then security is improved, but user convenience deteriorates because users must re-authenticate frequently
Solution Approach 1:
The system dynamically adjusts the timeout period based on contextual conditions rather than using a fixed timeout. In calm states with no sensor anomalies, the timeout is extended allowing users to work without frequent re-authentication. When sensors detect alert or panicked conditions, the timeout is shortened or immediately triggered to lock access. This dynamic approach balances security needs with user convenience by adapting to the actual risk level.
Solution Approach 2:
The system continuously monitors sensor data (accelerometer, light sensor, proximity sensor, gyroscope) and uses this feedback to adjust authentication behavior. The context engine analyzes sensor patterns to determine whether the device is in a safe state or at risk of theft. This feedback loop allows the system to automatically adjust timeout periods and authentication requirements based on real-time conditions, preventing both premature lockouts and security vulnerabilities.
3Measurement precision
If the system monitors sensor context continuously to detect theft attempts, then security detection capability is improved, but power consumption increases
Solution Approach 1:
The system implements periodic sensor sampling rather than continuous monitoring. The context engine checks sensor data at intervals and transitions between nervous states based on detected conditions. This periodic approach maintains adequate theft detection capability while significantly reducing power consumption compared to continuous monitoring. The system can increase sampling frequency when transitioning to alert or panicked states and reduce frequency during calm states.
Solution Approach 2:
The monitoring intensity is dynamically adjusted based on the current nervous state. During calm states, the system uses lower-power monitoring with longer intervals between sensor checks. When the system transitions to alert or panicked states due to sensor anomalies, monitoring intensity increases with more frequent checks and lower threshold triggers. This dynamic monitoring strategy maintains security effectiveness while optimizing power consumption based on actual risk levels.
Data Source
AI summary
Context captured with sensors of an information handling system is applied to selectively lock access to currently unlocked information, with conditions for locking access based upon the context. Nervous states enforce locking of selected information based upon the confidence of the security of the information under sensed external conditions. Increased sensitivity for locking access includes reduced timeouts to a lock command, increased response to sensed conditions, and more rapid response where unlocked access is to sensitive information.