Duress Detection via Biometric Analysis for Account Security
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Solution Overview
Problem
Mobile device users are vulnerable to unauthorized access to their personal and financial data when under duress, leading to potential theft and fraud, as existing technologies lack effective measures to secure account information in such situations.
Innovation Solution
The system collects and analyzes biometric data to detect when a user is under duress, altering the presentation of account information on devices like smartphones and ATMs to prevent unauthorized access, by either omitting or falsifying account details, and sending alerts to limit transactions or notify authorities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If biometric data collection and analysis is implemented to detect user duress, then account security against unauthorized access is improved, but device complexity and processing requirements increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting biometric data and establishing baseline physiological patterns before a duress event occurs. This allows the system to quickly detect anomalies without requiring complex real-time analysis during critical moments, thereby improving security while managing system complexity through advance preparation.
Solution Approach 2:
The patent introduces an intermediary layer between the user and account information by implementing a biometric monitoring system that acts as a mediator. This intermediary analyzes physiological data and intervenes when duress is detected, altering or hiding account information without requiring direct user action, thus enhancing security while maintaining manageable system architecture.
2Measurement precision
If biometric sensors and continuous monitoring are deployed, then detection of duress states is improved, but energy consumption and computational resources increase
Solution Approach 1:
The system employs periodic action by collecting biometric data at regular intervals rather than continuously, and by periodically comparing current readings against established baselines. This approach maintains high duress detection accuracy while significantly reducing energy consumption and computational resource requirements compared to continuous real-time monitoring.
Solution Approach 2:
The patent applies partial action by focusing monitoring efforts on specific physiological indicators most relevant to duress detection rather than analyzing all biometric parameters continuously. The system activates full monitoring capabilities only when anomalies are detected, using partial monitoring during normal states to conserve energy while maintaining detection precision.
3Object-affected harmful factors
If account information is altered or hidden when duress is detected, then protection against theft and fraud is improved, but loss of information access for legitimate users occurs
Solution Approach 1:
The system implements dynamics by making account information presentation adaptive and conditional rather than static. Information is dynamically altered or hidden based on real-time biometric assessment of user state, allowing full access during normal conditions while providing protection during duress events. This dynamic approach prevents theft and fraud without permanently restricting legitimate information access.
Solution Approach 2:
The patent applies parameter changes by modifying the presentation parameters of account information based on detected user state. When duress is detected, parameters such as information visibility, detail level, or access permissions are changed to protect against theft. When normal state is confirmed, parameters return to normal, ensuring legitimate users maintain full information accessibility.
Data Source
AI summary
Methods and systems for account access security at a network access device. The method includes receiving data indicating that a particular user has initiated a particular request at an access point; obtaining sensor data that is generated by one or more sensors of the access point proximate to a time that the particular user initiated the particular request; classifying the particular request as a particular type of request; in response to determining that the particular request is classified as the particular type of request, initiating an exception processing mode in which requests that are initiated by the users at the access point result in the generation and output of inaccurate completion data to mimic completion of the request; processing the particular request using the exception processing mode; and providing the inaccurate completion data generated by the access point for the particular request.