Screen Recording Sensitive Data Masking via OCR
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Solution Overview
Problem
Current screen recording systems either completely stop recording screens with potential sensitive information, leading to loss of valuable non-sensitive data or require manual removal, which is time-consuming and error-prone.
Innovation Solution
A method and system that utilize a masking unit to identify sensitive information on a screen through optical character recognition and other means, concealing it in real-time to generate a modified image for recording, while allowing non-sensitive information to be recorded.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If screen recording systems completely stop recording screens that potentially include sensitive information, then privacy protection is improved, but valuable non-sensitive information is lost
Solution Approach 1:
The screen is divided into multiple regions or elements, and the system processes each segment independently to identify and mask only the sensitive portions while preserving non-sensitive content. This allows selective recording where sensitive fields are obscured but other valuable information on the screen remains accessible.
Solution Approach 2:
Different parts of the screen are treated differently based on their content sensitivity. Sensitive information areas are masked or concealed, while non-sensitive areas are recorded normally. This local differentiation resolves the contradiction by protecting privacy where needed without discarding valuable data elsewhere on the screen.
2Object-affected harmful factors
If manual removal of sensitive information from recorded screens is performed, then privacy protection is improved, but time consumption and operational costs increase
Solution Approach 1:
The system automatically identifies, processes, and masks sensitive information without requiring manual intervention. The automated detection and processing mechanisms eliminate the need for human operators to manually review and redact sensitive data, significantly reducing time consumption while maintaining privacy protection.
Solution Approach 2:
Manual mechanical processes of reviewing and redacting sensitive information are replaced with automated computational systems that use pattern recognition, optical character recognition, and image processing algorithms to detect and mask sensitive data automatically, eliminating human labor requirements.
3Object-affected harmful factors
If manual processing is used to remove sensitive information, then privacy protection is improved, but error rates increase
Solution Approach 1:
Manual mechanical processing is replaced with automated computational systems that use algorithms for pattern recognition, optical character recognition, and image processing. These automated systems provide consistent, repeatable results without human error, improving reliability while maintaining privacy protection.
Solution Approach 2:
The system incorporates feedback mechanisms where processed images are reviewed and validated, allowing the system to learn from corrections and improve its accuracy. This feedback loop ensures high reliability in sensitive information detection and masking while maintaining consistent privacy protection.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables continuous recording of screens with sensitive information concealed, reducing data loss and operational costs by automating the process, thus improving privacy and recording efficiency.
Implementation Method 1
identifying the at least one part is based on optical character recognition (OCR) applied to the least one part
Data Source
AI summary
A system and method for concealing sensitive information may include: receiving a screenshot from an application; generating an application model that identifies the application by automatically determining a respective position or location of part(s) of the screenshot; determining a part of the screenshot includes sensitive information, automatically concealing the sensitive information in the screenshot based application model to thus generate a modified screenshot, and recording the modified screenshot.


