AI Inline Text Censoring for Fine-Grained Privacy
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Solution Overview
Problem
Existing systems for censoring text-based data lack dynamic, fine-grained control, often preventing document transmission due to the inability to handle sensitive information effectively, limiting their usefulness.
Innovation Solution
A censoring system using artificial intelligence to identify and censor specific target patterns within text-based data, replacing sensitive characters with substitute strings based on user permissions, allowing controlled transmission of censored data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional censoring systems prevent document transmission when sensitive data is detected, then security protection is improved, but document usability and communication effectiveness deteriorate
Solution Approach 1:
The patent extracts only the sensitive portions of text (specific characters or words matching target patterns) rather than censoring entire documents. The system identifies and removes only the harmful elements (sensitive data) while preserving the rest of the document content, enabling both security protection and document usability.
Solution Approach 2:
The patent applies different treatment to different parts of the text based on their sensitivity. Sensitive characters matching target patterns are censored, while non-sensitive characters are preserved. This localized approach allows the document to maintain usability in non-sensitive areas while providing security protection in sensitive areas.
2Reliability
If conventional censoring systems censor entire documents containing sensitive data, then security protection is improved, but information loss and communication effectiveness worsen
Solution Approach 1:
The system extracts and censors only the specific sensitive characters that match target patterns, rather than removing entire documents. This selective extraction minimizes information loss while maintaining security protection for the identified sensitive data.
Solution Approach 2:
The patent applies partial censoring action only to the extent necessary - censoring specific target pattern matches rather than entire documents. This partial action is sufficient to protect sensitive information while avoiding excessive censorship that would cause unnecessary information loss.
3Ease of operation
If conventional censoring systems use simple keyword filtering, then ease of operation is improved, but measurement precision and censorship accuracy deteriorate
Solution Approach 1:
The patent replaces simple mechanical keyword filtering with a computer-based model (machine learning/AI system) that can accurately identify target patterns. This substitution maintains ease of operation through automated processing while dramatically improving censorship accuracy by understanding context and pattern variations.
Solution Approach 2:
The system changes the parameters of the censorship process by using trained models with adjustable sensitivity thresholds and pattern matching criteria. This allows the system to maintain ease of operation while achieving high precision through configurable detection parameters and intelligent pattern recognition.
Data Source
AI summary
Systems and methods for censoring text-based data are provided. In some embodiments a censoring system may include at least one processor and at least one non-transitory memory storing application programming interface instructions. The censoring system may be configured to perform operations comprising storing a target pattern type and a computer-based model for identifying a target data pattern corresponding to a target pattern type within text based data. The censoring system may also be configured to receive text-based data by a server, and to retrieve the stored target pattern type to be censored in the text-based data. The censoring system may be configured to identify within the received text-based data, a target data pattern corresponding to the retrieved target pattern type. The censoring system may be configured to censor target characters within the identified target data pattern, and transmit the censored text-based data to a receiving party.