Inline Text Censorship Using AI Pattern-Based Character Masking
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Solution Overview
Problem
Existing systems struggle with dynamic, fine-grained control over the censorship of sensitive text data during electronic communication, often leading to incomplete or overly restrictive document transmission.
Innovation Solution
A system utilizing artificial intelligence to identify and censor specific target characters within text data, applying computer-based models to replace sensitive information with substitute characters based on user permissions and security levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional censoring systems are used to identify and prevent transmission of documents containing sensitive data, then security protection is improved, but document transmission capability deteriorates
Solution Approach 1:
The system segments the document into individual characters and applies censorship selectively to only those characters that match sensitive data patterns, rather than censoring entire documents or sections. This allows legitimate content to be transmitted while protecting sensitive information.
Solution Approach 2:
The system applies different treatment to different parts of the document based on their sensitivity. Sensitive characters are censored while non-sensitive characters are transmitted normally, creating local quality variation in the censorship application.
2Reliability
If all text data is censored to ensure complete security, then security protection is improved, but information utility deteriorates
Solution Approach 1:
The system extracts only the sensitive characters from the document using pattern matching, and censors only those extracted characters. This selective extraction approach preserves information utility by leaving non-sensitive content intact while still providing security protection.
Solution Approach 2:
The system applies partial censorship action by targeting only the specific characters that match sensitive data patterns rather than applying censorship to all text. This partial action maintains the balance between security and information utility.
3Speed
If simple pattern matching is used for censorship, then processing speed is improved, but censorship precision deteriorates
Solution Approach 1:
The system changes the parameter of pattern matching from simple string comparison to multi-level pattern recognition that considers character sequences, formats, and contextual patterns. This enhances censorship precision while maintaining processing speed through efficient pattern algorithms.
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.