NLP Masking for Anonymous Feedback Text
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Solution Overview
Problem
Existing feedback systems struggle to ensure anonymity, as subtle personal traits in free-form text responses can still identify respondents, even after removing explicit identifiers, limiting the effectiveness of anonymous feedback in business and medical contexts.
Innovation Solution
A method using natural language processing (NLP) algorithms, such as GPT-3, to generate alternative text that maintains the semantic characteristics of the original feedback while masking personal traits, allowing respondents to select anonymized text that replaces their original entries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If free-form text responses are collected for feedback, then the honesty and usefulness of feedback is improved, but personal traits in the text can still identify respondents, compromising anonymity
Solution Approach 1:
The patent extracts and removes personal traits from feedback text using NLP algorithms. The system identifies characteristics such as writing style, vocabulary patterns, and grammatical preferences that could identify respondents, then removes or masks these traits while preserving the core feedback content, thereby maintaining anonymity without losing feedback honesty.
Solution Approach 2:
The patent introduces an intermediary processing layer between feedback collection and analysis. This intermediary NLP-based masking system processes the original text through multiple stages: identifying personal traits, generating masked versions, and preserving semantic meaning. This intermediary layer ensures that feedback remains honest and useful while eliminating identification risks.
2Ease of operation
If explicit identifiers are removed from feedback, then basic anonymity is achieved, but subtle personal traits remain that can still identify respondents
Solution Approach 1:
The patent changes the parameters of text analysis from simple identifier detection to comprehensive linguistic feature analysis. The NLP algorithms examine multiple parameters including vocabulary diversity, sentence structure patterns, grammatical preferences, and stylistic markers. By analyzing and masking these linguistic parameters, the system achieves more effective anonymity while maintaining ease of operation through automated processing.
3Productivity
If traditional text masking methods are used, then processing speed is maintained, but personal traits are not effectively masked, reducing anonymity
Solution Approach 1:
The patent replaces traditional mechanical text masking methods (simple redaction or removal) with intelligent NLP-based processing. Instead of mechanically deleting text segments, the system uses natural language processing algorithms to understand and transform text while preserving meaning. This substitution enables effective personal trait masking without significantly compromising processing speed, as the NLP operations are optimized for efficiency.
Data Source
AI summary
A method, computer system, and a computer program product for masking identifying traits contained in response text is provided. Embodiments may include receiving a request to anonymize response text in response to a predefined respondent interaction, wherein the response text is generated by the respondent and then obtaining the response text, wherein the obtained response text has semantic characteristics. Next, the obtained response text may be input into a natural language processing (NLP) algorithm and thereafter receiving an alternative masking text as output from the NLP algorithm, wherein the received alternative masking text maintains the semantic characteristics of the obtained response text. Finally, the response text may be replaced with the received alternative masking text.