Natural Language Text Obscuring With Author-Content Separation
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Solution Overview
Problem
Existing differential privacy techniques for natural language text often result in output data that is not human-readable or maintains the same length as the input, and author identification can be achieved through metadata and writing style, compromising user anonymity.
Innovation Solution
An autoencoder arrangement using a variational autoencoder (VAE) disentangles author features from content features in a latent space, applying differential privacy constraints to the author latent vectors while preserving content meaning, generating obscured text through a decoder model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If differential privacy techniques are applied to natural language text, then user anonymity is improved, but output human-readability deteriorates
Solution Approach 1:
The patent introduces an autoencoder model as an intermediary between the input text and the differential privacy mechanism. The encoder transforms the input text into a latent space representation, where differential privacy noise is added to obscure author-specific features while preserving content meaning. The decoder then transforms this noisy latent representation back into human-readable text, resolving the contradiction between anonymity and readability.
Solution Approach 2:
The patent modifies the latent space parameters of the text representation by adding controlled noise to author-specific features while preserving content-related features. This parameter transformation in the latent space allows the system to achieve differential privacy guarantees while maintaining output text that remains human-readable and meaningful.
2Reliability
If differential privacy techniques are applied to natural language text, then user anonymity is improved, but output length consistency deteriorates
Solution Approach 1:
The autoencoder model serves as an intermediary that handles the transformation from input text to obscured output text. The decoder component specifically addresses length variation by learning to reconstruct text with appropriate length characteristics from the noisy latent representation, thus maintaining reasonable output length consistency while preserving anonymity.
3Reliability
If author features are obscured in natural language text, then user anonymity is improved, but content meaning preservation deteriorates
Solution Approach 1:
The patent segments the latent space representation into different feature components, including author-specific features and content-related features. By applying differential privacy noise selectively to author features while preserving content features, the system achieves anonymity without sacrificing content meaning. This segmentation allows independent handling of anonymity and content preservation objectives.
Solution Approach 2:
The patent applies parameter transformations that selectively modify author-specific parameters in the latent space while leaving content-related parameters unchanged. This selective parameter modification ensures that anonymity is achieved through obscuring author features while content meaning is preserved through maintaining content feature integrity.
4Reliability
If existing differential privacy techniques are applied to natural language text, then privacy protection is improved, but output quality deteriorates
Solution Approach 1:
The autoencoder model acts as an intermediary that processes the differential privacy mechanism's output. The decoder transforms the noisy latent representation into high-quality human-readable text, effectively filtering and reconstructing the output to maintain quality standards while preserving the privacy protection achieved through differential privacy.
Solution Approach 2:
The patent applies controlled parameter changes in the latent space that balance privacy protection and output quality. By carefully managing the noise addition process and the subsequent decoding transformation, the system achieves differential privacy guarantees while maintaining output text quality that is suitable for human consumption and further processing.
Data Source
AI summary
Various examples are directed to systems and methods for obscuring natural language data. A programmed computer may access natural language data comprising a first plurality of sequenced tokens. The programmed computer may apply an encoder model to the first plurality of sequenced tokens to generate a latent space representation of the first plurality of sequenced tokens, the latent space representation comprising a first content latent vector describing a content of the natural language data and a first author latent vector describing an author of the natural language data. The programmed computer may modify the first author latent vector to generate an obscured author latent vector. The programmed computer may apply a decoder model to the first content latent vector and the obscured author latent vector to generate obscured natural language data, the obscured natural language data comprising a second plurality of tokens.