Neural Network Data Transformation for Privacy Utility Trade-off
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Solution Overview
Problem
The use of large datasets in AI/ML operations raises ethical concerns regarding individual privacy and personal data, as many datasets contain sensitive information that owners may withhold or obfuscate, reducing the utility of the data.
Innovation Solution
The implementation of a machine learning model that includes a data transformation module, a sensitive attribute suppression module, an annotated useful attribute preservation module, and a generic feature suppression module, which work together to transform the dataset, suppress sensitive attributes, preserve useful attributes, and minimize generic feature information, thereby enhancing data utility while maintaining privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If data owners withhold or obfuscate sensitive data to protect privacy, then individual privacy is improved, but the utility of the released data deteriorates
Solution Approach 1:
The patent extracts and removes sensitive attributes from the dataset through a dedicated suppression module. The system identifies sensitive attributes (e.g., race, gender, age) and systematically removes them from the training data, transforming the original dataset into a脱敏版本 that retains useful information while eliminating privacy risks. This extraction approach resolves the contradiction by separating sensitive information from useful patterns.
Solution Approach 2:
The patent transforms the data representation by changing parameters through neural network transformations. The system applies learned transformations to modify the data structure and attribute representations, converting sensitive attributes into suppressed forms while preserving useful patterns. This parameter transformation enables the data to maintain utility without exposing sensitive information.
2Reliability
If data owners release large datasets to improve AI/ML model performance, then model generalization is improved, but ethical concerns regarding privacy deteriorate
Solution Approach 1:
The patent segments the dataset processing into distinct functional modules: a data transformation module for general pattern learning, a sensitive attribute suppression module for privacy protection, and a utility preservation module for maintaining data usefulness. This segmentation allows the system to simultaneously achieve model generalization through large-scale data processing while protecting privacy through targeted suppression of sensitive attributes.
Solution Approach 2:
The patent introduces an intermediary processing system between the original dataset and the training process. This intermediary system (comprising multiple neural networks working together) transforms the data to remove sensitive information while preserving useful patterns, acting as a mediator that reconciles the conflicting goals of model performance and privacy protection.
3Object-affected harmful factors
If generic features are suppressed to protect unannotated sensitive information, then privacy protection is improved, but the complexity of the data transformation process deteriorates
Solution Approach 1:
The patent implements a self-service mechanism where the system automatically identifies and suppresses generic features without requiring manual annotation of all sensitive attributes. The neural network-based approach enables the system to autonomously learn which features to suppress based on patterns in the data, reducing the need for complex manual configuration while maintaining comprehensive privacy protection.
Data Source
AI summary
A method may include: accepting, by a data transformation module, an original dataset as input to a first and a second neural network and outputting a transformed dataset; accepting, by a sensitive attribute suppression module, the transformed dataset as input to a third neural network and calculating a sensitive attribute suppression loss; accepting, by an annotated useful attribute preservation module, the transformed dataset as input to a fourth neural network and calculating a useful attribute preservation loss; accepting by a generic feature suppression module, parameters of a distribution of a latent variable from the first neural network and calculating, for an unannotated generic attribute, a generic feature suppression loss; combining the sensitive attribute suppression loss, the useful attribute preservation loss, and the generic feature suppression loss into a total loss; and training the first neural network and the second neural network with the total loss.