Semantic Equivalence Data Transformation for Privacy-Compliant ML Training
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Solution Overview
Problem
Current machine learning model training approaches rely on 'fake data' due to restrictions on using actual user personal information, leading to decreased model efficiency and increased chances of result outliers.
Innovation Solution
The approach transforms personal information data into semantic equivalents, maintaining meaning for the machine learning model, thereby enabling the use of actual user data for training while ensuring compliance with data privacy restrictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If actual user personal information data is used for training machine learning models, then model accuracy and completeness are improved, but data privacy restrictions and compliance issues arise
Solution Approach 1:
The patent creates semantic equivalent copies of personal information data that preserve the meaningful relationships and patterns needed for machine learning training while removing direct personal identifiers. These semantic equivalents are synthetic data representations that capture the essential semantic structure without containing actual personal information, thus enabling model training on accurate data while complying with privacy restrictions.
Solution Approach 2:
The patent transforms personal information data by changing its semantic parameters through controlled natural language generation. The transformation process modifies the data representation from actual personal information to semantically equivalent forms that maintain statistical and relational properties necessary for training, while altering the identifying characteristics to ensure privacy compliance.
2Object-affected harmful factors
If fake data is used for machine learning model training to comply with privacy restrictions, then data privacy compliance is improved, but model efficiency and accuracy decrease
Solution Approach 1:
Instead of using completely fabricated fake data, the patent generates semantic equivalent copies that replicate the structural and relational properties of actual personal information. These copies maintain the statistical distributions, contextual relationships, and semantic patterns necessary for effective machine learning training, thereby preserving model efficiency while achieving privacy compliance.
Solution Approach 2:
The patent introduces semantic equivalents as an intermediary between actual personal information and the machine learning model. This intermediary layer preserves the essential training value by maintaining semantic relationships and patterns while breaking the direct link to identifiable personal information, thus enabling efficient training without compromising privacy compliance.
3Object-affected harmful factors
If personal information data is transformed into semantic equivalents, then data privacy protection is improved, but data transformation complexity increases
Solution Approach 1:
The patent replaces complex mechanical or procedural data transformation methods with a language model-based semantic transformation system. By leveraging pre-trained language models to generate semantic equivalents, the system achieves sophisticated privacy protection through natural language processing rather than through complex data masking, anonymization, or synthetic data generation pipelines.
Solution Approach 2:
The patent employs pre-trained language models that possess inherent semantic understanding and generation capabilities. These models automatically generate semantically equivalent data representations without requiring extensive manual configuration, feature engineering, or complex transformation rules, thereby reducing the operational complexity of the transformation process while maintaining strong privacy protection.
Data Source
AI summary
An approach is provided in which the approach detects a set of personal information data corresponding to a set of users in a set of training data. The approach transforms the set of training data into a set of semantically equivalent training data by replacing the set of personal information with a set of semantic equivalent data. The approach then trains a machine learning model using the set of semantically equivalent training data.