Synthetic Data Annotation for Privacy-Preserving Model Training
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Solution Overview
Problem
Existing machine learning models struggle to be trained on confidential data without violating privacy, as direct access to such data is often restricted.
Innovation Solution
Training a synthetic data generation model in an eyes-off environment using anonymity techniques to replicate confidential data patterns, generating synthetic data that is then annotated in an eyes-on environment, and using this data to train a target model for deployment in the eyes-off environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human annotators directly access confidential data to provide annotations, then model training accuracy is improved, but data privacy is compromised
Solution Approach 1:
The patent introduces an intermediary system that sits between the confidential data and human annotators. This system uses automated preprocessing, differential privacy techniques, and controlled access mechanisms to enable annotators to work with protected data without direct exposure, thus maintaining both privacy and annotation quality
Solution Approach 2:
The patent creates anonymized copies or synthetic versions of confidential data that preserve the structural and statistical properties needed for accurate annotation while removing or obscuring personally identifiable information. Annotators work with these copies rather than the original confidential data
2Object-affected harmful factors
If synthetic data is used to train models in eyes-off environment, then data privacy is maintained, but model accuracy may be reduced
Solution Approach 1:
The patent performs preliminary actions to enhance synthetic data quality before it is used for model training. This includes using high-fidelity generative models, applying domain-specific constraints, and iteratively refining the synthetic data generation process to ensure the synthesized data closely mirrors the statistical properties and patterns of real confidential data
Solution Approach 2:
The patent adjusts key parameters in the synthetic data generation process, such as noise levels, transformation intensity, and generative model hyperparameters, to optimize the balance between privacy protection and data fidelity. By carefully tuning these parameters, the system maintains realistic data characteristics while ensuring privacy safeguards
3Productivity
If annotated data is shared between eyes-off and eyes-on environments, then model training effectiveness is improved, but data security risks increase
Solution Approach 1:
The patent segments the data lifecycle into distinct secure zones: eyes-off environment for confidential data processing, intermediary processing zone for annotation, and eyes-on environment for model training. Each segment has controlled data flow and access permissions, allowing annotated data to be shared across environments while maintaining security boundaries through encryption, access controls, and audit trails
Data Source
AI summary
Systems and methods are directed to building annotated models based on eyes-off data. Specifically, a synthetic data generation model is trained and used to further train a target model. The synthetic data generation model is trained within an eyes-off environment using an anonymity technique on confidential data. The synthetic data generation model is then used to create synthetic data that closely represents the confidential data but without any specific details that can be linked back to the confidential data. The synthetic data is then annotated and used to train the target model within an eyes-on environment. Subsequently, the target model is deployed back within the eyes-off environment to classify the confidential data.