User-Entity Differential Privacy in Natural Language Models
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Solution Overview
Problem
Conventional differential privacy systems are inflexible and fail to balance data privacy and model accuracy, often providing inadequate protection for sensitive data used in natural language modeling, as they are limited in the types of data they can protect and struggle to maintain a balance between privacy and model utility.
Innovation Solution
A user-entity differential privacy system that generates natural language models by injecting random Gaussian noise into the model parameters based on both user and sensitive entity information, optimizing the trade-off between privacy loss and model utility, and providing flexible protection for various data types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional differential privacy systems are used to protect sensitive data, then data privacy protection is provided, but the systems are inflexible and fail to balance data privacy and model accuracy
Solution Approach 1:
The patent segments the privacy protection mechanism into two distinct components: user-level differential privacy (protecting participation information) and entity-level differential privacy (protecting sensitive entities). This segmentation allows each component to be optimized independently for its specific data type, providing flexible protection across different data categories while maintaining overall system reliability
Solution Approach 2:
The system dynamically adjusts privacy parameters (epsilon values, noise scales) based on the specific data type being protected and the sensitivity requirements. Different data types (user information vs. sensitive entities) receive different levels of protection, allowing the system to adapt to varying privacy needs while maintaining model accuracy
2Reliability
If conventional differential privacy systems are used, then some data protection is provided, but the balance between data privacy and model accuracy is not effective
Solution Approach 1:
The patent applies different privacy protection strengths to different parts of the data: user-level protection with one epsilon value and entity-level protection with another epsilon value. This local quality approach ensures that privacy protection is tailored to the specific sensitivity and importance of each data type, preventing unnecessary accuracy loss in non-sensitive areas while maintaining strong protection where needed
Solution Approach 2:
The system changes privacy parameters (epsilon, noise scale) based on the specific training phase and data type. During training, different privacy budgets are allocated to user information versus sensitive entities. The noise scale is dynamically adjusted based on gradient sensitivity, allowing the system to maintain model accuracy while providing effective privacy protection
3Object-affected harmful factors
If noise is injected into model parameters to protect privacy, then data security is improved, but model utility may be reduced
Solution Approach 1:
The patent applies partial differential privacy protection rather than uniform protection across all data. By selectively applying privacy mechanisms only where necessary (based on data sensitivity) and using targeted noise injection, the system achieves adequate data security while minimizing the impact on model utility. The privacy protection is applied at the appropriate level (user or entity) rather than excessively across the entire model
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media can generate a natural language model that provides user-entity differential privacy. For example, in one or more embodiments, a system samples sensitive data points from a natural language dataset. Using the sampled sensitive data points, the system determines gradient values corresponding to the natural language model. Further, the system generates noise for the natural language model. The system generates parameters for the natural language model using the gradient values and the noise, facilitating simultaneous protection of the users and sensitive entities associated with the natural language dataset. In some implementations, the system generates the natural language model through an iterative process (e.g., by iteratively modifying the parameters).