18164215. GENERATIVE MACHINE LEARNING MODELS FOR PRIVACY PRESERVING SYNTHETIC DATA GENERATION USING DIFFUSION simplified abstract (NVIDIA Corporation)
Contents
- 1 GENERATIVE MACHINE LEARNING MODELS FOR PRIVACY PRESERVING SYNTHETIC DATA GENERATION USING DIFFUSION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 GENERATIVE MACHINE LEARNING MODELS FOR PRIVACY PRESERVING SYNTHETIC DATA GENERATION USING DIFFUSION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
GENERATIVE MACHINE LEARNING MODELS FOR PRIVACY PRESERVING SYNTHETIC DATA GENERATION USING DIFFUSION
Organization Name
Inventor(s)
Karsten Julian Kreis of Vancouver (CA)
Arash Vahdat of Mountain View CA (US)
GENERATIVE MACHINE LEARNING MODELS FOR PRIVACY PRESERVING SYNTHETIC DATA GENERATION USING DIFFUSION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18164215 titled 'GENERATIVE MACHINE LEARNING MODELS FOR PRIVACY PRESERVING SYNTHETIC DATA GENERATION USING DIFFUSION
Simplified Explanation
The abstract of the patent application describes systems and methods for configuring generative machine learning models using privacy criteria, such as differential privacy criteria. These models can generate outputs representing content while satisfying differential privacy criteria by diffusing training data to multiple noise levels.
- Systems and methods for configuring generative machine learning models using privacy criteria
- Models can generate outputs representing content while satisfying differential privacy criteria
- Models are determined by diffusing training data to multiple noise levels
Potential Applications
The technology can be applied in industries such as healthcare, finance, and marketing where sensitive data needs to be protected while still utilizing machine learning models for generating content.
Problems Solved
This technology addresses the issue of maintaining privacy while using machine learning models to generate content, ensuring that sensitive data remains protected.
Benefits
The benefits of this technology include enhanced privacy protection, the ability to generate content using machine learning models, and compliance with privacy regulations.
Potential Commercial Applications
A potential commercial application of this technology could be in the development of secure data generation platforms for industries that require privacy protection, such as healthcare and finance.
Possible Prior Art
One possible prior art could be the use of differential privacy techniques in machine learning models to protect sensitive data while generating content.
What are the specific privacy criteria used in configuring the generative machine learning models?
The specific privacy criteria used in configuring the generative machine learning models are differential privacy criteria, which ensure that the models satisfy privacy constraints while generating content.
How does diffusing training data to multiple noise levels help in determining the machine learning models?
Diffusing training data to multiple noise levels helps in determining the machine learning models by adding varying levels of noise to the data, which helps in protecting privacy while still training the models effectively.
Original Abstract Submitted
In various examples, systems and methods are disclosed relating to differentially private generative machine learning models. Systems and methods are disclosed for configuring generative models using privacy criteria, such as differential privacy criteria. The systems and methods can generate outputs representing content using machine learning models, such as diffusion models, that are determined in ways that satisfy differential privacy criteria. The machine learning models can be determined by diffusing the same training data to multiple noise levels.
