Nvidia corporation (20240111894). GENERATIVE MACHINE LEARNING MODELS FOR PRIVACY PRESERVING SYNTHETIC DATA GENERATION USING DIFFUSION simplified abstract
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 20240111894 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 in ways that satisfy differential privacy criteria by diffusing training data to multiple noise levels.
- Generative machine learning models configured using privacy criteria
- Outputs representing content generated by diffusion models
- Models determined to satisfy differential privacy criteria
- Training data diffused to multiple noise levels
Potential Applications
The technology described in this patent application could have potential applications in the following areas:
- Healthcare data analysis
- Financial data modeling
- Social media content generation
Problems Solved
The technology addresses the following problems:
- Ensuring privacy in machine learning models
- Generating content while maintaining data confidentiality
- Adhering to differential privacy criteria in model training
Benefits
The benefits of this technology include:
- Enhanced privacy protection in machine learning applications
- More accurate content generation with privacy considerations
- Compliance with data privacy regulations
Potential Commercial Applications
A potential commercial application of this technology could be in:
- Data analytics software for businesses
Possible Prior Art
One possible prior art for differentially private generative machine learning models is the paper "Differentially Private Generative Adversarial Networks" by Abadi et al. (2016).
Unanswered Questions
How does this technology compare to existing privacy-preserving machine learning methods?
This article does not provide a direct comparison to other privacy-preserving machine learning methods.
What are the computational costs associated with implementing this technology?
The article does not address the computational costs of implementing differentially private generative machine learning models.
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.
