Invention Publication
- Patent Title: GENERATIVE MACHINE LEARNING MODELS FOR PRIVACY PRESERVING SYNTHETIC DATA GENERATION USING DIFFUSION
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Application No.: US18164215Application Date: 2023-02-03
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Publication No.: US20240111894A1Publication Date: 2024-04-04
- Inventor: Karsten Julian KREIS , Tim DOCKHORN , Tianshi CAO , Arash VAHDAT
- Applicant: NVIDIA Corporation
- Applicant Address: US CA Santa Clara
- Assignee: NVIDIA Corporation
- Current Assignee: NVIDIA Corporation
- Current Assignee Address: US CA Santa Clara
- Main IPC: G06F21/62
- IPC: G06F21/62 ; G06N3/0455

Abstract:
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.
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