18459354. LEVERAGING INTERMEDIATE CHECKPOINTS TO IMPROVE THE PERFORMANCE OF TRAINED DIFFERENTIALLY PRIVATE MODELS simplified abstract (Google LLC)
Contents
- 1 LEVERAGING INTERMEDIATE CHECKPOINTS TO IMPROVE THE PERFORMANCE OF TRAINED DIFFERENTIALLY PRIVATE MODELS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 LEVERAGING INTERMEDIATE CHECKPOINTS TO IMPROVE THE PERFORMANCE OF TRAINED DIFFERENTIALLY PRIVATE MODELS - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
LEVERAGING INTERMEDIATE CHECKPOINTS TO IMPROVE THE PERFORMANCE OF TRAINED DIFFERENTIALLY PRIVATE MODELS
Organization Name
Inventor(s)
Om Dipakbhai Thakkar of Fremont CA (US)
Arun Ganesh of Seattle WA (US)
Virat Vishnu Shejwalkar of Amherst MA (US)
Abhradeep Guha Thakurta of Los Gatos CA (US)
Rajiv Mathews of Sunnyvale CA (US)
LEVERAGING INTERMEDIATE CHECKPOINTS TO IMPROVE THE PERFORMANCE OF TRAINED DIFFERENTIALLY PRIVATE MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18459354 titled 'LEVERAGING INTERMEDIATE CHECKPOINTS TO IMPROVE THE PERFORMANCE OF TRAINED DIFFERENTIALLY PRIVATE MODELS
Simplified Explanation
The method described in the patent application involves training a differentially private model using a private training set, generating intermediate checkpoints during the training process, and then aggregating the model and checkpoints to determine a second differentially private model.
- Training a first differentially private (DP) model using a private training set
- Generating intermediate checkpoints during the training process
- Aggregating the first DP model and intermediate checkpoints to determine a second DP model
Potential Applications
This technology could be applied in industries where privacy and data security are paramount, such as healthcare, finance, and government.
Problems Solved
This technology addresses the challenge of training machine learning models on sensitive data while maintaining privacy and confidentiality.
Benefits
The benefits of this technology include improved data privacy, increased trust in machine learning systems, and enhanced compliance with data protection regulations.
Potential Commercial Applications
Potential commercial applications of this technology include secure data analytics platforms, privacy-preserving machine learning services, and confidential data sharing solutions.
Possible Prior Art
One possible prior art in this field is the use of federated learning techniques to train models on distributed data while preserving privacy.
Unanswered Questions
How does this technology compare to existing methods for training differentially private models?
The article does not provide a direct comparison with other methods for training differentially private models, leaving the reader to wonder about the unique advantages of this approach.
What are the specific use cases where this technology would be most beneficial?
The article mentions potential applications in various industries, but does not delve into specific use cases or scenarios where this technology would provide the most value.
Original Abstract Submitted
A method includes training a first differentially private (DP) model using a private training set, the private training set including a plurality of training samples, the first DP model satisfying a differential privacy budget, the differential privacy budget defining an amount of information about individual training samples of the private training set that may be revealed by the first DP model. The method also includes, while training the first DP model, generating a plurality of intermediate checkpoints, each intermediate checkpoint of the plurality of intermediate checkpoints representing a different intermediate state of the first DP model, each of the intermediate checkpoints satisfying the same differential privacy budget. The method further includes determining an aggregate of the first DP model and the plurality of intermediate checkpoints, and determining, using the aggregate, a second DP model, the second DP model satisfying the same differential privacy budget.
