Examples

Here are some examples of how to use the Private Evolution library.

Images

• Using foundation models (diffusion models) as the APIs. These examples follow the experimental settings in the paper Differentially Private Synthetic Data via Foundation Model APIs 1: Images (ICLR 2024).

  • CIFAR10 dataset: This example shows how to generate differentially private synthetic images for the CIFAR10 dataset using the APIs from a pre-trained ImageNet diffusion model.

  • Camelyon17 dataset: This example shows how to generate differentially private synthetic images for the Camelyon17 dataset using the APIs from a pre-trained ImageNet diffusion model.

  • Cat dataset: This example shows how to generate differentially private synthetic images for the Cat dataset using the APIs from Stable Diffusion.

• Using simulators as the APIs. These examples follow the experimental settings in the paper Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Models.

  • MNIST dataset: This example shows how to generate differentially private synthetic images for the MNIST dataset using a text render.

  • CelebA dataset (simulator-generated data): This example shows how to generate differentially private synthetic images for the CelebA dataset using the generated data from a computer graphics-based renderer for face images.

  • CelebA dataset (weak simulator): This example shows how to generate differentially private synthetic images for the CelebA dataset using a rule-based avatar generator.

Text

These examples follow the experimental settings in the paper Differentially Private Synthetic Data via Foundation Model APIs 2: Text (ICML 2024 Spotlight).

• Yelp dataset: These examples show how to generate differentially private synthetic text for the Yelp dataset using LLM APIs from:

  • OpenAI APIs: See example

  • Huggingface models: See example

• OpenReview dataset: These examples show how to generate differentially private synthetic text for the OpenReview dataset using LLM APIs from:

  • OpenAI APIs: See example

  • Huggingface models: See example

• PubMed dataset: These examples show how to generate differentially private synthetic text for the PubMed dataset using LLM APIs from:

  • OpenAI APIs: See example

  • Huggingface models: See example