This repo is a Python library to generate differentially private (DP) synthetic data without the need of any ML model training. It is based on the following papers that proposed a new DP synthetic data framework that only utilizes the blackbox inference APIs of foundation models (e.g., Stable Diffusion, GPT models).
- Differentially Private Synthetic Data via Foundation Model APIs 1: Images
[paper (ICLR 2024)] [paper (arxiv)]
Authors: [Zinan Lin], [Sivakanth Gopi], [Janardhan Kulkarni], [Harsha Nori], [Sergey Yekhanin]
Please refer to the documentation for more details, including the installation instructions, usage, and examples.
11/21/2024: The refactored codebase for image generation has been released. It is completely refactored to be more modular and easier to use and extend. The code originally published with the paper has been moved to the deprecated branch, which is no longer maintained.
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This project uses foundation model APIs to create synthetic data with differential privacy guarantees. Differential privacy (DP) is a formal framework that ensures the output of an algorithm does not reveal too much information about its inputs. Without a formal privacy guarantee, a synthetic data generation algorithm may inadvertently reveal sensitive information about its input datapoints.
Using synthetic data in downstream applications can carry risk. Synthetic data may not always reflect the true data distribution, and can cause harms in downstream applications. Both the dataset and algorithms behind the foundation model APIs may contain various types of bias, leading to potential allocation, representation, and quality-of-service harms. Additionally, privacy violations can still occur if the ε and δ privacy parameters are set inappropriately, or if multiple copies of a sample exist in the seed dataset. It is important to consider these factors carefully before any potential deployments.