We conduct comprehensive experiments on three benchmark datasets. The goal of this toolbox is to make private generation of synthetic data samples accessible to machine learning practitioners This repo is a python library to generate differentially private (dp) synthetic data without the need of any ml model training
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It is based on the following papers that proposed private evolution (pe), a new dp synthetic data framework that only utilizes the blackbox inference apis of foundation models (e.g., stable diffusion, gpt models).
In this paper, we discover that the pe framework is sufficiently general to allow inference apis beyond foundation models
In this section, we’ll examine the problem of generating synthetic data using differentially private algorithms. Generating synthetic replicas of private text data with a formal privacy guarantee, i.e., differential privacy (dp), offers a promising and scalable solution
