Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …

While modern machine learning models rely on increasingly large training datasets, data is
often limited in privacy-sensitive domains. Generative models trained with differential privacy …

As the prevalence of data analysis grows, safeguarding data privacy has become a
paramount concern. Consequently, there has been an upsurge in the development of …

Diffusion models (DMs) are one of the most widely used generative models for producing
high quality images. However, a flurry of recent papers points out that DMs are least private …

Generating differentially private (DP) synthetic data that closely resembles the original
private data is a scalable way to mitigate privacy concerns in the current data-driven world …

Training even moderately-sized generative models with differentially-private stochastic
gradient descent (DP-SGD) is difficult: the required level of noise for reasonable levels of …

We show that the canonical approach for training differentially private GANs--updating the
discriminator with differentially private stochastic gradient descent (DPSGD)--can yield …

The availability of rich and vast data sources has greatly advanced machine learning
applications in various domains. However, data with privacy concerns comes with stringent …

Differential privacy (DP) has emerged as a rigorous notion to quantify data privacy.
Subsequently, Renyi differential privacy (RDP) becomes an alternative to the ordinary DP …

With the continuous expansion of data scale, data condensation technology has emerged as
a means to reduce costs related to storage, time, and energy consumption. Data …