Abstract Machine Learning (ML) models are ubiquitous in real-world applications and are a
constant focus of research. Modern ML models have become more complex, deeper, and …

With the ongoing rapid adoption of Artificial Intelligence (AI)-based systems in high-stakes
domains, ensuring the trustworthiness, safety, and observability of these systems has …

In the past decade, differential privacy has seen remarkable success as a rigorous and
practical formalization of data privacy. This privacy definition and its divergence based …

This work presents a systematic benchmark of differentially private synthetic data generation
algorithms that can generate tabular data. Utility of the synthetic data is evaluated by …

A reconstruction attack on a private dataset D takes as input some publicly accessible
information about the dataset and produces a list of candidate elements of D. We introduce a …

We propose AIM, a novel algorithm for differentially private synthetic data generation.\aim is
a workload-adaptive algorithm, within the paradigm of algorithms that first selects a set of …

We study private synthetic data generation for query release, where the goal is to construct a
sanitized version of a sensitive dataset, subject to differential privacy, that approximately …

We provide a differentially private algorithm for producing synthetic data simultaneously
useful for multiple tasks: marginal queries and multitask machine learning (ML). A key …

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 …

We study the problem of efficiently generating differentially private synthetic data that
approximate the statistical properties of an underlying sensitive dataset. In recent years …