Information about us, our actions, and our preferences is created at scale through surveys or
scientific studies or as a result of our interaction with digital devices such as smartphones …

Differential privacy (DP) is in our smart phones, web browsers, social media, and the federal
statistics used to allocate billions of dollars. Despite the mathematical concept being only 17 …

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 …

The wide-spread availability of rich data has fueled the growth of machine learning
applications in numerous domains. However, growth in domains with highly-sensitive data …

We propose a general approach for differentially private synthetic data generation, that
consists of three steps:(1) select a collection of low-dimensional marginals,(2) measure …

In differential privacy (DP), a challenging problem is to generate synthetic datasets that
efficiently capture the useful information in the private data. The synthetic dataset enables …

We propose, implement, and evaluate a new algo-rithm for releasing answers to very large
numbersof statistical queries likek-way marginals, sub-ject to differential privacy. Our …

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 …

This paper studies the synthesis of high-dimensional datasets with differential privacy (DP).
The state-of-the-art solution addresses this problem by first generating a set M of noisy low …

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 …