This explainer document aims to provide an overview of the current state of the rapidly
expanding work on synthetic data technologies, with a particular focus on privacy. The …

Machine learning has the potential to assist many communities in using the large datasets
that are becoming more and more available. Unfortunately, much of that potential is not …

Differential privacy is a theoretical framework for ensuring the privacy of individual-level data
when performing statistical analysis of privacy-sensitive datasets. This tutorial provides an …

Boosting is a general method for improving the accuracy of learning algorithms. We use
boosting to construct improved privacy-pre serving synopses of an input database. These …

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 present a new algorithm for differentially private data release, based on a simple
combination of the Exponential Mechanism with the Multiplicative Weights update rule. Our …

In this article, we demonstrate that, ignoring computational constraints, it is possible to
release synthetic databases that are useful for accurately answering large classes of queries …

We consider statistical data analysis in the interactive setting. In this setting a trusted curator
maintains a database of sensitive information about individual participants, and releases …

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 …

Massive amounts of data on human beings can now be analyzed. Pragmatic purposes
abound, including selling goods and services, winning political campaigns, and identifying …