  Data for good implies unfettered access to data. But data owners must be
conservative about how, when, and why they share data or risk violating the
trust of the people they aim to help, losing their funding, or breaking the
law. Data sharing agreements can help prevent privacy violations, but require a
level of specificity that is premature during preliminary discussions, and can
take over a year to establish.
  We consider the generation and use of synthetic data to facilitate ad hoc
collaborations involving sensitive data. A good synthetic dataset has two
properties: it is representative of the original data, and it provides strong
guarantees about privacy.
  In this paper, we discuss important use cases for synthetic data that
challenge the state of the art in privacy-preserving data generation, and
describe DataSynthesizer, a dataset generation tool that takes a sensitive
dataset as input and generates a structurally and statistically similar
synthetic dataset, with strong privacy guarantees, as output. The data owners
need not release their data, while potential collaborators can begin developing
models and methods with some confidence that their results will work similarly
on the real dataset. The distinguishing feature of DataSynthesizer is its
usability - in most cases, the data owner need not specify any parameters to
start generating and sharing data safely and effectively.
  The code implementing DataSynthesizer is publicly available on GitHub at
https://github.com/DataResponsibly. The work on DataSynthesizer is part of the
Data, Responsibly project, where the goal is to operationalize responsibility
in data sharing, integration, analysis and use.
