Synthetic Data for Social Good

Data scientists need access to data to do social good. 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 1) discuss use cases for synthetic data that challenge the state of the art in privacy-preserving data generation, and 2) 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. ∗This work was supported by the University of Washington Information School, Microsoft, the Gordon and Betty Moore Foundation (Award #2013-10-29) and the Alfred P. Sloan Foundation (Award #3835) through the Data Science Environments program. †This work was supported in part by NSF Grants No. 1741047, 1464327 and 1539856, and BSF Grant No. 2014391. Bloomberg Data for Good Exchange Conference. 24-Sep-2017, New York City, NY, USA.