POSE: Phase II: Building the Differential Privacy Ecosystem through OpenDP

Data science and artificial intelligence can offer many benefits for society. Large important data sets containing private information about individuals, such as electronic medical records or online shopping behavior, could be used to help researchers understand human health and behavior, help governments evaluate policies, and help companies offer custom products and services. However, even when the data are stripped of identifying information, it may still be possible to link specific people back to the data and violate their privacy. Because the owners of these datasets are obligated to protect the privacy of the people whose data have been collected, the potential to make the data available for research to benefit society is lost. To address this problem the OpenDP software, an open-source suite of tools and algorithms for differential privacy, can be used to ensure that no individual whose personal data is in a dataset can be identified. This project supports a community effort to develop a self-sustaining ecosystem around the OpenDP software. The project creates a sustainable OpenDP ecosystem of developers, contributors and users, together with a governing board that can guide future development and respond to community needs. Workshops, conferences, and media connect new contributors, creating new ways to contribute, credit, and manage volunteer effort. Outreach to industry and researchers in public health and genomics grows the OpenDP user base. Planning for future growth of and finding new applications for OpenDP will ensure the ecosystem's long-term stability. Through this award, users of and contributors to OpenDP will have a stable community that will assist them in developing privacy solutions for the future. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.