We are engaging a community of collaborators in academia, industry, and government to build trustworthy, open-source software tools for privacy-protective statistical analysis of sensitive personal data. These tools, which we call OpenDP, will offer the rigorous protections of differential privacy for the individuals who may be represented in confidential data and statistically valid methods of analysis for researchers who study the data.
We began this project in partnership with Microsoft developing a differentially private data curator application. Building on this collaboration, we are now building a broader community around OpenDP with stakeholders and contributors from across academia, industry, and government. Together, we plan to design, implement, and govern an “OpenDP Commons” that includes a library of differentially private algorithms and other general-purpose tools for use in end-to-end differential privacy systems.
OpenDP is being incubated by Harvard University’s Privacy Tools and Privacy Insights projects (at SEAS and IQSS), with generous support from the Sloan Foundation. Research that laid the foundation for OpenDP was done under NSF grant CNS-1237235, Cooperative Agreement No. CB16ADR0160001 from the US Census Bureau, NSF grant No. 1565387, an earlier grant from the Sloan Foundation, and a gift from Google.
Visit us on Github: https://github.com/opendifferentialprivacy/
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