This project is a broad, multidisciplinary effort to help enable the collection, analysis, and sharing of personal data for research in social science and other fields while providing privacy for individual subjects. In particular, we aim to build an array of computational, statistical, legal, and policy tools that can be incorporated into data repositories to make privacy-protective data-sharing easier for lay researchers. These tools will integrate with the Dataverse Network software, which is already used to host data repositories around the world.
This is a collaborative effort between Harvard's Center for Research on Computation and Society, Institute for Quantitative Social Science, Berkman Center for Internet & Society, Data Privacy Lab, and MIT Libraries' Program on Information Science.
Our work received seed funding from Google and is now primarily supported by a NSF Secure and Trustworthy Cyberspace Frontier grant and a grant from the Sloan Foundation. Any opinions, findings, and conclusions or recommendations expressed on this website are those of the author(s) and do not necessarily reflect the views of our funders.
Featured Popular Articles
- "Why The World Watches America's Lead On Privacy Issues" by Adam Tanner (November 13, 2014 - Forbes)
- "Nine Things You Don't Know About The Gathering Of Your Personal Data" by Adam Tanner (November 4, 2014 - Forbes)
- Simultaneous private learning of multiple concepts
- Fingerprinting Codes and the Price of Approximate Differential Privacy
- Towards a Modern Approach to Privacy-Aware Government Data Releases
- Order revealing encryption and the hardness of private learning
- The Complexity of Computing the Optimal Composition of Differential Privacy
- Fair Information Sharing for Treasure Hunting.
- 1 of 14