Abstract

Co-authors: James Honaker (Harvard University) , Gary King (Harvard University) , Jack Murtagh (Harvard University) , Kobbi Nissim (Ben-Gurion University and CRCS Harvard University) , Jonathan Ullman (Northeastern University) , Salil Vadhan  (Harvard University)

We provide an overview of the design of PSI (“a Private data Sharing Interface”), a system we are developing to enable researchers in the social sciences and other fields to share and explore privacy-sensitive datasets with the strong privacy protections of differential privacy.
PSI is designed so that none of its users need expertise in privacy, computer science, or statistics. PSI enables them to make informed decisions about the appropriate use of differential privacy, the setting of privacy parameters, the partitioning of a privacy budget across different statistics, and the interpretation of errors introduced for privacy.
Additionally, PSI is designed to be integrated with existing and widely used data repository infrastructures as part of a broader collection of mechanisms for the handling of privacy-sensitive data, including an approval process for accessing raw data (e.g. through IRB review), access control, and secure storage.
Its initial set of differentially private algorithms were chosen to include statistics that have wide use in the social sciences, and are integrated with existing statistical software designed for modeling, interpreting, and exploring social science data.

Related Links

• https://arxiv.org/pdf/1609.04340v1.pdf - working paper