A New Model for International, Privacy-Preserving Data Science

Currently when data analysis is performed between National Statistical Organizations (NSOs) such as the US Census Bureau and Statistics Canada, a complex series of arrangements must be agreed to that creates severe yet important restrictions on how and by whom the required data is accessed, thus increasing burden and time.

Here we demonstrate a new approach using remote, privacy-preserving processes via a collaboration between multiple NSOs in conjunction with the United Nations Privacy-Enhancing Technologies Lab (UN PET Lab). The proof-of-concept involves using the open-source data science platform PySyft and establishing the cloud infrastructure necessary such that nodes hosted by the US Census Bureau and other NSOs are facilitated by a network gateway hosted by the UN PET Lab. This architecture enables a private join on synthetic data representing realistic trade data from UN Comtrade, without each NSO needing to directly access the other NSO's data. It also enables investigations into key policy and governance questions as these technologies mature.

We believe this project will be an important milestone towards enabling privacy-preserving and remote data science between international government entities and uncovering future aspects of privacy policy and governance.