National Statistical Organizations (NSOs) such as the U.S. Census Bureau have competing mandates to both maintain the privacy of individuals and organizations whose data they collect and to provide broad access to that data. This often means that access to NSO data is highly restricted and regimented in order to minimize the risks of privacy violations. The emergence of privacy-enhancing technologies, or PETs, are creating opportunities to reconsider how NSOs can make data more available while preserving the privacy of subjects within the data. Here we report on an ongoing collaboration between the Census Bureau, Statistics Canada (StatCan), and the Italian National Institute of Statistics (Istat) in conjunction with the United Nations Privacy-Enhancing Technologies Lab (UN PET Lab). The project involves using the open-source platform PySyft and establishing the digital infrastructure necessary so that nodes hosted by the Census Bureau, StatCan, and Istat, facilitated by a network gateway hosted by the UN PET Lab, can perform a join on synthetic data. This will allow for testing and building confidence before attempting joins on actual private data. We believe this project will be an important milestone towards enabling international, privacy-preserving data science between government agencies.