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+---
+title: xD Team Members Publish Working Paper on International, Privacy-Preserving Data Science
+publish_date: 2024-12-17
+permalink: /news/un-pet-lab-working-paper/
+img_alt_text: Data flow in PySyft platform
+image: /assets/img/news/xd-team-members-publish-working-paper-on-international-privacy-preserving-data-science.jpg
+image_accessibility: Data flow in PySyft platform
+---
+<p>
+  We're excited to share a research project from xD done in collaboration with the United Nations Privacy-Enhancing Technologies Lab (UN PET Lab), the Italian National Institute of Statistics (Istat), and Statistics Canada (StatCan). In this working paper, we highlight the use of OpenMined's PySyft tool to explore how national statistical organizations (NSOs) can perform privacy-preserving data joins. We discuss the current mechanisms for privacy-preserving data sharing before a technical description of how PySyft works, and how the tool can enable easier data collaborations between NSOs.
+</p>
+<p>
+  The working paper is hosted on the Census Resource Library under the title
+  <a href="https://www.census.gov/library/working-papers/2024/comm/mitchell-et-al.html">
+  A New Model for International Privacy Preserving Data Sharing Across National Statistical Organizations</a>.
+</p>