Governments around the world have faced serious challenges in recent years, including global health pandemics, supply chain disruptions, rising energy and food prices, and shrinking household budgets. To be successful in dealing with these crises, decision-makers need the right data at the right time. However, many types of data needed for this from statistics offices and others are sensitive because they contain private information about individuals or companies.

Privacy-enhancing technologies (PETs) are receiving increasing attention as a way to gain access to sensitive data while ensuring that privacy is protected. While legal agreements on data sharing can lead to unwanted breaches, PETs are designed to guarantee privacy.

The newly published “UN Guide on PETs for Official Statistics” (opens in new tab) summarizes tools to privately run statistics on sensitive data. It explicitly mentions the popular tools ABY and MOTION (see pp. 29-30) as state-of-the-art open-source secure Multi-Party Computation (sMPC) frameworks. The research and development of these tools as well as several privacy-preserving applications built on top of them has been carried out by the Cryptography and Privacy Engineering Group (ENCRYPTO) of professor Thomas Schneider during phases I and II of the collaborative research center CROSSING.