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 (opens in new tab) summarizes tools to privately run statistics on sensitive data. It explicitly mentions the popular tools “UN Guide on PETs for Official Statistics” and ABY (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 MOTION during phases I and II of the collaborative research center CROSSING. Cryptography and Privacy Engineering Group (ENCRYPTO) of professor Thomas Schneider
Frameworks ABY and MOTION
You can find more information about the frameworks and relating research papers on Github.
- For ABY, visit https://github.com/encryptogroup/ABY
- For MOTION, visit https://github.com/encryptogroup/MOTION
Supporting funding was provided by the German Research Foundation (DFG) via the Collaborative Research Center CROSSING and the , by the European Research Council via the Research Training Group Privacy and Trust, and the Hessian Ministry of Higher Education, Research, Science and the Arts and the Federal Ministry of Education and Research via ERC starting grant PSOTI. ATHENE