Speaker
Description
National Statistical Offices collect massive volumes of data to fulfill their missions. These data fuel the generation of regional, national, and international statistics across various sectors. However, their immense potential remains largely untapped due to strict and legitimate privacy regulations. In this context, Lomas is a novel open-source platform designed to realize the full potential of the data held by public administrations. It enables authorized users, such as approved researchers and government analysts, to execute algorithms on confidential datasets without directly accessing the data. The Lomas platform is designed to operate within a trusted computing environment, such as governmental IT infrastructure. Authorized users access the platform remotely to submit their algorithms for execution on private datasets. Lomas executes these algorithms without revealing the data to the user and returns the results protected by Differential Privacy, a framework that introduces controlled noise to the results, rendering any attempt to extract identifiable information unreliable. Differential Privacy allows for the mathematical quantification and automatic control of the risk of disclosure while allowing for a complete transparency regarding how data is protected and utilized.
In this presentation, we illustrate how the platform can be used for the secondary use of data in research through a real-world use case in collaboration with a Swiss university. We provide guidance on how we balanced the privacy-utility trade-off to ensure the validity and statistical reproducibility of the research conclusions while offering the strongest possible privacy guarantee.
