Fortified,assured,streamlined & trusted (FAST) services: Streamlined privacy protection in DataLabs

Abstract

The Australian Bureau of Statistics (ABS) is committed to improving microdata access while maintaining privacy and confidentiality through its virtual DataLab, which enables researchers to conduct complex analyses. Currently, DataLab research outputs must comply with strict disclosure rules before clearance, but the manual vetting process is cost-inefficient and error-prone. As output volumes grow across diverse projects, so too does the risk of differencing — even when individual outputs meet disclosure requirements. To address this, the ABS has been developing streamlined output protection by equipping safe users with protection tools and implementing automated vetting systems. These tools use an enhanced cellkey methodology, assigning unique random keys to each contributing record and applying protection based on aggregated keys within each table cell. This ensures consistent protection across projects sharing the same contributors. The ``same contributors, same noise'' feature mitigates differencing risks and reduces protection costs when applied universally, while vetting systems verify that outputs are generated using approved tools before dissemination. Our first contribution is a prototype ``Fortified, Assured, Streamlined, Trusted (FAST)'' output protection toolkit built in R and R Shiny to streamline DataLab vetting processes. We also developed a sequential descent optimisation algorithm supporting both asymmetric and symmetric perturbation distributions. Our method integrates $(\epsilon, \delta)$ differential privacy parameters directly into the noise distribution design used in the ABS perturbation methodology.