Expert Meeting on Statistical Data Confidentiality

At Statistics Norway, the methodology department is responsible for the internal education of staff. Traditionally, SDC training has been offered on demand, with courses held at most once a year. For a statistician who is newly employed or interested in applying a new SDC method, waiting up to a year for training is both impractical and unproductive. In addition, Statistics Norway has...

National Statistical Offices (NSOs) are pivotal in producing data essential for analyzing sociodemographic and economic trends. Most national statistical laws enshrine the confidentiality of collected data to safeguard citizens’ privacy. Yet, these laws frequently lack specific, actionable measures to enforce this principle. Consequently, supplementary normative frameworks are critical to...

To reflect key advances in statistical disclosure control (SDC), we present a revised and unified version of the World Bank’s microdata anonymization guides. The World Bank previously published three separate guides: one on SDC theory and two practice guides for implementing microdata anonymization using the R package sdcMicro, both via command line and the sdcApp GUI. These guides have been...

Synthetic data is often hailed as the future of safe data access – but in practice, it is insufficient for a method to be mathematically private or analytically useful: if legal and privacy teams do not understand the guarantees, they cannot confidently allow its use. This creates a critical but underexplored tension between cutting-edge privacy techniques and real-world operational...

With the need for deeper analysis and more granular data, statistical offices must place a greater focus on measures to mitigate the risks of statistical data disclosure. There can, however, be tensions between users who require granular data and the need for statistical offices not to disclose information of the data subjects.

However, this tension may not be most obvious to some user...