Expert Meeting on Statistical Data Confidentiality

We implement a synthetic data generation framework on a pseudonymized subset of the 2021 Census data for Luxembourg. Focusing on seven categorical variables—including ordered age and education—we drop unique records upfront to mitigate the risk of singling out. Synthetic data are produced via the CART method in the synthpop package. Utility is measured using the propensity score mean-squared...

Disseminating synthetic data enables easy access to data that retains statistical similarities to the original data if access to sensitive data is restricted. However, the model employed when generating the synthetic data may influence the structure of the data, potentially affecting subsequent predictive analysis. This paper empirically investigates whether the choice of synthesis model...

The demand for georeferenced data is increasing, while sharing proprietary location data poses privacy and confidentiality challenges. This study investigates the use of synthetic data generators (SDGs) to protect sensitive locations in georeferenced datasets. We propose transforming spatial coordinates into a one-dimensional index via a Hilbert space-filling curve, thereby preserving local...

The Trusted Research Environment (TRE, or Research Data Environment, RDC) has been the great success story of data access this century. By providing highly secure yet flexible access, the TRE has enabled research use of the most sensitive data. In its turn the development of the TRE has led to significant developments in research data governance, particularly output disclosure control. The TRE...

Differential privacy (DP) has become the de facto data protection mechanism due to its strong privacy guarantees. The mathematical foundation of $\epsilon$-DP is based on the principle that the presence or absence of any record in a data set should not influence the protected result by more than an exponential factor determined by the parameter $\epsilon$. Even though DP was originally...

Teaching versions of datasets are an important component of the data discovery pipeline. These datasets often serve as an introduction to the data for potential users, allowing them to explore the data and assess the relevance of a dataset to their needs. However, in cases where source data is only available in restricted settings, such as trusted research environments (TREs), then capacity to...

This contribution addresses the intersection of statistical disclosure control and the special requirements of psychological research. Exemplarily, we show the unique sensitivity and complexity of empirical data from psychological research and the problems and possibilities to anonymize them.

The replication crisis in psychology (Open Science Collaboration, 2015; Camerer et al., 2018) has...

When evaluating the scientific worth of microdata, formally anonymized data provides maximum research potential. But this data can only be accessed onsite via Remote Execution or Safe Centers which offers little convenience for data users. In contrast, factual anonymized data can be accessed from the institutional workspace (offsite access, e. g. Scientific Use Files; SUFs) but offer less...

Confidential administrative data is usually only available to researchers within a trusted research environment (TRE). Recently, some UK groups have proposed that low-fidelity synthetic data (LFSD) is available to researchers outside the TRE to allow code-testing and data discovery. At least one such organisation already makes synthetic data available to users who request it. Relationships...