Software systems for tabular data releases
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Preserving confidentiality of high-dimensional tabulated data: Statistical and computational issues
Statistics and Computing
Model Diagnostics for Remote Access Regression Servers
Statistics and Computing
Remote access systems for statistical analysis of microdata
Statistics and Computing
Mechanism Design via Differential Privacy
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
Combinations of SDC methods for microdata protection
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Random Forests for Generating Partially Synthetic, Categorical Data
Transactions on Data Privacy
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To protect confidentiality, statistical agencies typically alter data before releasing them to the public. Ideally, although generally not done, the agency also provides a way for secondary data analysts to assess the quality of inferences obtained with the released data. Quality measures can help secondary data analysts to identify inaccurate conclusions resulting from the disclosure limitation procedures, as well as have confidence in accurate conclusions. We propose a framework for an interactive, web-based system that analysts can query for measures of inferential quality. As we illustrate, agencies seeking to build such systems must consider the additional disclosure risks from releasing quality measures. We suggest some avenues of research on limiting these risks.