Problem
Research questions and friction points this paper is trying to address.
confidential dissemination
frequency tables
disclosure risk
microdata
privacy protection
iLBA: An R package for confidentially disseminating aggregated frequency tables
🤖 AI Summary
This study addresses the privacy risks posed by small-count cells in frequency tables released by statistical agencies. To mitigate these risks while preserving data utility, the authors propose information loss-bounded aggregation (iLBA), a novel approach that integrates small cell adjustment (SCA) with controlled tabular rounding. The method generates confidential summary tables under strict bounds on information loss, effectively balancing confidentiality and analytical usefulness. Notably, this work provides the first open-source R package implementing iLBA, enabling users to construct masked fine-grained tables, produce disclosure-controlled summary tables, and perform single-cell queries. The implementation supports efficient, reproducible disclosure control for tabular data, offering a practical solution for official statistics dissemination.
📝 Abstract
Statistical agencies frequently release frequency tables derived from microdata, but small frequency cells may lead to disclosure risks. We present \texttt{iLBA}, an open-source \textsf{R} package for confidential dissemination of aggregated frequency tables. The package implements the Information-Loss-Bounded Aggregation (iLBA) algorithm, which combines Small Cell Adjustment (SCA) at the finest level table with an aggregation procedure that introduces controlled ambiguity while bounding information loss. The software enables users to construct masked finest level tables, generate confidential aggregated tables for selected variables, and obtain masked frequencies for single-cell queries. By providing an accessible implementation of the iLBA method, the package facilitates reproducible and efficient disclosure control for tabular data derived from microdata.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Information-Loss-Bounded Aggregation
Small Cell Adjustment
confidential dissemination
tabular data privacy
R package
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