iLBA: An R package for confidentially disseminating aggregated frequency tables

TL;DR

The paper introduces iLBA, an R package that applies a novel aggregation algorithm to release frequency tables with reduced disclosure risk while controlling information loss.

Contribution

It provides an open-source implementation of the iLBA algorithm, enabling confidential dissemination of microdata-derived frequency tables with controlled ambiguity.

Findings

The package effectively masks small frequency cells to reduce disclosure risk.

It allows construction of masked finest level tables and confidential aggregated tables.

The software supports reproducible disclosure control for tabular microdata.

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.