dominoise: Parsimonious Noise Perturbation for Magnitude Tables
Protects magnitude tables, such as tables of enterprise turnover,
by multiplying each total by a Gaussian noise combining a dominance-driven
component and a general-purpose one, governed by three parameters only.
Information loss and disclosure risks for three attack scenarios
(external inference, internal inference and differencing) are available in
closed form, so that calibration requires neither simulation nor
re-estimation on the data. The package guides that calibration
step by step, from the differencing noise to the dominance parameters,
with decision tables and risk-utility plots, applies the mechanism to an
aggregated table using SHA-512 hashed random keys for consistency over
time, and reports the risk and utility actually achieved. The method is
described in Jamme (2027)
<doi:10.1007/978-3-032-37883-5_9>.
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[aut, cre],
Institut national de la statistique et des études économiques [cph]