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 .

References

Jamme, J. (2027). Parsimonious Perturbation Mechanism for Magnitude Tables with Analytical Risk-Utility Metrics. In: Domingo-Ferrer, J., González-Yero, I. (eds) Privacy in Statistical Databases. PSD 2026. Lecture Notes in Computer Science, vol 16925, pp. 130–146. Springer, Cham. doi:10.1007/978-3-032-37883-5_9

See also

Useful links:

• https://github.com/InseeFrLab/dominoise

• https://inseefrlab.github.io/dominoise/

• Report bugs at https://github.com/InseeFrLab/dominoise/issues

Author

Maintainer: Julien Jamme julien.jamme@insee.fr (ORCID)

Authors:

• Julien Jamme julien.jamme@insee.fr (ORCID)

Other contributors:

• Institut national de la statistique et des études économiques [copyright holder]