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# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "dominoise" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'dominoise: Parsimonious Noise Perturbation for Magnitude Tables'
version: 0.2.0.9000
doi: 10.1007/978-3-032-37883-5_9
identifiers:
- type: doi
  value: 10.32614/CRAN.package.dominoise
abstract: 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) <https://doi.org/10.1007/978-3-032-37883-5_9>.
authors:
- family-names: Jamme
  given-names: Julien
  email: julien.jamme@insee.fr
  orcid: https://orcid.org/0009-0009-9054-7053
preferred-citation:
  type: conference-paper
  title: Parsimonious Perturbation Mechanism for Magnitude Tables with Analytical
    Risk-Utility Metrics
  authors:
  - family-names: Jamme
    given-names: Julien
    email: julien.jamme@insee.fr
    orcid: https://orcid.org/0009-0009-9054-7053
  editors:
  - family-names: Domingo-Ferrer
    given-names: Josep
  - family-names: González-Yero
    given-names: Ismael
  collection-title: Privacy in Statistical Databases. PSD 2026
  collection-type: proceedings
  volume: '16925'
  publisher:
    name: Springer
  year: '2027'
  doi: 10.1007/978-3-032-37883-5_9
  start: '130'
  end: '146'
  conference:
    name: Privacy in Statistical Databases. PSD 2026
    address: Cham
repository: https://inseefrlab.r-universe.dev
repository-code: https://github.com/InseeFrLab/dominoise
commit: 42dd1602cfcdf767af980ee8dc962095e2fbd84f
url: https://inseefrlab.github.io/dominoise/
date-released: '2026-10-15'
contact:
- family-names: Jamme
  given-names: Julien
  email: julien.jamme@insee.fr
  orcid: https://orcid.org/0009-0009-9054-7053
references:
- type: manual
  title: 'dominoise: Parsimonious Noise Perturbation for Magnitude Tables'
  authors:
  - family-names: Jamme
    given-names: Julien
  year: '2026'
  notes: R package version 0.2.0.9000
  url: https://github.com/InseeFrLab/dominoise