{
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  "Package": "dominoise",
  "Title": "Parsimonious Noise Perturbation for Magnitude Tables",
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  "Authors@R": "c(\nperson(\"Julien\", \"Jamme\", email = \"julien.jamme@insee.fr\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0009-0009-9054-7053\")),\nperson(\"Institut national de la statistique et des études économiques\",\nrole = \"cph\"))",
  "Description": "Protects magnitude tables, such as tables of enterprise\nturnover, by multiplying each total by a Gaussian noise\ncombining a dominance-driven component and a general-purpose\none, governed by three parameters only. Information loss and\ndisclosure risks for three attack scenarios (external\ninference, internal inference and differencing) are available\nin closed form, so that calibration requires neither simulation\nnor re-estimation on the data. The package guides that\ncalibration step by step, from the differencing noise to the\ndominance parameters, with decision tables and risk-utility\nplots, applies the mechanism to an aggregated table using\nSHA-512 hashed random keys for consistency over time, and\nreports the risk and utility actually achieved. The method is\ndescribed in Jamme (2027) <doi:10.1007/978-3-032-37883-5_9>.",
  "License": "GPL (>= 3)",
  "URL": "https://github.com/InseeFrLab/dominoise,\nhttps://inseefrlab.github.io/dominoise/",
  "BugReports": "https://github.com/InseeFrLab/dominoise/issues",
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  "Repository": "https://inseefrlab.r-universe.dev",
  "Date/Publication": "2026-10-15 17:15:20 UTC",
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    "User": "root"
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  "Author": "Julien Jamme [aut, cre] (ORCID:\n<https://orcid.org/0009-0009-9054-7053>),\nInstitut national de la statistique et des études économiques [cph]",
  "Maintainer": "Julien Jamme <julien.jamme@insee.fr>",
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  "_created": "2026-10-16T12:48:14.000Z",
  "_published": "2026-10-16T13:08:01.012Z",
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      "date": "2026-08-24"
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    "perturbation",
    "random-key",
    "risk-assessment"
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    "assess_risk_II",
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    "pm_plot_impact",
    "pm_plot_risk_max",
    "pm_plot_risk_profile",
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    "pm_sigma_eps",
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      "page": "assess_loss_ci",
      "title": "Confidence-interval bound of the relative loss Z",
      "topics": [
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    },
    {
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      "title": "Conditional expectation of the absolute relative loss |Z|",
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    },
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      "title": "Compute the upper bound of the differencing risk.",
      "topics": [
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    },
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      "title": "Observed disclosure risk",
      "topics": [
        "assess_risk_empirical"
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    },
    {
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      "title": "Scenario-I (dominance) risk measure",
      "topics": [
        "assess_risk_I"
      ]
    },
    {
      "page": "assess_risk_II",
      "title": "Scenario-II (p%-rule) risk measure",
      "topics": [
        "assess_risk_II"
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      "page": "assess_utility_empirical",
      "title": "Observed information loss",
      "topics": [
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    },
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      "page": "plot.pm_calib_diff",
      "title": "Plot method for a differencing calibration grid",
      "topics": [
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    },
    {
      "page": "plot.pm_calib_dominance",
      "title": "Plot method for a dominance calibration grid",
      "topics": [
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    },
    {
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      "title": "Calibration of the differencing noise ('sigma_epsilon')",
      "topics": [
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    },
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      "page": "pm_calib_dominance",
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      "topics": [
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    {
      "page": "pm_commit_diff",
      "title": "Commit the differencing decision into the parameter object",
      "topics": [
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    },
    {
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      "topics": [
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    },
    {
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      "topics": [
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      "title": "Perturb an aggregated table",
      "topics": [
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