Changelog

Source: NEWS.md

dominoise (development version)

First working version of the package. The API is still unstable: function names and arguments may change without deprecation until version 1.0.0.

Parameters

• pm_params() gathers every choice in a single object: the policy, organised by disclosure scenario (dominance, prule, diff), and the three mechanism parameters (sigma_nu, sigma_eps, n), filled in as calibration proceeds.

Calibration — step 1, the differencing noise

• pm_calib_diff() returns the theoretical grid of sigma_eps values meeting a given (beta, tau) couple, with the loss floor they imply.
• plot() on that grid draws the risk-utility frontier.
• pm_commit_diff() records the decision and sets sigma_eps.

Calibration — step 2, the dominance noise

• pm_calib_dominance() returns, over a grid of dominance levels rho, the scenario-I risk and the information loss for each candidate (sigma_nu, n).
• summary() reduces that grid to the worst-case risk and the loss range of every parameter combination.
• pm_suggest_n() solves for the largest n meeting the risk ceiling — the calibration rule of step 2 — with an optional safety margin.
• pm_commit_dominance() records sigma_nu and n, and reports the resulting trade-off.

Figures

• pm_plot_risk_profile(), pm_plot_risk_max() and pm_plot_tradeoff() reproduce the calibration figures of the paper. The first two accept any scenario; the third is specific to the risk metric selected.

Applying the mechanism

• pm_perturb() applies Y' = Y (1 + rho^n nu + eps) to an aggregated table and returns it with rho, the derived keys and the perturbed total, plus a pm_meta attribute recording the full configuration.
• pm_draws() derives the two Gaussian draws from a cell key by SHA-512 hashing and quantile inversion, so that perturbations are reproducible over time.

Metrics

• assess_loss_expectation() and assess_loss_ci() give the a-priori information loss conditional on dominance.
• assess_risk_I() and assess_risk_II() give the a-priori risk measures for the external and internal inference scenarios.
• assess_risk_diff() gives an a-priori upper bound of the risk measure for the differencing scenario.
• assess_utility_empirical() and assess_risk_empirical() measure what was actually achieved on the perturbed table, alongside the theoretical values predicted for the same cells.