Parsimonious Noise Perturbation for Magnitude Tables

Documentation for package ‘dominoise’ version 0.2.0

assess_loss_ci	Confidence-interval bound of the relative loss Z
assess_loss_expectation	Conditional expectation of the absolute relative loss \|Z\|
assess_risk_diff	Compute the upper bound of the differencing risk.
assess_risk_empirical	Observed disclosure risk
assess_risk_I	Scenario-I (dominance) risk measure
assess_risk_II	Scenario-II (p%-rule) risk measure
assess_utility_empirical	Observed information loss
plot.pm_calib_diff	Plot method for a differencing calibration grid
plot.pm_calib_dominance	Plot method for a dominance calibration grid
pm_calib_diff	Calibration of the differencing noise ('sigma_epsilon')
pm_calib_dominance	Calibration of the dominance noise (sigma_nu and n)
pm_commit_diff	Commit the differencing decision into the parameter object
pm_commit_dominance	Commit the dominance decision (sigma_nu and n) into the parameter object
pm_draws	Derive the two Gaussian draws from a cell key
pm_params	Create the perturbation-parameter object containing all the parameters of the noise.
pm_perturb	Perturb an aggregated table
pm_plot_impact	Observed perturbation against dominance
pm_plot_risk_max	Worst-case risk against a mechanism parameter
pm_plot_risk_profile	Risk profile as a function of dominance
pm_plot_tradeoff	Risk-utility trade-off map
pm_sigma_eps	Smallest 'sigma_eps' guaranteeing the differencing-risk ceiling
pm_suggest_n	Largest admissible shape parameter n
summary.pm_calib_dominance	Worst-case risk and loss range of a dominance calibration grid