add_perturb: De-identification via random noise

In deident: Persistent Data Anonymization Pipeline

De-identification via random noise

Description

add_perturb() adds an perturbation step to a transformation pipeline (NB: intended for numeric data). When ran as a transformation, each specified variable is transformed by the noise function.

Usage

add_perturb(object, ..., noise = adaptive_noise(0.1))

Arguments

object	Either a data.frame, tibble, or existing DeidentList pipeline.
...	variables to be transformed.
noise	a single-argument function that applies randomness.

Value

A 'DeidentList' representing the untrained transformation pipeline. The object contains fields:

• deident_methods a list of each step in the pipeline (consisting of variables and method)

and methods:

• mutate apply the pipeline to a new data set

• to_yaml serialize the pipeline to a '.yml' file

See Also

adaptive_noise(), white_noise(), and lognorm_noise()

Examples

pipe.perturb <- add_perturb(ShiftsWorked, `Daily Pay`)
pipe.perturb$mutate(ShiftsWorked)

pipe.perturb.white_noise <- add_perturb(ShiftsWorked, `Daily Pay`, noise=white_noise(0.1))
pipe.perturb.white_noise$mutate(ShiftsWorked)

pipe.perturb.noisy_adaptive <- add_perturb(ShiftsWorked, `Daily Pay`, noise=adaptive_noise(1))
pipe.perturb.noisy_adaptive$mutate(ShiftsWorked)

deident documentation built on April 3, 2025, 6:14 p.m.