Package 'deident'

Function factory to apply white noise to a vector proportional to the spread of the data

Description

Function factory to apply white noise to a vector proportional to the spread of the data

Usage

adaptive_noise(sd.ratio = 1/10)

Arguments

sd.ratio

the level of noise to apply relative to the vectors standard deviation.

Value

a function

Examples

f <- adaptive_noise(0.2)
f(1:10)

---

De-identification via categorical aggregation

Description

add_blur() adds an bluring step to a transformation pipeline (NB: intended for categorical data). When ran as a transformation, values are recoded to a lower cardinality as defined by blur. #'

Usage

add_blur(object, ..., blur = c())

Arguments

object	Either a data.frame, tibble, or existing DeidentList pipeline.
...	variables to be transformed.
blur	a key-value pair such that 'key' is replaced by 'value' on transformation.

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

category_blur() is provided to aid in defining the blur

Examples

.blur <- category_blur(ShiftsWorked$Shift, `Working` = "Day|Night")
pipe.blur <- add_blur(ShiftsWorked, `Shift`, blur = .blur)
pipe.blur$mutate(ShiftsWorked)

---

De-identification via hash encryption

Description

add_encrypt() adds an encryption step to a transformation pipeline. When ran as a transformation, each specified variable undergoes replacement via an encryption hashing function depending on the hash_key and seed set.

Usage

add_encrypt(object, ..., hash_key = "", seed = NA)

Arguments

object	Either a data.frame, tibble, or existing DeidentList pipeline.
...	variables to be transformed.
hash_key	a random alphanumeric key to control encryption
seed	a random alphanumeric to concat to the value being encrypted

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

Examples

# Basic usage; without setting a `hash_key` or `seed` encryption is poor.
pipe.encrypt <- add_encrypt(ShiftsWorked, Employee)
pipe.encrypt$mutate(ShiftsWorked)

# Once set the encryption is more secure assuming `hash_key` and `seed` are 
# not exposed.
pipe.encrypt.secure <- add_encrypt(ShiftsWorked, Employee, hash_key="hash1", seed="Seed2")
pipe.encrypt.secure$mutate(ShiftsWorked)

---

Add aggregation to pipelines

Description

add_group() allows for the injection of aggregation into the transformation pipeline. Should you need to apply a transformation under aggregation (e.g. add_shuffle) this helper creates a grouped data.frame as would be done with dplyr::group_by(). The function add_ungroup() is supplied to perform the inverse operation.

Usage

add_group(object, ...)

add_ungroup(object, ...)

Arguments

object	Either a data.frame, tibble, or existing DeidentList pipeline.
...	Variables on which data is to be grouped.

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

Examples

pipe.grouped <- add_group(ShiftsWorked, Date, Shift)
pipe.grouped_shuffle <- add_shuffle(pipe.grouped, `Daily Pay`)
add_ungroup(pipe.grouped_shuffle, `Daily Pay`)

---

De-identification via numeric aggregation

Description

add_numeric_blur() adds an bluring step to a transformation pipeline (NB: intended for numeric data). When ran as a transformation, the data is split into intervals depending on the cuts supplied of the series [-Inf, cut.1), [cut.1, cut.2), ..., [cut.n, Inf] where cuts = c(cut.1, cut.2, ..., cut.n).

Usage

add_numeric_blur(object, ..., cuts = 0)

Arguments

object	Either a data.frame, tibble, or existing DeidentList pipeline.
...	variables to be transformed.
cuts	The position in which data is to be divided.

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

---

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)

---

De-identification via replacement

Description

add_pseudonymize() adds a psuedonymization step to a transformation pipeline. When ran as a transformation, terms that have not been seen before are given a new random alpha-numeric string while terms that have been previously transformed reuse the same term.

Usage

add_pseudonymize(object, ..., lookup = list())

Arguments

object	Either a data.frame, tibble, or existing DeidentList pipeline.
...	variables to be transformed.
lookup	a pre-existing name-value pair to define intended psuedonymizations. Instances of 'name' will be replaced with 'value' on transformation.#'

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

Examples

# Basic usage; 
pipe.pseudonymize <- add_pseudonymize(ShiftsWorked, Employee)
pipe.pseudonymize$mutate(ShiftsWorked)

pipe.pseudonymize2 <- add_pseudonymize(ShiftsWorked, Employee, 
                                    lookup=list("Kyle Wilson" = "Kyle"))
pipe.pseudonymize2$mutate(ShiftsWorked)

---

De-identification via random sampling

Description

add_shuffle() adds a shuffling step to a transformation pipeline. When ran as a transformation, each specified variable undergoes a random sample without replacement so that summary metrics on a single variable are unchanged, but inter-variable metrics are rendered spurious.

Usage

add_shuffle(object, ..., limit = 0)

Arguments

object	Either a data.frame, tibble, or existing DeidentList pipeline.
...	variables to be transformed.
limit	integer - the minimum number of observations a variable needs to have for shuffling to be performed. If the variable has length less than limit values are replaced with NAs.

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

add_group() for usage under aggregation

Examples

# Basic usage; 
pipe.shuffle <- add_shuffle(ShiftsWorked, Employee)
pipe.shuffle$mutate(ShiftsWorked)

pipe.shuffle.limit <- add_shuffle(ShiftsWorked, Employee, limit=1)
pipe.shuffle.limit$mutate(ShiftsWorked)

---

Apply a 'deident' pipeline

Description

Applies a pipeline as defined by deident to a data frame. tibble, or file.

Usage

apply_deident(object, deident, ...)

Arguments

object	The data to be deidentified
deident	A deidentification pipeline to be used.
...	Terms to be passed to other methods

---

Apply a 'deident' pipeline to a new data frame

Description

Apply a 'deident' pipeline to a new data frame

Usage

apply_to_data_frame(data, transformer, ...)

Arguments

data	The data set to be converted
transformer	The pipeline to be used
...	To be passed on to other methods

---

Base class for all De-identifier classes

Description

Create new Deidentifier object

Setter for 'method' field

Save 'Deidentifier' to serialized object.

Apply 'method' to a vector of values

Apply 'method' to variables in a data frame

Apply 'mutate' method to an aggregated data frame.

Aggregate a data frame and apply 'mutate' to each.

Convert self to a list

String representation of self

Check if parameters are in allowed fields

Arguments

method	New function to be used as the method.
location	File path to save to.
keys	Vector of values to be processed
force	Perform transformation on all variables even if some given are not in the data.
grouped_data	a 'grouped_df' object
data	A data frame to be manipulated
grp_cols	Vector of variables in 'data' to group on.
mutate_cols	Vector of variables in 'data' to transform.
type	character vector describing the object. Defaults to class.
...	Options to check exist

Fields

method

Function to call for data transform.

---

Deidentifier class for applying 'blur' transform

Description

Convert self to a list.

Arguments

blur	Look-up list to define aggregation.
keys	Vector of values to be processed
...	Values to be concatenated to keys

Details

'Bluring' refers to aggregation of data e.g. converting city to country, or post code to IMD. The level of blurring is defined by the list given at initialization which maps key to value e.g. list(London = "England", Paris = "France").

Value

Blurer Apply blur to a vector of values

Fields

blur

List of aggregations to be applied. Create new Blurer object

---

Utility for producing 'blur'

Description

Utility for producing 'blur'

Usage

category_blur(vec, ...)

Arguments

vec	The vector of values to be used
...	Replacement = RegexPattern pairs of arguments

---

Create a deident pipeline

Description

Create a deident pipeline

Usage

create_deident(method, ...)

Arguments

method	A deidentifier to initialize.
...	list of variables to be deidentifier. NB: key word arguments will be passed to method at initialization.

---

Define a transformation pipeline

Description

deident() creates a transformation pipeline of 'deidentifiers' for the repeated application of anonymization transformations.

Usage

deident(data, deidentifier, ...)

Arguments

data	A data frame, existing pipeline, or a 'deidentifier' (as either initialized object, class generator, or character string)
deidentifier	A deidentifier' (as either initialized object, class generator, or character string) to be appended to the current pipeline
...	Positional arguments are variables of 'data' to be transformed and key-word arguments are passed to 'deidentifier' at creation

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

Examples

# 
pipe <- deident(ShiftsWorked, Pseudonymizer, Employee)

print(pipe)

apply_deident(ShiftsWorked, pipe)

---

Apply a pipeline to files on disk.

Description

Apply a deident pipeline to a set of files and save them back to disk

Usage

deident_job_from_folder(
  deident_pipeline,
  data_dir,
  result_dir = "Deident_results"
)

Arguments

deident_pipeline	The deident list to be used.
data_dir	a path to the files to be transformed.
result_dir	a path to where files are to be saved.

---

R6 class for the removal of variables from a pipeline

Description

A Deident class dealing with the exclusion of variables.

---

Deidentifier class for applying 'encryption' transform

Description

Create new Encrypter object

Convert self to a list.

Arguments

hash_key	An alpha numeric key for use in encryption
seed	An alpha numeric key which is concatenated to minimize brute force attacks
keys	Vector of values to be processed
...	Values to be concatenated to keys

Details

'Encrypting' refers to the cryptographic hashing of data e.g. md5 checksum. Encryption is more powerful if a random hash and seed are supplied and kept secret.

Value

Encrypter Apply blur to a vector of values

Fields

hash_key

Alpha-numeric secret key for encryption

seed

String for concatenation to raw value

---

Restore a serialized deident from file

Description

Restore a serialized deident from file

Usage

from_yaml(path)

Arguments

path	Path to serialized deident.

Examples

deident <- deident(ShiftsWorked, Pseudonymizer, Employee)
.tempfile <- tempfile(fileext = ".yml")
deident$to_yaml(.tempfile)

deident.yaml <- from_yaml(.tempfile)
deident.yaml$mutate(ShiftsWorked)

---

GroupedShuffler class for applying 'shuffling' transform with data aggregated

Description

Convert self to a list.

Character representation of the class

Arguments

limit	Minimum number of rows required to shuffle data
data	A data frame to be manipulated
...	Vector of variables in 'data' to transform.

Details

'Shuffling' refers to the a random sampling of a variable without replacement e.g. [A, B, C] becoming [B, A, C] but not [A, A, B]. "Grouped shuffling" refers to aggregating the data by another feature before applying the shuffling process. Grouped shuffling will preserve aggregate level metrics (e.g. mean, median, mode) but removes ordinal properties i.e. correlations and auto-correlations

Fields

group_on

Symbolic representation of grouping variables

limit

Minimum number of rows required to shuffle data Create new GroupedShuffler object

---

Function factory to apply log-normal noise to a vector

Description

Function factory to apply log-normal noise to a vector

Usage

lognorm_noise(sd = 0.1)

Arguments

sd	the standard deviation of noise to apply.

Value

a function

Examples

f <- lognorm_noise(1)
f(1:10)

---

Group numeric data into baskets

Description

Group numeric data into baskets

---

R6 class for deidentification via random noise

Description

A Deident class dealing with the addition of random noise to a numeric variable.

Create new Perturber object

Apply noise to a vector of values

Convert self to a list.

Character representation of the class

Arguments

noise	a single-argument function that applies randomness.
keys	Vector of values to be processed
...	Values to be concatenated to keys

Fields

noise.str

character representation of noise

method

random noise function

Examples

pert <- Perturber$new()
  pert$transform(1:10)

---

R6 class for deidentification via replacement

Description

A Deident class dealing with the (repeatable) random replacement of string for deidentification.

Create new Pseudonymizer object

Check if a key exists in lookup

Check if a key exists in lookup

Retrieve a value from lookup

Returns self$lookup formatted as a tibble

Convert self to a list.

Apply the deidentifcation method to the supplied keys

Arguments

lookup	a pre-existing name-value pair to define intended psuedonymizations. Instances of 'name' will be replaced with 'value' on transformation.
keys	value to be checked
...	values to concatenate to key and check
parse_numerics	True: Force columns to characters. NB: only character vectors will be parsed.

Fields

lookup

list of mapping from key-value on transform.

---

Synthetic data set listing daily shift pattern for fictitious employees

Description

A synthetic data set intended to demonstrate the design and application of a deidentification pipeline. Employee names are entirely fictitious and constructed from the ⁠FiveThirtyEight Most Common Name Dataset⁠.

Usage

ShiftsWorked

Format

A data frame with 3,100 rows and 6 columns:

Record ID

Table primary key (integer)

Employee

Name of listed employee

Date

The date being considered

Shift

The shift-type done by employee on date. One of 'Day', 'Night' or 'Rest'.

Shift Start

Shift start time (missing if on 'Rest' shift)

Shift End

Shift end time (missing if on 'Rest' shift)

Daily Pay

Shift end time (missing if on 'Rest' shift)

---

Shuffler class for applying 'shuffling' transform

Description

Create new Shuffler object

Update minimum vector size for shuffling

Apply the deidentifcation method to the supplied keys

Convert self to a list.

Arguments

method	[optional] A function representing the method of re-sampling to be used. By default uses exhaustive sampling without replacement.
keys	Value(s) to be transformed.
...	Value(s) to concatenate to keys and transform @inheritParams Pseudonymizer
limit	integer - the minimum number of observations a variable needs to have for shuffling to be performed. If the variable has length less than limit values are replaced with NAs.

Details

'Shuffling' refers to the a random sampling of a variable without replacement e.g. [A, B, C] becoming [B, A, C] but not [A, A, B]. Shuffling will preserve top level metrics (e.g. mean, median, mode) but removes ordinal properties i.e. correlations and auto-correlations

Fields

limit

minimum vector length to be shuffled. If vector to be transformed has length < limit, the data is replaced with NAs

---

Starwars characters

Description

The original data, from SWAPI, the Star Wars API, https://swapi.py4e.com/, has been revised to reflect additional research into gender and sex determinations of characters. NB: taken from dplyr

Usage

starwars

Format

A tibble with 87 rows and 14 variables:

name

Name of the character

height

Height (cm)

mass

Weight (kg)

hair_color,skin_color,eye_color

Hair, skin, and eye colors

birth_year

Year born (BBY = Before Battle of Yavin)

sex

The biological sex of the character, namely male, female, hermaphroditic, or none (as in the case for Droids).

gender

The gender role or gender identity of the character as determined by their personality or the way they were programmed (as in the case for Droids).

homeworld

Name of homeworld

species

Name of species

films

List of films the character appeared in

vehicles

List of vehicles the character has piloted

starships

List of starships the character has piloted

Examples

starwars

---

Function factory to apply white noise to a vector

Description

Function factory to apply white noise to a vector

Usage

white_noise(sd = 0.1)

Arguments

sd	the standard deviation of noise to apply.

Value

a function

Examples

f <- white_noise(1)
f(1:10)

---