Persistent Data Anonymization Pipeline
Function factory to apply white noise to a vector proportional to the ...
De-identification via categorical aggregation
De-identification via hash encryption
Add aggregation to pipelines
De-identification via numeric aggregation
De-identification via random noise
De-identification via replacement
De-identification via random sampling
Apply a 'deident' pipeline
Apply a 'deident' pipeline to a new data frame
Base class for all De-identifier classes
Deidentifier class for applying 'blur' transform
Utility for producing 'blur'
Create a deident pipeline
Apply a pipeline to files on disk.
Define a transformation pipeline
R6 class for the removal of variables from a pipeline
Deidentifier class for applying 'encryption' transform
Restore a serialized deident from file
GroupedShuffler class for applying 'shuffling' transform with data agg...
Function factory to apply log-normal noise to a vector
Deidentification API root
Group numeric data into baskets
R6 class for deidentification via random noise
R6 class for deidentification via replacement
Shuffler class for applying 'shuffling' transform
Tidy eval helpers
Function factory to apply white noise to a vector
A framework for the replicable removal of personally identifiable data (PID) in data sets. The package implements a suite of methods to suit different data types based on the suggestions of Garfinkel (2015) <doi:10.6028/NIST.IR.8053> and the ICO "Guidelines on Anonymization" (2012) <https://ico.org.uk/media/1061/anonymisation-code.pdf>.