sparknlp_jsl.structured_deidentification#

Classes#

class StructuredDeidentification(spark: pyspark.sql.SparkSession, columns: Dict[str, str], columnsSeed: Dict[str, int] = None, obfuscateRefFile: str = '', obfuscateRefSource: str = 'both', days: int = 0, useRandomDateDisplacement: bool = False, dateFormats: List[str] = None, language: str = 'en', idColumn: str = '')#

A helper class that allow to obfuscate a structured deidentification.

Parameters:

• columns (dict) –

  Is a map that allows to select the name of the column with the entity.

  The key of the map is the column in the dataframe and the value of the map is the entity for that column.

  The default entities are: “location” A general location. “location-other” A location that is not country, street,hospital,city or state “street” A street “hospital” The name of a hospital. “city” A city “state” A state “zip” The zip code “country” A country “contact” The contact of one person “username” A username “phone” A number phone. “fax” The number fax “url” A url for internet “email” The email of one person “profession” A profession of one person “name” The name opf one person “doctor” The name of a doctor “patient” The name of the patient “id” A general ID number “bioid” Is a system to screen for protein interactions as they occur in living cells “age” The age of something or someone “organization” Name of one organization o company “healthplan” The id that identify the healthplan “medicalrecord” The identification of a medical record “device” The id that identified a device “date” A general date “ssn” A Social Security Number “ip” A Internet Protocol “passport” A random passport “dln” A Driver’s License Number “npi” A National Provider Identifier “c_card” A credit card number “iban” A International Bank Account Number “dea” A Drug Enforcement Administration

• columnsSeed (int) – Allow to add a seed to the column that you want to obfuscate.The seed used to randomly select the entities used during obfuscation mode.

• obfuscateRefFile (bool) – This is an optional parameter that allows to add your own terms to be used for obfuscation. The file contains as a key the entity and as the value the terms that will be used in the obfuscation.

• days (int) – Number of days to obfuscate the dates by displacement. If not provided a random integer between 1 and 60 will be used

• useRandomDateDisplacement (bool) – Use a random displacement days in dates entities.If true use random displacement days in dates entities,if false use the days.

• dateFormats (List[str]) – List of date formats.example [“dd-MM-yyyy”, “dd/MM/yyyy”, “d/M/yyyy”, “dd-MM-yyyy”, “d-M-yyyy”]

• language – The language used to select faker entities. The values are the following: ‘en’(English), ‘de’(German), ‘es’(Spanish), ‘fr’(French), ‘ar’(Arabic) or ‘ro’(Romanian). Default:’en’.

• idColumn (str) – The column that contains the id of the row. If provided, data will obfuscate consistently by idColumn, especially date entities.

columns#

instance#

obfuscateRefFile#

spark#

obfuscateColumns(df: pyspark.sql.DataFrame)#

Obfuscate the columns of a dataframe.

Parameters:

df (DataFrame) – The dataframe to obfuscate

Returns:

A dataframe with the columns obfuscated

Return type:

DataFrame