Skip to content

Latest commit

 

History

History
77 lines (59 loc) · 2.44 KB

tutorial.md

File metadata and controls

77 lines (59 loc) · 2.44 KB

Data anonymization with python Mysto

In this tutorial, we'll use Mysto to anonymize columuns in a sample data set.

Prerequisites:

  • Python3
  • The following Python packages are required:
    • pandas (pip3 install pandas)
    • pycryptodome (pip3 install pycryptodome)
    • ff3 (pip3 install ff3)

This tutorial is paired with a Jupyter notebook file, but it can be run just fine from the command line also.

To begin with, start a Jupyter notebook using the tutorial file

jupyter notebook MystoTutorial.ipynb &

As a first step, load the sample CSV file. We use the Pandas CSV reader which provides excellent handling of data types, especially dates.

df=main.load_csv('https://raw.githubusercontent.com/mysto/python-mysto/main/samples/CCSampleData.csv', {"Zipcode": str}, date_types=['Birth Date'])
  1. Define HIPAA rules for generalizing zipcode and birth date colums
rules = [
    '{"column" : "Zipcode", "type" : "us_zip_code"}',
    '{"column" : "Birth Date", "type" : "Generalize.Date", "format" : "5"}',
]

df1 = main.anonymize(df, rules)
df1

In the output you'll see for zipcode, last two digit are generalized with zeros and birth date has been generalized to year.

  1. Next we'll add rules to clip first and last name and mask the account number
rules = [
    '{"column" : "Zipcode", "type" : "us_zip_code"}',
    '{"column" : "Birth Date", "type" : "Generalize.Date", "format" : "5"}',
    
    '{"column" : "First name", "type" : "clipl", "n" : "1"}',
    '{"column" : "Last name", "type" : "clipl", "n" : "3"}',
    '{"column" : "Acct num", "type" : "Mask", "format" : "5"}'
]

df2 = main.anonymize(df, rules)
df2
  1. Finally, we will tokenize the SSN and CSN columns with format-preserving encryption and redact the account number column.
rules = [
    '{"column" : "Zipcode", "type" : "us_zip_code"}',
    '{"column" : "Birth Date", "type" : "Generalize.Date", "format" : "5"}',
    
    '{"column" : "First name", "type" : "clipl", "n" : "1"}',
    '{"column" : "Last name", "type" : "clipl", "n" : "3"}',
    '{"column" : "Acct num", "type" : "Mask", "format" : "5"}',
    
    '{"column" : "SSN", "type" : "FF3", "format" : "000-00-0000", "sep" : "-"}',
    '{"column" : "Canadian SIN", "type" : "FF3", "format" : "000-000-000"}',
    '{"column" : "Acct num", "type" : "Mask", "format" : "5"}'
]

main.initialize_fpe("EF4359D8D580AA4F7F036D6F04FC6A94", "D8E7920AFA330A73")

df3 = main.anonymize(df, rules)
df3