redacted

1️⃣ version: 0.2.1

✍️ author: Mitchell Lisle

📛 An experimental data anonymisation library

Install

pip install redacted

Usage

Anonymiser

from redacted import Anonymiser, AusPostCode, AusDriversLicence

anonymiser = Anonymiser(info_types=[AusPostCode, AusDriversLicence])

# returns an AnonymisedText Object with metadata and information and text replaced with a like-for-like example
anonymised = anonymiser.anonymise('Milhouse Van Houten 2203 18423441')
anonymised.text # Milhouse Van Houten 7862 R90715

The AnonymisedText object contains all information about what was matched. The example below shows matches that we found with some information about where in the string they occurerd. info_types is a list of all values that we looked for in the given string.

AnonymisedText(
    original='Milhouse Van Houten 2203 18423441',
    text='Milhouse Van Houten 7862 R90715',
    matches=[
        Match(text='2203', start=20, end=24, len=4, type=<class 'redacted.info_types.AusPostCode'>),
        Match(text='18423441', start=25, end=33, len=8, type=<class 'redacted.info_types.AusDriversLicence'>)
    ],
    info_types=[
        <class 'redacted.info_types.AusPostCode'>,
        <class 'redacted.info_types.AusDriversLicence'>
    ]
)

Info Types

The core of what this library does is use regex expressions to look for values in a given string. If we find a match there is a replacement strategy for each info type that we can use to replace the value in the string. The current list of info types is (code for these can be found in redacted.info_types:

Email,
AusPassport,
AusDriversLicence,
AusTaxFileNumber,
AusPostCode,
AusLicensePlate,
LongDigit

⚠️ The order is important when passing them in to the Anonymiser class. If we match on an info type at the beginning, subsequent matches will be ignored. More generic types (such as LongDigit) should be placed at the end so we don't capture too many non-specific matches. For example:

from redacted import Anonymiser, LongDigit, AusDriversLicence

anonymiser = Anonymiser(info_types=[LongDigit, AusDriversLicence])

# The following is a AusDriversLicence number, but because LongDigit is also a match,
# we would match this as `LongDigit` which is less specific. In some cases we might want to prefer
# LongDigit over LicenceNumber, this is left to you to decide when setting up your info_types.
anonymised = anonymiser.anonymise('18423441')
anonymised.text # 4563456