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Description:

piianonymizer 0.2.5

Data Protection Framework
Data Protection Framework is a python library/command line application for identification, anonymization and de-anonymization of Personally Identifiable Information data.
The framework aims to work on a two-fold principle for detecting PII:

Using RegularExpressions using a pattern
Using NLP for detecting NER (Named Entity Recognitions)

Common Usage

pip install pii-anonymizer
Specify configs in pii-anonymizer.json
Choose whether to run in standalone or spark mode with python -m pii_anonymizer.standalone or python -m pii_anonymizer.spark

Features and Current Status
Completed

Following Global detectors have been completed:

EMAIL_ADDRESS : An email address identifies the mailbox that emails are sent to or from. The maximum length of the domain name is 255 characters, and the maximum length of the local-part is 64 characters.
CREDIT_CARD_NUMBER : A credit card number is 12 to 19 digits long. They are used for payment transactions globally.

Following detectors specific to Singapore have been completed:

PHONE_NUMBER : A telephone number.
FIN/NRIC : A unique set of nine alpha-numeric characters on the Singapore National Registration Identity Card.
THAI_ID : 13 numeric digits of Thai Citizen ID

Following anonymizers have been added

Replacement ('replace'): Replaces a detected sensitive value with a specified surrogate value. Leave the value empty to simply delete detected sensitive value.
Hash ('hash'): Hash detected sensitive value with sha256.
Encryption: Encrypts the original sensitive data value using a Fernet (AES based).

Currently supported file formats: csv, parquet
Encryption
To use encryption as anonymize mode, a compatible encryption key needs to be created and assigned to PII_SECRET environment variables. Compatible key can be generated with
python -m pii_anonymizer.key
This will generate output similar to
Keep this encrypt key safe
81AOjk7NV66O62QpnFsvCXH8BDB26KM9TIH7pBfZ6PQ=

To set this key as an environment variable run
export PII_SECRET=81AOjk7NV66O62QpnFsvCXH8BDB26KM9TIH7pBfZ6PQ=
TO-DO
Following features are part of the backlog with more features coming soon

Detectors:

NAME
ADDRESS

Anonymizers:

Masking: Replaces a number of characters of a sensitive value with a specified surrogate character, such as a hash (#) or asterisk (*).
Bucketing: "Generalizes" a sensitive value by replacing it with a range of values. (For example, replacing a specific age with an age range,
or temperatures with ranges corresponding to "Hot," "Medium," and "Cold.")

You can have a detailed at upcoming features and backlog in this Github Board
Development setup

Install Poetry
Setup hooks and install packages with make install

Config JSON
Limitation: when reading multiple files, all files that matches the file_path must have same headers. Additionally, when file format is not given anonymizer will assume that the file format is the first matched filename. Thus, when the file_path ends with /* and the folder contains mixed file format, the operation will fail.
An example for the config JSON is located at <PROJECT_ROOT>/pii-anonymizer.json
{
"acquire": {
"file_path": <FILE PATH TO YOUR INPUT CSV>, -> ./input_data/file.csv or ./input_data/*.csv to read all files that matches
"delimiter": <YOUR CSV DELIMITER>
},
"analyze": {
"exclude": ['Exception']
},
"anonymize": {
"mode": <replace|hash|encrypt>,
"value": "string to replace",
"output_file_path" : <PATH TO YOUR CSV OUTPUT FOLDER>,
"output_file_format": <csv|parquet>,
"output_file_name": "anonymized" -> optionally, specify the output filename.
},
"report" : {
"location" : <PATH TO YOUR REPORT OUTPUT FOLDER>,
"level" : <LOG LEVEL>
}
}

Running Tests
You can run the tests by running make test or triggering shell script located at <PROJECT_ROOT>/bin/run_tests.sh
Trying out on local
Anonymizing a delimited csv file

Set up a JSON config file similar to the one seen at the project root.
In the 'acquire' section of the json, populate the input file path and the delimiter.
In the 'report' section, provide the output path, where you want the PII detection report to be generated.
A 'high' level report just calls out which columns have PII attributes.
A 'medium' level report calls out the percentage of PII in each column and the associated PII (email, credit card, etc)type for the same.
Run the main class - python -m pii_anonymizer.standalone --config <optionally, path of the config file or leave blank to defaults to pii-anonymizer.json>
You should see the report being appended to the file named 'report_<date>.log' in the output path specified in the
config file.

Packaging
Run poetry build and the .whl file will be created in the dist folder.
Licensing
Distributed under the MIT license. See LICENSE for more information.
Contributing
You want to help out? Awesome!