Anonymizer tool for datasets such CSV files
Project description
Anonymizer tool for datasets such CSV files.
Uses the excelent library mimesis to generate fake data.
Install
Using pip:
pip install datanonymizerOr from source:
git clone https://github.com/fgmacedo/datanonymizer
cd datanonymizer
python setup.py installUsage
Pass your data through stdin and get it back anonymized on stdout.
cat input_file.csv | datanonymizer >output_file.csvUsing a config file to declare conversions and generators for the required fields:
cat input_file.csv | datanonymizer --config ./dataset_anon_config.yml >output_file.csvOptional arguments:
-h, --help show this help message and exit
-l LANGUAGE, --language LANGUAGE
Language used by the Generator
-di DELIMITER_INPUT, --delimiter_input DELIMITER_INPUT
CSV delimiter
-do DELIMITER_OUTPUT, --delimiter_output DELIMITER_OUTPUT
CSV delimiter
--head HEAD Outputs only the first <head> lines
--seed SEED Seed for the pseudo random generator providers
--config CONFIG Configuration fileConfig file
You’l need a configuration file to setup transformations for each dataset.
This file is a simple yaml where you can configure fields.
Field names should match the column name declared into the CSV input file.
---
fields:
Task ID:
omit: true
Location:
conversions:
- fn: coords_to_h3
kwargs:
resolution: 8
Client Address:
conversions:
- fn: has_value
rename: has_address
Company Name:
generator:
provider: business.company
rename: company
Invoice ID:
generator:
provider: person.identifier
kwargs:
mask: "#######"
rename: invoiceGenerators
You can use any generator available at the generic API from mimesis.
For example, if you wanna mimic data with company names:
---
fields:
Company Name:
generator:
provider: business.companyBut you can replace the real names by names of fruits:
---
fields:
Company Name:
generator:
provider: food.fruitConversions
You can apply any pre-configured conversion functions available.
coords_to_h3
has_value
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