anonympy 🕶️

With ❤️ by ArtLabs

Overview

A general Python library for data anonymization of tabular, text, image and sound data. See ArtLabs/projects for more or similar projects.

Main Features

Tabular

• Ease of use
• Efficient anonymization (based on pandas DataFrame)
• Numerous anonymization techniques
• Numeric
• Generalization - Binning
• Perturbation
• PCA Masking
• Generalization - Rounding
• Categorical
• Synthetic Data
• Resampling
• Tokenization
• Partial Email Masking
• DateTime
• Synthetic Date
• Perturbation

Images

• Anonymization Techniques
• Personal Images (faces)
• Blurring
• Pixaled Face Blurring
• Salt and Pepper Noise
• General Images
• Blurring

Text, Sound

• In Development

Installation

Dependencies

1. Python (>= 3.7)
2. cape-privacy
3. faker
4. pandas
5. OpenCV
6. . . .

Install with pip

Easiest way to install anonympy is using pip

pip install anonympy

Due to conflicting pandas/numpy versions with cape-privacy, it's recommend to install them seperately

pip install cape-privacy==0.3.0 --no-deps 

Install from source

Installing the library from source code is also possible

git clone https://github.com/ArtLabss/open-data-anonimizer.git
cd open-data-anonimizer
pip install -r requirements.txt
make bootstrap
pip install cape-privacy==0.3.0 --no-deps 

Downloading Repository

Or you could download this repository from pypi and run the following:

cd open-data-anonimizer
python setup.py install

Usage Example

You can find more examples here

Tabular

from anonympy.pandas import dfAnonymizer
from anonympy.pandas.utils import load_dataset

df = load_dataset() 
print(df)

# Calling the generic Function
anonym = dfAnonymizer(df)
anonym.anonymize(inplace = False) # changes will be returned, not applied

# Or applying a specific anonymization technique to a column
from anonympy.pandas.utils import available_methods

anonym.categorical_columns
... ['name', 'web', 'email', 'ssn']
available_methods('categorical') 
... categorical_fake	categorical_fake_auto	categorical_resampling	categorical_tokenization	categorical_email_masking
  
anonym.anonymize({'name': 'categorical_fake', 
                  'age': 'numeric_noise',
                  'birthdate': 'datetime_noise',
                  'salary': 'numeric_rounding',
                  'web': 'categorical_tokenization', 
                  'email':'categorical_email_masking', 
                  'ssn': 'column_suppression'})
print(anonym.to_df())

Images

# Passing an Image
import cv2
from anonympy.images import imAnonymizer

img = cv2.imread('sulking_boy.jpg')
anonym = imAnonymizer(img)

blurred = anonym.face_blur((31, 31), shape='r', box = 'r')  # blurring shape and bounding box ('r' / 'c')
cv2.imshow('Blurred', blurred)

anonym.face_blur()	anonym.face_pixel()	anonym.face_SaP()

# Passing a Folder 
path = 'C:/Users/shakhansho.sabzaliev/Downloads/Data' # images are inside `Data` folder
dst = 'D:/' # destination folder
anonym = imAnonymizer(path, dst)

anonym.blur(method = 'median', kernel = 11) 

This will create a folder Output in dst directory.

The Data folder had the following structure

|   1.jpg
|   2.jpg
|   3.jpeg
|   
\---test
    |   4.png
    |   5.jpeg
    |   
    \---test2
            6.png

The Output folder will have the same structure and file names but blurred images.

Development

Contributions

The Contributing Guide has detailed information about contributing code and documentation.

Important Links

• Official source code repo: https://github.com/ArtLabss/open-data-anonimizer
• Download releases: https://pypi.org/project/anonympy/
• Issue tracker: https://github.com/ArtLabss/open-data-anonimizer/issues

License

BSD 3

Code of Conduct

Please see Code of Conduct. All community members are expected to follow it.