anonympy 0.3.7

A Data Anonymization package for tabular, image and PDF data

anonympy 🕶️

With ❤️ by ArtLabs

Overview

General Data Anonymization library for images, PDFs and tabular data. See ArtLabs/projects for more or similar projects.

Main Features

Ease of use - this package was written to be as intuitive as possible.

Tabular

• Efficient - based on pd.DataFrame
• Numerous anonymization methods
• Numeric data
• Generalization - Binning
• Perturbation
• PCA Masking
• Generalization - Rounding
• Categorical data
• Synthetic Data
• Resampling
• Tokenization
• Partial Email Masking
• Datetime data
• Synthetic Date
• Perturbation

Images

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

PDF

• Find sensitive information and cover it with black boxes

Text, Sound

• In Development

Installation

Dependencies

1. Python (>= 3.7)
2. cape-privacy
3. faker
4. pandas
5. OpenCV
6. pytesseract
7. transformers
8. . . . . .

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

More examples here

Tabular

>>> from anonympy.pandas import dfAnonymizer
>>> from anonympy.pandas.utils_pandas import load_dataset

>>> df = load_dataset() 
>>> print(df)

	name	age	birthdate	salary	web	email	ssn
0	Bruce	33	1915-04-17	59234.32	http://www.alandrosenburgcpapc.co.uk	[e-mail]	343554334
1	Tony	48	1970-05-29	49324.53	http://www.capgeminiamerica.co.uk	[e-mail]	656564664

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

	name	age	birthdate	age	web	email	ssn
0	Stephanie Patel	30	1915-05-10	60000.0	5968b7880f	[e-mail]	391-77-9210
1	Daniel Matthews	50	1971-01-21	50000.0	2ae31d40d4	[e-mail]	872-80-9114

# Or applying a specific anonymization technique to a column
>>> from anonympy.pandas.utils_pandas 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',  # {'column_name': 'method_name'}
                  'age': 'numeric_noise',
                  'birthdate': 'datetime_noise',
                  'salary': 'numeric_rounding',
                  'web': 'categorical_tokenization', 
                  'email':'categorical_email_masking', 
                  'ssn': 'column_suppression'})
>>> print(anonym.to_df())

	name	age	birthdate	salary	web	email
0	Paul Lang	31	1915-04-17	60000.0	8ee92fb1bd	j*****[e-mail]
1	Michael Gillespie	42	1970-05-29	50000.0	51b615c92e	e*****[e-mail]

Images

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

>>> img = cv2.imread('salty.jpg')
>>> anonym = imAnonymizer(img)

>>> blurred = anonym.face_blur((31, 31), shape='r', box = 'r')  # blurring shape and bounding box ('r' / 'c')
>>> pixel = anonym.face_pixel(blocks=20, box=None)
>>> sap = anonym.face_SaP(shape = 'c', box=None)

blurred	pixel	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

PDF

In order to initialize pdfAnonymizer object we have to install pytesseract and poppler, and provide path to the binaries of both as arguments or add paths to system variables

>>> from anonympy.pdf import pdfAnonymizer

# need to specify paths, since I don't have them in system variables
>>> anonym = pdfAnonymizer(path_to_pdf = "Downloads\\test.pdf",
                       pytesseract_path = r"C:\Program Files\Tesseract-OCR\tesseract.exe",
                       poppler_path = r"C:\Users\shakhansho\Downloads\Release-22.01.0-0\poppler-22.01.0\Library\bin")

# Calling the generic function
>>> anonym.anonymize(output_path = 'output.pdf',
                     remove_metadata = True,
                     fill = 'black',
                     outline = 'black')

test.pdf	output.pdf

In case you only want to hide specific information, instead of anonymize use other methods

>>> anonym = pdfAnonymizer(path_to_pdf = r"Downloads\test.pdf")
>>> anonym.pdf2images() #  images are stored in anonym.images variable 
>>> anonym.images2text(anonym.images) # texts are stored in anonym.texts

#  Entities of interest 
>>> locs: dict = anonym.find_LOC(anonym.texts[0])  # index refers to page number
>>> emails: dict = anonym.find_emails(anonym.texts[0])  # {page_number: [coords]}
>>> coords: list = locs['page_1'] + emails['page_1'] 

>>> anonym.cover_box(anonym.images[0], coords)
>>> display(anonym.images[0])

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.