anonympy 0.1.3

A Data Anonymization package for tabular, image and sound data

anonympy 🕶️

With :heartpulse: 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
• Binning
• Perturbation
• PCA Masking
• Rounding
• Categorical
• Synthetic Data
• Resampling
• Tokenization
• Email Masking
• DateTime
• Synthetic Date
• Perturbation

Text, Image, Sound

• In development

Installation

Dependencies

1. Python (>= 3.7)
2. cape-privacy
3. faker
4. scikit-learn
5. pandas
6. numpy
7. . . .

Install with pip

Easiest way to install anonympy is using pip

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

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

pip install anonympy

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 

Usage Example

You can find more examples here

from anonympy.pandas import dfAnonymizer
from anonympy.pandas.utils 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	1915-04-17	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 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', 
                  'web': 'categorical_tokenization', 
                  'email':'categorical_email_masking', 
                  'ssn': 'categorical_fake'})
print(anonym.to_df())

	name	age	birthdate	salary	web	email	ssn
0	Paul Lang	33	1915-04-17	59234.32	8ee92fb1bd	j*****[e-mail]	792-82-0468
1	Michael Gillespie	48	1970-05-29	49324.53	51b615c92e	e*****[e-mail]	762-13-6119

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.