Welcome to PyARXaaS’s documentation!

PyARXaaS is a Python wrapper package for ARXaaS. It provides user-friendly abstractions for the APIs exposed ARXaaS. Github link

• For quick-start see Quick Start
• For more in-depth information about the API see API Documentation .

How it works

PyARXaaS is a simple pure Python client package that provides abstractions for interacting with a ARXaaS instance. The package supports analyzing re-identification risks and anonymizing tabular datasets containing sensitive personal data.

Features

• ARXaaS class for configuration and calling actions.
• Dataset class for encapsulating and configuring a dataset
• Privacy Model classes for configuring the Privacy Models to use in anonymization.
• Easy integration with pandas DataFrames

Simple Use Case

Quick overview of how to get started using the package:

# import dependencies
from pyarxaas import ARXaaS
from pyarxaas.privacy_models import KAnonymity
from pyarxaas import AttributeType
from pyarxaas import Dataset
import pandas as pd

arxaas = ARXaaS(url) # url contains url to AaaS web service

df = pd.read_csv("data.csv")

# create Dataset
dataset = Dataset.from_pandas(df)


# set attribute type
dataset.set_attributes(AttributeType.QUASIIDENTIFYING, 'name', 'gender')
dataset.set_attribute(AttributeType.IDENTIFYING, 'id')

# get the risk profle of the dataset
risk_profile = arxaas.risk_profile(dataset)

# get risk metrics
re_indentifiation_risk = risk_profile.re_identification_risk
distribution_of_risk = risk_profile.distribution_of_risk

Licensing

PyARXaaS is distributed under the MIT license. See LICENCE

Contents:

• User Guide
  • Introduction
    • Architecture overview
  • Installing PyARXaaS
    • Pip install
    • (Optional) Setup virtual environment
    • Get the Source Code
  • Quick Start
    • Analyze the risk of a dataset
    • Anonymize a dataset
  • Using the Dataset class
    • Construction
    • Covert Dataset to other types
    • Mutation
  • Connecting to and using ARXaaS
    • Creating
    • Risk Profile
    • Anonymization
    • Hierarchy Generation
  • Creating Hierarchies
    • Hierarchy Building
• API Documentation
  • ARXaaS
  • Dataset
  • AnonymizeResult
  • RiskProfile
    • RiskProfile contains two main properties
  • Attribute Type
  • AnonymizeMetrics
  • Privacy Model
  • Hierarchy Builders
    • IntervalHierarchyBuilder
    • OrderHierarchyBuilder
    • RedactionHierarchyBuilder
• Example Notebooks
  • Example analyzation and anonymization of sensitive dataset
    • Create ARXaaS connection
    • fetch sensitive data
    • Anonymization Status
    • RiskProfile for the anonymized dataset
  • Hierachy generation using PyARXaaS
    • Create connection to ARXaaS
    • Fetch data
    • 1. Extract column to create hierarchy from
    • 2. Create hierarchy builder to use
    • 3. Call the ARXaaS service to create the hierarchy
    • Redaction hiearchy without configuration
    • 1. Extract column to create hierarchy from
    • 2. Create hierarchy builder to use
    • 3. Add intervals to the builder. The intervals must be continous(without gaps)
    • 4. (Optionally) Add groupings. Groupings are added to a specific level and are order based according to the interval order
    • 3. Call the ARXaaS service to create the hierarchy
    • 1. Extract column to create hierarchy from
    • 2. Strip to uniques
    • 3. Order column values
    • 2. Create hierarchy builder to use
    • 3. Group the values
    • 3. Call the ARXaaS service to create the hierarchy
    • 1. Create the builder

Indices and tables

• Index
• Module Index
• Search Page