Quick Start¶

This page gives a good introduction in how to get started with PyARXaaS

First, make sure that:

PyARXaaS is installed
PyARXaaS is up-to-date
You have a tabular dataset to use
You hava a running ARXaaS instance to connect to.
- Instructions on how to run ARXaaS can be found here: https://github.com/oslomet-arx-as-a-service/ARXaaS/blob/master/README.md
If you are going to anonymize a dataset, you need to have the required hierarchies. See anonymize section for more information

Let’s get started with some simple examples.

Analyze the risk of a dataset¶

Analyze the risk of a dataset using PyARXaaS is very simple.

Begin by importing the Dataset class and pandas which we are going to use to create a Dataset
```
from pyarxaas import Dataset
import pandas as pd
```

Then we create a Dataset from a local csv file

Note

The dataset in this example contains the columns/fields id, name, gender
dataframe = pd.read_csv("data.csv", sep=";")
# create Dataset
dataset = Dataset.from_pandas(dataframe)
The Dataset class encapsulates the raw data, attribute types of the dataset fields and hierarchies

Then we set the Attribute Type for the Dataset fields.

# import the attribute_type module
 from pyarxaas import AttributeType

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

To make a call to the ARXaaS instance we need to make a instance of the ARXaaS class.

The AaaS connector class needs a url to the ARXaaS instance. In this example we have ARXaaS running locally.
# import the ARXaaS class
from pyarxaas import ARXaaS

# establishing a connection to the ARXaaS service using a URL
arxaas = ARXaaS("http://localhost:8080")

After the ARXaaS object is created we can use it to call the ARXaaS instance to make a RiskProfile for our Dataset.
```
# get the risk profle of the dataset
risk_profile = arxaas.risk_profile(dataset)
```

The RiskProfile contains two properties; re-indentification risks and distributed risks. The two properties contains the different risks and the distribution of risks for the Dataset.
# get risk metrics as a dictionary
re_indentifiation_risk = risk_profile.re_identification_risk
distribution_of_risk = risk_profile.distribution_of_risk

# get risk metrivs as pandas.DataFrame
re_i_risk_df = risk_profile.distribution_of_risk_dataframe()
dist_risk_df = risk_profile.distribution_of_risk_dataframe()

Anonymize a dataset¶

Anonymizing a dataset using PyARXaaS.

Begin by importing the Dataset class and pandas which we are going to use to create a Dataset
```
from pyarxaas import Dataset
import pandas as pd
```

Same as when in analyze we set the attribute type for the dataset fields:

# import the attribute_type module
 from pyarxaas import AttributeType

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

3. In addition to setting attribute types we need to provide Transformation Models known as hierarchies for the dataset fields/columns with type AttributeType.QUASIIDENTIFYING Hierarchies can be added as pandas.DataFrame objects:

# importing the hierarchies from a local csv file. Specify the file path as the first parameter
id_hierarchy = pd.read_csv("id_hierarchy.csv", header=None)
name_hierarchy = pd.read_csv("name_hierarchy.csv", header=None)

# setting the imported csv file. Specify the column name as the fist parameter, and the hierarchy as the second parameter
dataset.set_hierarchy('id', id_hierarchy)
dataset.set_hierarchy('name', name_hierarchy)

When anonymizing we need to supply a Privacy Model for ARXaaS to run on the dataset. You can read more about the models here ARX Privacy Models

# importing the privacy_models module
from pyarxaas.privacy_models import KAnonymity

# creating a privacy_models object
kanon = KAnonymity(4)

To make a call to the ARXaaS instance we need to make a instance of the AaaS class. The AaaS connector class needs a url to the ARXaaS instance. In this example we have ARXaaS running locally.
```
# import the aaas module
from pyarxaas import ARXaaS

# establishing a connection to the ARXaaS service using the URL
arxaas = ARXaaS("http://localhost:8080")
```

After the ARXaaS object is created we can use it to call the ARXaaS instance. Back if the anonymization is successful we receive an AnonymizeResult

# specify the dataset as the first parameter, and privacy model list as the second paramter
anonymize_result = arxxaas.anonymize(dataset, [kanon])

AnonymizeResult contains the new Dataset, the RiskProfile for the new , the Dataset, the anonymization status for the Dataset and AnonymizeMetrics which contains metrics regarding the anonymzation performed on the dataset.

# get the new dataset
anonymized_dataset = anonymize_result.dataset
anon_dataframe = anonymized_dataset.to_dataframe()

# get the risk profile for the new dataset
anon_risk_profile = anonymize_result.risk_profile

# get risk metrics as a dictionary
re_indentifiation_risk = anon_risk_profile.re_identification_risk
distribution_of_risk = anon_risk_profile.distribution_of_risk

# get risk metrivs as pandas.DataFrame
re_i_risk_df = anon_risk_profile.distribution_of_risk_dataframe()
dist_risk_df = anon_risk_profile.distribution_of_risk_dataframe()

# get the anonymiztion metrics
anon_metrics = anonymize_result.anonymization_metrics