Privacy Masks

Synthesized provides a variety of masks to anonymize parts of data for privacy purposes. The privacy masks replace the most identifying fields within a data record with an artificial pseudonym.

Synthesized enables data masking through the following transformers:


FormatPreservingMask can apply generic format-preserving hashing transformations for a given regex pattern.

from synthesized.privacy import FormatPreservingMask
import pandas as pd

df = pd.DataFrame({'Id': ['AAA001', 'BBB001', 'AAA002', 'AAA001', 'BBB001']})
transformer = FormatPreservingMask('Id', pattern=r'[abc]-\d{3}')

IDs that are the same in the original DataFrame are also the same in the transformed DataFrame.


NanMask masks the data by nulling out a given column.

The following example illustrates it:

from synthesized.privacy import NanMask
import pandas as pd

df = pd.DataFrame({'card_no': ['490 508 10L', 'ff4sff4', 'jdj DFj 34', '123POFjd33', '2334 fgg4 223', 'djdjjf 83838jd83', '123 453']})
transformer = NanMask(name='card_no')


RoundingMask masks a numerical column by binning the values to N bins. Arg n_bins determines the number of bins to bin the value range of the column, the default value is 20.

The following example illustrates it:

from synthesized.privacy import RoundingMask
import pandas as pd
import numpy as np

df = pd.DataFrame({'age': np.random.randint(1, 97, size=(5000,))})
transformer = RoundingMask(name='age', n_bins=10)


MaskingFactory can be used to create a set of data masking transformers, as described above, to transform multiple columns of a DataFrame in the same function call. To demonstrate this, we will use the following example DataFrame:

from synthesized.privacy import MaskingFactory
from faker import Faker
import pandas as pd

fkr = Faker()
df = pd.DataFrame({'Username': [fkr.user_name() for _ in range(1000)],
                    'Name': [ for _ in range(1000)],
                    'Password': [fkr.password() for _ in range(1000)],
                    'CreditCardNo': [fkr.credit_card_number() for _ in range(1000)],
                    'Age': [fkr.pyint(min_value=10, max_value=78) for _ in range(1000)],
                    'MonthlyIncome': [fkr.pyint(min_value=1000, max_value=10000) for _ in range(1000)]})

In order to create a set of transformers to act on this DataFrame a MaskingFactory object is first created. A config dictionary object is then supplied to the create_masks() method of this factory, in order to specify the masking technique to use on specific columns in the DataFrame:

masking_factory = MaskingFactory()
config = {
    "rounding": [
        {"name": "Age",
        "nbins": "20"},
        {"name": "MonthlyIncome",
        "nbins": "3"}
    "nan": [
        {"name": "Password"},
        {"name" : "CreditCardNo"}
privacy_masks = masking_factory.create_masks(df, config)
privacy_masks.transform(df, inplace=True)

The possible keys for the config dictionary are the appropriate keywords for each masking technique, as described above. The values are then lists containing dictionaries that specify the name of the column to be masked, using the name keyword, as well as any additional arguments to be passed to the appropriate mask. For instance, in the above example, the columns Age and MonthlyIncome are masked using the RoundingMask with nbins set for each column independently. The columns Password and CreditCardNo are masked using the NanMask which requires no additional arguments.