Getting started

Example with the adult dataset, anonymizing using three techniques: k-anonymity, \(\ell\)-diversity and t-closeness (the data and hierarchies can be found in the examples folder of the repository):

import pandas as pd
import anjana
from anjana.anonymity import k_anonymity, l_diversity, t_closeness

# Read and process the data
data = pd.read_csv("adult.csv")
data.columns = data.columns.str.strip()
cols = [
    "workclass",
    "education",
    "marital-status",
    "occupation",
    "sex",
    "native-country",
]
for col in cols:
   data[col] = data[col].str.strip()

# Define the identifiers, quasi-identifiers and the sensitive attribute
quasi_ident = [
    "age",
    "education",
    "marital-status",
    "occupation",
    "sex",
    "native-country",
]
ident = ["race"]
sens_att = "salary-class"

# Select the desired level of k, l and t
k = 10
l_div = 2
t = 0.5

# Select the suppression limit allowed
supp_level = 50

# Import the hierarquies for each quasi-identifier. Define a dictionary containing them
hierarchies = {
    "age": dict(pd.read_csv("hierarchies/age.csv", header=None)),
    "education": dict(pd.read_csv("hierarchies/education.csv", header=None)),
    "marital-status": dict(pd.read_csv("hierarchies/marital.csv", header=None)),
    "occupation": dict(pd.read_csv("hierarchies/occupation.csv", header=None)),
    "sex": dict(pd.read_csv("hierarchies/sex.csv", header=None)),
    "native-country": dict(pd.read_csv("hierarchies/country.csv", header=None)),
}

# Apply the three functions: k-anonymity, l-diversity and t-closeness
data_anon = k_anonymity(data, ident, quasi_ident, k, supp_level, hierarchies)
data_anon = l_diversity(
    data_anon, ident, quasi_ident, sens_att, k, l_div, supp_level, hierarchies
)
data_anon = t_closeness(
    data_anon, ident, quasi_ident, sens_att, k, t, supp_level, hierarchies
)

Note

Applying the three techniques outlined above on the given dataset (with more than 30,000 rows), and with 6 quasi-identifiers, takes less than 4 seconds.

Define your own hierarchies

All the anonymity functions available in ANJANA receive a dictionary with the hierarchies to be applied to the quasi-identifiers. In particular, this dictionary has as key the names of the columns that are quasi-identifiers to which a hierarchy is to be applied (it may happen that you do not want to generalize some QIs and therefore no hierarchy is to be applied to them, just do not include them in this dictionary). The value for each key (QI) is formed by a dictionary in such a way that the value 0 has as value the raw column (as it is in the original dataset), the value 1 corresponds to the first level of transformation to be applied, in relation to the values of the original column, and so on with as many keys as levels of hierarchies have been established.

For a better understanding, let’s look at the following example. Supose that we have the following simulated dataset (extracted from the hospital_extended.csv dataset used for testing purposes) with age, gender and city as quasi-identifiers, name as identifier and disease as sensitive attribute. Regarding the QI, we want to apply the following hierarquies: interval of 5 years (first level) and 10 years (second level) for the age. Suppression as first level for both gender and city.

name	age	gender	city	disease
Ramsha	29	Female	Tamil Nadu	Cancer
Yadu	24	Female	Kerala	Viral infection
Salima	28	Female	Tamil Nadu	TB
Sunny	27	Male	Karnataka	No illness
Joan	24	Female	Kerala	Heart-related
Bahuksana	23	Male	Karnataka	TB
Rambha	19	Male	Kerala	Cancer
Kishor	29	Male	Karnataka	Heart-related
Johnson	17	Male	Kerala	Heart-related
John	19	Male	Kerala	Viral infection

Then, in order to create the hierarchies we can define the following dictionary:

age = data['age'].values
# Values: [29 24 28 27 24 23 19 29 17 19] (note that the following can be automatized)
age_5years = ['[25, 30)', '[20, 25)', '[25, 30)',
              '[25, 30)', '[20, 25)', '[20, 25)',
              '[15, 20)', '[25, 30)', '[15, 20)', '[15, 20)']

age_10years = ['[20, 30)', '[20, 30)', '[20, 30)',
               '[20, 30)', '[20, 30)', '[20, 30)',
               '[10, 20)', '[20, 30)', '[10, 20)', '[10, 20)']

hierarchies = {
    "age": {0: age,
            1: age_5years,
            2: age_10years},
    "gender": {
        0: data["gender"].values,
        1: np.array(["*"] * len(data["gender"].values)) # Suppression
    },
    "city": {0: data["city"].values,
             1: np.array(["*"] * len(data["city"].values))} # Suppression
}

In addition, we can also use the function _generate_intervals()_ from _utils_ for creating the interval-based hierarchy as follows:

import numpy as np
from anjana.anonymity import utils

age = data['age'].values

hierarchies = {
    "age": {
        0: data["age"].values,
        1: utils.generate_intervals(data["age"].values, 0, 100, 5),
        2: utils.generate_intervals(data["age"].values, 0, 100, 10),
    },
    "gender": {
        0: data["gender"].values,
        1: np.array(["*"] * len(data["gender"].values)) # Suppression
    },
    "city": {0: data["city"].values,
             1: np.array(["*"] * len(data["city"].values))} # Suppression
}