Anonymizing collections of documents

In the previous blog post, we showed how one can anonymize text in document form. While the code is useful for processing a single document, anonymizing a collection of documents can take time if we run the script for each document separately.

In this blog post, we show how one can anonymize collections of documents. The process is similar to the previous blog post, but loads all required components only once, and anonymizes all documents in one go.

Prerequisites

To use the anonipy package, we must have Python version 3.8 or higher installed on the machine.

Installation

Before we start, we must first install the anonipy package. To do that, run the following command in the terminal:

pip install anonipy

This will install the anonipy package, which contains all of the required modules.

If you already installed it and would like to update it, run the following command:

pip install anonipy --upgrade

Preparing the components

First, let us prepare all of the components for the anonymization process. It consists of preparing the entity extractor, the anonymization strategy, and the generators for the anonymization process.

from anonipy.anonymize.extractors import EntityExtractor
from anonipy.anonymize.generators import (
    MaskLabelGenerator,
    DateGenerator,
    NumberGenerator,
)
from anonipy.anonymize.strategies import PseudonymizationStrategy
from anonipy.constants import LANGUAGES

# =====================================
# Prepare the entity extractor
# =====================================

# define the labels to be extracted and their types
labels = [
    {"label": "name", "type": "string"},
    {"label": "social security number", "type": "custom"},
    {"label": "date of birth", "type": "date"},
    {"label": "date", "type": "date"},
]

# initialize the entity extractor
entity_extractor = EntityExtractor(
    labels, lang=LANGUAGES.ENGLISH, score_th=0.5
)

# =====================================
# Prepare the anonymization strategy
# =====================================

# initialize the generators
mask_generator = MaskLabelGenerator()
date_generator = DateGenerator()
number_generator = NumberGenerator()

# prepare the anonymization mapping
def anonymization_mapping(text, entity):
    if entity.type == "string":
        return mask_generator.generate(entity, text)
    if entity.label == "date":
        return date_generator.generate(entity, output_gen="middle_of_the_month")
    if entity.label == "date of birth":
        return date_generator.generate(entity, output_gen="middle_of_the_year")
    if entity.label == "social security number":
        return number_generator.generate(entity)
    return "[REDACTED]"

# initialize the pseudonymization strategy
pseudo_strategy = PseudonymizationStrategy(mapping=anonymization_mapping)

Anonymizing the collection of documents

Now, let us anonymize all of the documents in the collection. We first prepare a folder containing the documents, that we want to anonymize. The path to the folder will be available via the input_folder variable.

We will, one-by-one, read all of the documents in the folder, extract the text from each document, and anonymize the text.

Finally, we will store the anonymized text in another folder. The path to the folder will be available via the output_folder variable.

import os
from os.path import isfile, join
from anonipy.utils.file_system import open_file, write_file, write_json

# prepare the input and output folder paths
input_folder = "path/to/input/folder"
output_folder = "path/to/output/folder"

# prepare a list of file paths in the input folder
file_names = [
    f for f in os.listdir(input_folder) if isfile(join(input_folder, f))
]

# iterate through each file
for file_name in file_names:
    # extract the text from the document
    file_text = open_file(join(input_folder, file_name))
    # extract the entities from the text
    doc, entities = entity_extractor(file_text)
    # anonymize the text
    anonymized_text, replacements = pseudo_strategy.anonymize(file_text, entities)
    # write the anonymized text into the output folder
    output_file_name = ".".join(file_name.split(".")[:-1]) + "_anonymized"
    write_file(anonymized_text, join(output_folder, output_file_name) + ".txt")
    write_json(replacements, join(output_folder, output_file_name) + ".json")

Given the above code, we can anonymize all of the documents in the collection without loading and preparing the extractor, generators and strategy every time.

Conclusion

In this blog post, we show how one can anonymize collections of documents in out go using the anonipy package. We first prepare the components for the anonymization process. We then find all of the files we want to anonymize and anonymize them. Each anonymized file is finally stored in a separate folder which contains the anonymized text.