kanonym4text 0.2.7

k-anonymity for texts

kanonym4text K-anonymity guerantee for texts

K Anonymity for text (or in short - kanonym4text) is an open‑sourced library that receives a corpus from the user (dataframe) and returns a K-anonymized corpus with additional information about the anonymization process performed. kanon4txet is designed to be easily utilized, to guarantee anonymization at a certain level pre-defined by the user (k) while still preserving some of the text utilization properties. This repo and package are part of our Y-Data data science final project, and we would love to hear your feedback and learn from it!

Overview

In this project, we aim to apply data science techniques to anonymize textual data while preserving their utility. K-Anonymity is a technique used to ensure that an individual in a dataset cannot be identified by linking their attributes to external information, by forcing each row to be identical to k-1 other rows The anonymized data can be used for various purposes like data sharing, research, and analysis without compromising privacy. We plan on creating a novel algorithm for k-anonymity. Specifically, we address the case of unstructured data items, such as texts. Using various NLP techniques, from classical to modern DL-based solutions, and testing the utility of the anonymized data. We have tested the library on two main datasets:

1. Amazon dataset,
2. Enron emails dataset

We show it is able to generate anonymized corpora in both cases.

Package High-Level Algorithm:

1. Data Preprocessing: tokenization, lemmatization, and stop word removal.
2. K-Anonymization: Generalization and Reduction.
3. Evaluation: Utilization is evaluated by embedding distance and semantic scores.
4. Visualization: dataset properties prior to and post anonymization will be visualized (optional)

Getting Started

You can get started with kanonym4text immediately by installing it with pip:

download package

pip install kanonym4text

Step 1 - creat a data frame from your corpus

The code receives a data frame containing the corpus in the following format: "txt" - column with the texts (default column name)

Step 2 - import the following and initializethe object

Use gensim word embedding model for you choice (default - glove-twitter-25)

from kanonym4text import Kanonym
kan = Kanonym()

Step 3 - read the df:

import pandas as pd
df = pd.read_csv('YOUR_FILE.csv')

Step 4 - run the anonymization process on your corpus

dfa, dist = kan.anonymize(df, k=2)

Running instructions: The main function is called anonymize.

It's input parameters are:

• df - Input Dataframe
• k - k
• col - The column in df that holds the text to anonymize. Default - txt
• num_stop - Number of stop word to use. Default - 1000
• num_jobs - Number of CPUs to utilize. Default - 1. All CPUs - -1.
• verbose - Output text level. Default - 0.

The function outputs are:

A tuple with these items:

1. A dataframe - the same df the user sent with additional columns:

1. general_txt - text after "generalization"
2. anon_txt_history - changes performed on text during annonymization process:
   [] - replaced
   {} - Lemmatize
   () - protected word (stop-word)
3. anon_txt - resulted anonymized text
4. neighbors - indeces of k neigbors (Bow anonymized)

2. An averaged cosine distance of sentence embedding for the documents before and after the anonymizaion

Running Example

use the following link to run some examples of the package on your own dataframe:

https://colab.research.google.com/drive/1eMSSvBxtsNFMOvKrUXgbsx1g3KOQD56s#scrollTo=ci2qjboGCt0A&uniqifier=1

or use the following code:

from kanonym4text import Kanonym
import pandas as pd

df = pd.read_csv('YOUR_FILE.csv')

kan = Kanonym('glove-twitter-25') # creating an object from class
dfa, dist = kan.anonymize(df, k=k, n_jobs=-1, plot=True)

Support

Create a Bug Report

If you see an error message or run into an issue, please create a bug report. This effort is valued and helps all users.

Submit a Feature Request

If you have an idea, or you're missing a capability that would make development easier and more robust, please Submit a feature request.

If a similar feature request already exists, don't forget to leave a "+1". If you add some more information such as your thoughts and vision about the feature, your comments will be embraced warmly :)

Contributing

Kanon4txt is an open-source project. We are committed to a fully transparent development process and highly appreciate any contributions. Whether you are helping us fix bugs, proposing new features, improving our documentation, or spreading the word - we would love to have you!