anonymize-nlp

AnonymizeNLP

Anonymize-NLP is a lightweight and robust package for text anonymization. It uses Natural Language Processing (NLP) and Regular Expressions (Regex) to identify and mask sensitive information in a string.

Features

• Anonymize specific categories in a text, including emails, monetary values, organizations, people, and phone numbers and more.
• Customizable anonymization: Specify which categories to anonymize and which to exclude.
• De-anonymization: Revert anonymized text back to its original form.
• Built-in compatibility with nlp NER - compromise.

Installation

Install Anonymize-NLP and its peer dependencies with npm.

npm i anonymize-nlp

Usage

import { AnonymizeNlp } from 'anonymizenlp';

const anonymizer = new AnonymizeNlp();
const anonymizedText = anonymizer.anonymize(`Hi I'm John Doe, my email is [e-mail] and my phone number is [phone].`);

console.log(anonymizedText);
// Output: "Hi I'm <FIRSTNAME> <LASTNAME>, my email is <EMAIL> and my phone number is <PHONENUMBER>."

const originalText = anonymizer.deAnonymize(anonymizedText);
console.log(originalText);
// Output: "Hi I'm John Doe, my email is [e-mail] and my phone number is [phone]."

API

Create a new AnonymizeNlp instance.

By default, all types are anonymized.

constructor(typesToAnonymize: AnonymizeType[] = anonymizeTypeOptions, typesToExclude: AnonymizeType[] = [])

• typesToAnonymize: Array of AnonymizeType that you want to anonymize in the text.
• typesToExclude: Array of AnonymizeType that you want to exclude from anonymization.

type AnonymizeType =
  | 'date'
  | 'email'
  | 'firstname'
  | 'lastname'
  | 'money'
  | 'organization'
  | 'phonenumber'
  | 'time'
  | 'creditcard'
  | 'domain'
  | 'ip'
  | 'token'
  | 'url'
  | 'id'
  | 'zip_code'
  | 'crypto'
  | 'apikey';

anonymize(input: string): string

Anonymizes the specified categories in the given text.

• input: The text to be anonymized.

deAnonymize(input: string): string

Reverts the anonymized text back to its original form.

• input: The anonymized text.

Contributing

Contributions to this project are welcome! If you would like to contribute, please follow these steps:

1. Fork the repository on GitHub.
2. Clone your fork to your local machine.
3. Create a new branch for your changes.
4. Make your changes and commit them to your branch.
5. Push your changes to your fork on GitHub.
6. Open a pull request from your branch to the main repository.

Please ensure that your code follows the project's coding style and that all tests pass before submitting a pull request. If you find any bugs or have suggestions for improvements, feel free to open an issue on GitHub.

License

This project is licensed under the MIT License. See the LICENSE file for the full license text.

Copyright (c) 2023. All rights reserved.