Skip to main content

Anonymizes an Excel file and synthesizes new data in its place

Project description

Excel Anonymizer

A Python script that anonymizes an Excel file and synthesizes new data in its place.

Excel_Anonymized_Demo Convert your sheets with sensitive data into anonymized data.

What is Excel Anonymizer

Excel Anonymizer is a python script that helps to ensure sensitive data is properly managed and governed. It provides fast identification and anonymization for private entities in text such as credit card numbers, names, locations, phone numbers, email address, date/time, with more entities to come.

Use case

Data anonymization is crucial because it helps protect privacy and maintain confidentiality. If data is not anonymized, sensitive information such as names, addresses, contact numbers, or other identifiers linked to specific individuals could potentially be learned and misused. Hence, by obscuring or removing this personally identifiable information (PII), data can be used freely without compromising individuals’ privacy rights or breaching data protection laws and regulations.

Overview

Anonymization consists of two steps:

  1. Identification: Identify all data fields that contain personally identifiable information (PII).
  2. Replacement: Replace all PIIs with pseudo values that do not reveal any personal information about the individual but can be used for reference.

Excel Anonymizer uses Microsoft Presidio together with Faker framework for anonymization purposes.

Quickstart

  1. Install the requirements
    pip install presidio_analyzer
    pip install presidio_anonymizer
    python -m spacy download en_core_web_lg
    
  2. Install the package
    pip install excel-anonymizer
    

Note: Spacy will install a Natural Language Processing package on the first run (587.7MB).

  1. Run the demo
    excel-anonymizer ../../personal_information.xlsx
    

That's it!

Usage

To use Excel Anonymizer with your Excel file, simply input the file.

excel-anonymizer your_excel_file_here.xlsx

Author

Siddharth Bhatia
License: MIT License

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

excel_anonymizer-1.1.7.tar.gz (15.4 kB view details)

Uploaded Source

Built Distribution

excel_anonymizer-1.1.7-py3-none-any.whl (5.4 kB view details)

Uploaded Python 3

File details

Details for the file excel_anonymizer-1.1.7.tar.gz.

File metadata

  • Download URL: excel_anonymizer-1.1.7.tar.gz
  • Upload date:
  • Size: 15.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.9.13

File hashes

Hashes for excel_anonymizer-1.1.7.tar.gz
Algorithm Hash digest
SHA256 4f8d702eca7ced4347aaca57e886981628a31f9e519413dd5d4f7dca432cb279
MD5 75bb317762750d73ed9fac8f05022f4b
BLAKE2b-256 d1088f4e856398bc119e42532561e2ade9b56beba1c46babe034beb5da9acd88

See more details on using hashes here.

File details

Details for the file excel_anonymizer-1.1.7-py3-none-any.whl.

File metadata

File hashes

Hashes for excel_anonymizer-1.1.7-py3-none-any.whl
Algorithm Hash digest
SHA256 ad38aa39c72ba7b5014ed334e46098e111cd5095228b44081253a131e427fbd1
MD5 f0f517e9c1545ed26de465ae29626c55
BLAKE2b-256 c86dc09dc207d416d73dbe7230aa2d3dd9dbc29b49750202e182a5ef6acad822

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page