Skip to main content

Privacy-first text anonymization tool with enterprise-grade accuracy for removing PII from documents

Project description

🕵️ Anon - Privacy-First Text Anonymizer

A powerful, offline-first text anonymization tool that removes personal identifiable information (PII) from text while keeping all data on your machine. Built with enterprise-grade accuracy using spaCy NER models and Microsoft Presidio.

✨ Features

  • 🔒 100% Offline - All processing happens on your machine
  • 🎯 High Accuracy - Advanced NER using spaCy large models + Presidio
  • 🖥️ Multiple Interfaces - Modern GUI, Web API, and CLI
  • 🚀 Background Processing - CLIs run detached with proper logging
  • 📦 Easy Installation - One-command install with automatic model setup
  • 🏢 Cross-Platform - Windows, macOS, and Linux support

🚀 Quick Start

Installation

pip install simple-anonymizer

The installation will automatically download the required spaCy model (en_core_web_lg) for optimal accuracy.

GUI Application

Launch the modern GUI interface:

anon-gui

The GUI runs in background - you can close the terminal after launch 📝 Logs available at ~/.anonymizer/gui_YYYYMMDD_HHMMSS.log

Web Interface

Start the web server:

anon-web start

Server runs in background - accessible at http://127.0.0.1:8080 📝 Comprehensive logging and process management

Web Server Management

# Start server (custom host/port)
anon-web start --host 0.0.0.0 --port 5000

# Check server status
anon-web status

# View recent logs
anon-web logs

# Stop server
anon-web stop

# Clean old log files
anon-web clean

Python API

from anonymizer_core import redact

# Basic anonymization
result = redact("John Doe works at Microsoft in Seattle.")
print(result.anonymized_text)
# Output: "<REDACTED> works at <REDACTED> in <REDACTED>."

Project details


Download files

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

Source Distribution

simple_anonymizer-0.1.9.tar.gz (325.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

simple_anonymizer-0.1.9-py3-none-any.whl (49.7 kB view details)

Uploaded Python 3

File details

Details for the file simple_anonymizer-0.1.9.tar.gz.

File metadata

  • Download URL: simple_anonymizer-0.1.9.tar.gz
  • Upload date:
  • Size: 325.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.1.3 CPython/3.13.5 Darwin/24.5.0

File hashes

Hashes for simple_anonymizer-0.1.9.tar.gz
Algorithm Hash digest
SHA256 d1fb64ad1da86cbffdcf125cfceca32340c3d7c6d1229c6ebc444cdcd0c958f0
MD5 cbd4ea0bd5fc791c369eb6e82f40d477
BLAKE2b-256 422c7aab07be140a4053ab84f69a09614540e18c97c37ec670d6365e16b6f83a

See more details on using hashes here.

File details

Details for the file simple_anonymizer-0.1.9-py3-none-any.whl.

File metadata

  • Download URL: simple_anonymizer-0.1.9-py3-none-any.whl
  • Upload date:
  • Size: 49.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.1.3 CPython/3.13.5 Darwin/24.5.0

File hashes

Hashes for simple_anonymizer-0.1.9-py3-none-any.whl
Algorithm Hash digest
SHA256 13a8bafc2a8ceb0bb515fa245268096f5177b4bbbeaf0d80dd08e58d6226f476
MD5 6b09e8e98cb7fa9bb8a31df7ee7a90f3
BLAKE2b-256 4ae9491aab2631adcb263698523dcaa344ebc117a04a04df5deb433db433f809

See more details on using hashes here.

Supported by

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