AnonAPI

AnonAPI is a lightweight and modular FastAPI-based web service designed to scramble, encrypt, and decrypt text data securely and efficiently. Ideal for developers building anonymization, obfuscation, or text protection tools in applications ranging from testing to privacy-focused deployments.

🕵️‍♂️ AnonAPI - Text Scrambler & Decryption Microservice

AnonAPI is a privacy-focused, FastAPI-powered microservice designed for scrambling and unscrambling text data. It allows developers and testers to anonymize sensitive information quickly and securely via a RESTful API. Perfect for use in data masking, testing pipelines, secure logging, or any situation where text obfuscation is required.

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Go and Check this Out

http://127.0.0.1:8000/docs#/default/scramble_file_api_scramble_upload_post

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🚀 Project Goals

• ✅ Build a lightweight API to scramble and unscramble sensitive text.
• ✅ Enable easy integration into any service using simple HTTP endpoints.
• ✅ Ensure the application is containerized and production-ready using Docker.
• ✅ Provide flexible text processing with features like: - Character-level scrambling - Word-level scrambling - Sentence-level permutation - Reversible unscrambling (optional, using deterministic methods)

💡 Why AnonAPI?

In many real-world applications, especially in testing or demo environments, exposing real user or sensitive data can violate compliance rules (like GDPR, HIPAA). AnonAPI is designed to:

• 🛡 Protect real data by obfuscating it
• 🔁 Reverse scrambling when needed
• ⚙️ Easily deploy and integrate with other tools
• 🐳 Run in isolated environments using Docker

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🧠 Features

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🏗 Folder Structure

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🔠 Supported Scrambling Models

1. Basic Model:

• Description: Returns the input text as-is, with no modification.

• Example:

            Input:  Hello World
            Output: Hello World
  

2. Seeded Model:

• Description: Produces a consistent random shuffling of characters based on a fixed seed value.

• Example:

     Input:     Hello
     Seed:      42
     Output:    loHel  (consistent for seed=42)
  

3. Caesar Model:

• Description: Applies a Caesar cipher by shifting each letter by a fixed number of positions in the alphabet.

• Example:

    Input:     Hello
    Shift:     3
    Output:    Khoor
  

###4. Rot13 Model:

• Description: A special case of Caesar cipher with a fixed shift of 13.

• Example:

    Input:     Hello
    Output:    Uryyb
  

5. Reverse Model:

• Description: Reverses the input string.

• Example:

    Input:     Hello
    Output:    olleH
  

6. Piglatin Model:

• Description: Converts English words into Pig Latin.

• Example:

    Input:     Hello World
    Output:    elloHay orldWay
  

7. Vigenere Model:

• Description: Applies the Vigenère cipher using a keyword.

• Example:

    Input:     Hello
    Keyword:   key
    Output:    Rijvs
  

8. Base64 Model:

• Description: Encodes the input text to Base64 format.

• Example:

    Input:     Hello
    Output:    SGVsbG8=
  

9. Morse Code Model:

• Description: Translates text to Morse code.

• Example:

    Input:     Hello
    Output:    .... . .-.. .-.. ---
  

10. Binary Code Model:

• Description: Converts each character to its binary (ASCII) representation.

• Example:

    Input:     Hi
    Output:    01001000 01101001
  

11. leet Model:

• Description: Converts text to "leet speak" (1337), replacing letters with look-alike numbers or symbols.

• Example:

    Input:     Hello
    Output:    H3ll0
  

12. Synonym Model:

• Description: Replaces words with their synonyms using an NLP library like WordNet.

• Example:

    Input:     Fast runner
    Output:    Quick sprinter
  

13. Emoji Model:

• Description: Translates words or concepts into emojis when possible.

• Example:

    Input:     I love pizza
    Output:    I ❤️ 🍕
  

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⚙️ Features

• ✅ Scramble API

  Accepts plain text and scrambles predefined sensitive entities (names, locations, etc.). Returns scrambled version along with mapping key.

• ✅ Descramble API

  Accepts scrambled text and a mapping key to return the original text. Helpful for reversible anonymization during debugging or controlled testing.

• ✅ NLTK Support

  Uses NLTK models (e.g., named entity recognition) to identify PII.

• ✅ Regex-based Fallback

  For cases where NLTK fails, regex-based matching ensures fallback anonymization.

• ✅ Modular Design

  Code is cleanly separated into modules for app logic, routing, and utility functions.

• ✅ Dockerized Deployment

  Container-ready for easy deployment using:

    docker build -t fastapi-scrambler .
    docker run -d -p 8000:8000 fastapi-scrambler
  

• ✅ Test Files Support

  Includes test_files/ folder with sample inputs for local experiments.

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🛠️ Installation and Setup

• Clone the Repo

    git clone https://github.com/your-username/data-scrambler-api.git
    cd data-scrambler-api
  

• Install Dependencies

    pip install -r requirements.txt
    python nltk_setup.py
  

• Run Locally

    uvicorn app.main:app --reload
    Visit: http://localhost:8000/docs for Swagger UI.
  

• 🐳 Run with Docker

    docker build -t fastapi-scrambler .
    docker run -d -p 8000:8000 fastapi-scrambler
  

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🧪 Sample Files

Use files inside test_files/ for testing the API endpoints with real-world examples.

🔐 Security & Limitations

• Only anonymizes named entities (persons, locations, dates).
• You can extend it to support emails, phone numbers, or custom regex patterns.
• Key mappings are stored in-memory or passed externally — ensure security in real deployments.