https://github.com/sarwanshah/hu_2020_bloom-filter-applications

Exploring the potential of bloom filters in password protection and plagiarism detection schemes
https://github.com/sarwanshah/hu_2020_bloom-filter-applications

Last synced: 8 months ago
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Exploring the potential of bloom filters in password protection and plagiarism detection schemes

• Host: GitHub
• URL: https://github.com/sarwanshah/hu_2020_bloom-filter-applications
• Owner: SarwanShah
• License: gpl-3.0
• Created: 2021-02-12T22:45:57.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2025-02-08T21:33:46.000Z (8 months ago)
• Last Synced: 2025-02-08T22:26:03.056Z (8 months ago)
• Language: Python
• Size: 1.83 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Password and Plagiarism Detection Application Using Bloom Filters  

## **Project Overview**  

This project demonstrates the development of an application that performs both **password breach detection** and **plagiarism detection** using **Bloom Filters** and **hash functions**. It offers efficient, scalable solutions to check large datasets quickly without storing the entire data in memory.  

The application leverages **MurmurHash3**, **SHA1**, and custom **Bloom Filter** implementations, along with **Tkinter** for the graphical interface.

---

## **Features**  

- **Password Verification**:  

  Uses a Bloom Filter to verify if a password has been leaked or compromised.  

- **Plagiarism Detection**:  

  - Detects the percentage of text overlap between a user-provided document and a large corpus of pre-processed text.  

  - Uses a Bloom Filter to optimize large-scale text comparisons efficiently.  

- **Bloom Filter Implementation**:  

  - Supports customizable filter size and error probability.  

  - Efficient hashing using **MurmurHash3** and **SHA1**.  

  - Allows loading and saving filters to/from files.  

- **GUI Application**:  

  - Built with **Tkinter**.  

  - Provides interactive password verification.  

  - Displays the plagiarism detection percentage for documents.  

---

## **File Descriptions**  

- **Application.py**: Implements the main GUI application for password checking. Integrates filter loading and input handling.  

- **BloomFilter.py**: Defines the **Bloom Filter** class with methods for filter creation, loading, and item checking.  

- **filterFileHandler.py**: Provides helper functions to generate and manage Bloom Filters.  

- **Main.py**: Demonstrates both password and plagiarism detection workflows.  

- **MurmurHash3.py**: Implements the **MurmurHash3** hash function optimized for Bloom Filters.  

---

## **How It Works**  

### **Password Detection**  

1. **Data Preparation**: A dataset of hashed passwords is loaded into a Bloom Filter.  

2. **Verification**: The application checks if the input password is in the compromised dataset.  

### **Plagiarism Detection**  

1. **Filter Generation**: Text data (e.g., news articles) is processed to generate a Bloom Filter.  

2. **Document Comparison**: The user provides a document, and each line is checked against the Bloom Filter to determine the percentage of overlap.  

3. **Output**: The application outputs the plagiarism percentage.