🔐 Fortifying Data Privacy with k-Anonymity, l-Diversity & t-Closeness

A privacy-preserving desktop tool implementing k-Anonymity, l-Diversity, and t-Closeness models to anonymize sensitive data without compromising its analytical value.

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📖 Project Overview

This project offers a Windows Forms application built with C# that supports practical anonymization of datasets through three industry-standard privacy models: k-Anonymity, l-Diversity, and t-Closeness. It enables organizations, researchers, and students to protect individual privacy in tabular data by minimizing the risk of re-identification while maintaining data utility for analysis.

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🔐 Why Data Privacy & Anonymization Matter

Protecting personal data has become crucial in today’s digital world, where vast amounts of sensitive information are collected, shared, and analyzed daily. Effective anonymization ensures that datasets can be used for research, analytics, or public release without exposing individual identities or sensitive attributes—reducing risks like identity theft, discrimination, or reputational harm

🛡️ Key Benefits

• Regulatory Compliance
  Helps meet legal obligations under GDPR, HIPAA, CCPA, and other data protection laws by minimizing exposure of Personally Identifiable Information (PII).

• Maintains Data Utility
  Models like k-anonymity, l-diversity, and t-closeness strike a balance between privacy protection and analytical value—essential for scientific discovery and real-world decision-making.

• Trust & Ethics
  Builds public trust by ensuring ethical handling of sensitive data—especially in healthcare, finance, and social science research.

• Mitigates Re-identification Risks
  Simple removal of names or IDs is not enough. Combining quasi-identifiers like ZIP code, birthdate, and gender can still re-identify up to 87% of individuals Strong anonymization techniques are essential.

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Anonymization Techniques in This Project

• k-Anonymity
  Ensures each record is indistinguishable from at least k–1 others based on quasi-identifiers.

• l-Diversity
  Protects against attribute disclosure by requiring that each anonymized group contains at least l distinct values for sensitive attributes.

• t-Closeness
  Maintains the distribution of sensitive attributes in each group close to the overall dataset, preventing inference attacks.

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Application Features

• Intuitive C# Windows Forms interface
• Load CSV/tabular datasets
• Define:
  • Explicit Identifiers (EIDs)
  • Quasi-Identifiers (QIDs)
  • Sensitive Attributes
• Apply privacy models independently or in combination
• Built-in correlation analysis
• Export anonymized dataset

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🛠️ Getting Started

📌 Requirements

• Windows OS
• .NET Framework 4.7.2+
• Visual Studio

Installation

git clone https://github.com/Maha028/Fortifying_DataPrivacy_with_k-Anonymity_l-Diversity_t-closeness_Models.git

1. Open the solution file DataArmor.sln in Visual Studio.
2. Build the solution (Ctrl+Shift+B)
3. Run the application (F5) or launch DataArmor.exe from the output folder

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📂 Project Structure

Path / File	Description
DataArmor.sln	Visual Studio solution file
/Form	GUI components and user interaction logic
/Anonymization	Core privacy model implementations
/DataAnalysis	Utility functions for data profiling/correlation
/Resources	Static icons/images used in UI
bin/Release/DataArmor.exe	Compiled Windows application