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Design and implementation of a privacy-preserving data mining framework

 

Table Of Contents


<p>1. Introduction<br>&nbsp; - Background and motivation<br>&nbsp; - Objectives of the project<br>2. Literature Review<br>&nbsp; - Overview of privacy-preserving data mining techniques<br>&nbsp; - Existing frameworks and their limitations<br>3. System Architecture<br>&nbsp; - Design of the privacy-preserving data mining framework<br>&nbsp; - Components and modules<br>4. Cryptographic Protocols<br>&nbsp; - Secure multi-party computation<br>&nbsp; - Homomorphic encryption<br>&nbsp; - Differential privacy<br>5. Implementation<br>&nbsp; - Selection of programming languages and tools<br>&nbsp; - Integration of cryptographic protocols<br></p>

Thesis Abstract

<p> The project aims to develop a privacy-preserving data mining framework that allows organizations to extract valuable insights from sensitive data without compromising individual privacy. The framework will utilize advanced cryptographic techniques and secure multi-party computation protocols to enable collaborative data analysis while ensuring that the raw data remains encrypted and private. The project will address the growing need for privacy-preserving data mining solutions in various domains, including healthcare, finance, and social media. <br></p>

Thesis Overview

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