Design and implementation of a privacy-preserving data classification framework
Table Of Contents
<p>1. Introduction<br> - Background and significance of privacy-preserving data classification<br> - Objectives and scope of the project<br>2. Literature Review<br> - Overview of privacy-preserving data analysis techniques<br> - Review of data classification methods in the context of privacy<br>3. Framework Design<br> - Requirements and design considerations<br> - Selection of privacy-preserving techniques<br>4. Prototype Implementation<br> - Development of the privacy-preserving data classification framework<br> - Integration of privacy-enhancing technologies<br>5. Evaluation Methodology<br> - Metrics for assessing the effectiveness of the framework<br> - Experimental setup and data sources<br></p>
Thesis Abstract
<p> This project focuses on the design and implementation of a privacy-preserving data classification framework. The framework aims to enable organizations to classify sensitive data while preserving the privacy of individuals. It will incorporate techniques such as differential privacy, secure multi-party computation, and homomorphic encryption to ensure that the classification process does not compromise the confidentiality of personal information. The project will involve the development of a prototype framework and the evaluation of its effectiveness in real-world data classification scenarios. <br></p>Thesis Overview
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