Design and implementation of a privacy-preserving data classification framework
Table Of Contents
- 1.Introduction - Background and significance of privacy-preserving data classification - Objectives and scope of the project
- 2.Literature Review - Overview of privacy-preserving data analysis techniques - Review of data classification methods in the context of privacy
- 3.Framework Design - Requirements and design considerations - Selection of privacy-preserving techniques
- 4.Prototype Implementation - Development of the privacy-preserving data classification framework - Integration of privacy-enhancing technologies
- 5.Evaluation Methodology - Metrics for assessing the effectiveness of the framework - Experimental setup and data sources
Thesis Abstract
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.
Thesis Overview
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