Design and Implementation of a Privacy-Preserving Recommendation System
Table Of Contents
<p>1. Introduction<br> - Background and Rationale<br> - Objectives of the Project<br>2. Literature Review<br> - Recommendation Systems and Privacy Concerns<br> - Privacy-Preserving Technologies<br> - State-of-the-Art Approaches<br>3. System Architecture and Design<br> - User Data Collection and Anonymization<br> - Privacy-Preserving Recommendation Algorithms<br> - Federated Learning Framework<br>4. Implementation Details<br> - Differential Privacy Integration<br> - Federated Learning Model Implementation<br> - User Interface Design<br>5. Privacy and Security Analysis<br> - Privacy Threat Model<br> - Privacy-Preserving Measures<br> - Security Considerations<br></p>
Thesis Abstract
<p> This project aims to design and implement a recommendation system that provides personalized recommendations while preserving user privacy. The system will leverage privacy-enhancing technologies such as differential privacy and federated learning to ensure that user data remains confidential and secure. By incorporating privacy-preserving mechanisms into the recommendation algorithm, the system will deliver accurate and relevant recommendations without compromising user privacy. <br></p>Thesis Overview
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