Design and Implementation of a Privacy-Preserving Recommendation System
Table Of Contents
- 1.Introduction - Background and Rationale - Objectives of the Project
- 2.Literature Review - Recommendation Systems and Privacy Concerns - Privacy-Preserving Technologies - State-of-the-Art Approaches
- 3.System Architecture and Design - User Data Collection and Anonymization - Privacy-Preserving Recommendation Algorithms - Federated Learning Framework
- 4.Implementation Details - Differential Privacy Integration - Federated Learning Model Implementation - User Interface Design
- 5.Privacy and Security Analysis - Privacy Threat Model - Privacy-Preserving Measures - Security Considerations
Thesis Abstract
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.
Thesis Overview
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