Development of a recommendation system for personalized music streaming
Table Of Contents
<p>1. Introduction<br> - Background and significance of privacy-preserving data aggregation<br> - Objectives and scope of the project<br>2. Literature Review<br> - Overview of privacy-preserving data aggregation techniques<br> - Existing frameworks and their limitations<br>3. System Architecture and Design<br> - Selection of cryptographic primitives<br> - Framework architecture and components<br>4. Implementation and Integration<br> - Development of the privacy-preserving data aggregation framework<br> - Integration with data sources and analysis tools<br>5. Security and Privacy Analysis<br> - Threat modeling and risk assessment<br> - Privacy guarantees and security measures<br></p>
Thesis Abstract
<p> The project aims to develop a recommendation system for personalized music streaming services. The system will utilize machine learning algorithms to analyze user preferences and behavior, and provide tailored music recommendations to enhance user experience. The project will focus on creating an efficient and accurate recommendation engine that can adapt to users' evolving tastes and preferences. <br></p>Thesis Overview
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