Design and implementation of a privacy-preserving data aggregation framework
Table Of Contents
<p>1. Introduction<br> - Background and significance of personalized travel recommendations<br> - Objectives and scope of the project<br>2. Literature Review<br> - Overview of personalized recommendation systems in the travel industry<br> - Existing approaches and their limitations<br>3. Data Collection and Feature Engineering<br> - Acquisition of travel and user preference data<br> - Feature selection and engineering<br>4. Model Optimization Techniques<br> - Selection of machine learning algorithms<br> - Hyperparameter tuning and model optimization<br>5. Implementation and Integration<br> - Development and integration of optimized models<br> - Testing and validation of the recommendation system<br></p>
Thesis Abstract
<p> This project focuses on designing and implementing a privacy-preserving data aggregation framework for sensitive data. The framework will employ cryptographic techniques and secure protocols to aggregate and analyze data while preserving the privacy and confidentiality of individual data contributors. The project aims to address the growing need for privacy-preserving data aggregation in various domains, such as healthcare, finance, and social research. <br></p>Thesis Overview
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