Privacy-Preserving Machine Learning Techniques
Table Of Contents
<p>1. Introduction<br> 1.1 Background and Motivation<br> 1.2 Objectives and Scope<br>2. Literature Review<br> 2.1 Privacy-Preserving Machine Learning Techniques<br> 2.2 Trade-offs Between Privacy and Model Accuracy<br>3. Federated Learning<br> 3.1 Distributed Model Training and Aggregation<br> 3.2 Privacy and Security Considerations<br>4. Differential Privacy<br> 4.1 Privacy Guarantees and Noise Addition<br> 4.2 Privacy Budget and Utility Trade-offs<br>5. Secure Multi-Party Computation<br> 5.1 Secure Protocols for Collaborative Model Training<br> 5.2 Privacy-Preserving Data Analysis<br></p>
Project Abstract
<p> Privacy-preserving machine learning techniques aim to enable the training and deployment of machine learning models while protecting sensitive data. This project will investigate various privacy-preserving methods, including federated learning, differential privacy, and secure multi-party computation. The project will focus on evaluating the trade-offs between privacy and model accuracy, as well as the practical considerations for implementing privacy-preserving machine learning in real-world applications. <br></p>Project Overview
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