Privacy-Preserving Machine Learning Techniques
Table Of Contents
1. Introduction
1.1 Background and Motivation
1.2 Objectives and Scope
2. Literature Review
2.1 Privacy-Preserving Machine Learning Techniques
2.2 Trade-offs Between Privacy and Model Accuracy
3. Federated Learning
3.1 Distributed Model Training and Aggregation
3.2 Privacy and Security Considerations
4. Differential Privacy
4.1 Privacy Guarantees and Noise Addition
4.2 Privacy Budget and Utility Trade-offs
5. Secure Multi-Party Computation
5.1 Secure Protocols for Collaborative Model Training
5.2 Privacy-Preserving Data Analysis
Thesis Abstract
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.
Thesis Overview
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