Secure multi-party computation for privacy-preserving data analysis
Table Of Contents
<p>1. Introduction<br> 1.1 Background and motivation<br> 1.2 Objectives of the project<br>2. Literature Review<br> 2.1 Overview of secure multi-party computation<br> 2.2 Privacy-preserving data analysis techniques<br> 2.3 Applications of MPC in different domains<br>3. Secure Multi-Party Computation Protocols<br> 3.1 Overview of MPC protocols and algorithms<br> 3.2 Implementation considerations and security guarantees<br> 3.3 Integration of MPC with data analysis tasks<br>4. Case Studies<br> 4.1 Application of MPC to collaborative data analysis<br> 4.2 Evaluation of privacy preservation and analysis capabilities<br>5. Discussion<br> 5.1 Interpretation of results and privacy guarantees<br> 5.2 Limitations and challenges of secure multi-party computation<br></p>
Thesis Abstract
<p> This project aims to explore the use of secure multi-party computation (MPC) techniques for privacy-preserving data analysis. MPC allows multiple parties to jointly compute a function over their inputs while keeping those inputs private. The project will involve implementing MPC protocols, applying them to collaborative data analysis tasks, and evaluating their effectiveness in preserving privacy while allowing meaningful analysis of sensitive data. <br></p>Thesis Overview
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