Secure multi-party computation for privacy-preserving data analysis
Table Of Contents
- 1.Introduction
- 1.1Background and motivation
- 1.2Objectives of the project
- 2.Literature Review
- 2.1Overview of secure multi-party computation
- 2.2Privacy-preserving data analysis techniques
- 2.3Applications of MPC in different domains
- 3.Secure Multi-Party Computation Protocols
- 3.1Overview of MPC protocols and algorithms
- 3.2Implementation considerations and security guarantees
- 3.3Integration of MPC with data analysis tasks
- 4.Case Studies
- 4.1Application of MPC to collaborative data analysis
- 4.2Evaluation of privacy preservation and analysis capabilities
- 5.Discussion
- 5.1Interpretation of results and privacy guarantees
- 5.2Limitations and challenges of secure multi-party computation
Thesis Abstract
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.
Thesis Overview
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