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Introduction:

Secure Multi-Party Computation (SMPC) is a cryptographic technique that allows multiple parties to jointly compute a function over their private inputs without revealing any individual input to the other parties. This technology has great potential for privacy-preserving analytics, as it enables organizations to collaborate on data analysis without compromising the confidentiality of their data. In this thesis, we will explore the use of SMPC for privacy-preserving analytics and investigate its feasibility and effectiveness in real-world scenarios.

Table of Contents:

Chapter 1: Introduction
1.1 Background
1.2 Problem Statement
1.3 Objective of Study
1.4 Limitation of Study
1.5 Scope of Study

Chapter 2: Literature Review
2.1 Overview of Secure Multi-Party Computation
2.2 Privacy-Preserving Analytics
2.3 Previous Studies on SMPC for Privacy-Preserving Analytics

Chapter 3: Research Methodology
3.1 Data Collection Methods
3.2 Data Analysis Techniques
3.3 SMPC Implementation

Chapter 4: Discussion of Findings
4.1 Feasibility of SMPC for Privacy-Preserving Analytics
4.2 Effectiveness of SMPC in Real-World Scenarios
4.3 Comparison with Other Privacy-Preserving Techniques

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Future Research Directions

Thesis Overview:

Secure Multi-Party Computation (SMPC) is a cutting-edge technology that allows multiple parties to collaborate on data analysis while preserving the privacy of their sensitive information. In this thesis, we will explore the use of SMPC for privacy-preserving analytics and evaluate its feasibility and effectiveness in real-world scenarios. The research methodology will involve data collection, analysis, and implementation of SMPC techniques. By studying the existing literature on SMPC and privacy-preserving analytics, we aim to make a significant contribution to the field and identify future research directions. Our findings will provide valuable insights into the potential of SMPC for protecting privacy in data analysis tasks.

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