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Introduction

Secure multi-party computation (MPC) is a cryptographic technique that allows multiple parties to jointly compute a function over their private inputs without revealing any information other than the output. This technology has gained significance in recent years due to the increasing need for privacy-preserving computation in various applications, including linear regression analysis.

Despite the importance of secure linear regression in fields such as healthcare, finance, and social science, there are challenges in implementing MPC for this specific task. This thesis aims to explore the potential of MPC for secure linear regression and address the limitations and challenges associated with this approach.

Table of Contents

Chapter 1: Introduction
1.1 Introduction
1.2 Background of the Study
1.3 Problem Statement
1.4 Objective of the Study
1.5 Limitation of the Study
1.6 Scope of the Study
1.7 Significance of the Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of Secure Multi-Party Computation
2.2 Applications of MPC in Linear Regression
2.3 Privacy-Preserving Techniques in MPC
2.4 Challenges in Implementing MPC for Linear Regression
2.5 Existing Solutions and Approaches
2.6 Security and Efficiency Considerations
2.7 Comparison of MPC with other Privacy-Preserving Techniques
2.8 Recent Developments in MPC for Linear Regression
2.9 Gaps and Future Directions in the Literature
2.10 Conclusion

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection and Preparation
3.3 MPC Protocol Selection
3.4 Implementation Considerations
3.5 Evaluation Metrics
3.6 Ethical Considerations
3.7 Risk Management
3.8 Data Analysis Techniques
3.9 Validation Methods

Chapter 4: Discussion of Findings
4.1 Overview of the Dataset
4.2 Performance Evaluation of MPC Protocol
4.3 Comparison with Traditional Linear Regression Methods
4.4 Privacy and Security Analysis
4.5 Scalability and Efficiency Considerations
4.6 Interpretation of Results
4.7 Implications for Practice
4.8 Recommendations for Future Research

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Practical Implications
5.4 Limitations of the Study
5.5 Recommendations for Practitioners
5.6 Recommendations for Future Research
5.7 Conclusion

Thesis Overview

Secure multi-party computation (MPC) has emerged as a powerful tool for conducting privacy-preserving computations in various applications. In this thesis, we focus on the application of MPC for secure linear regression, a fundamental statistical technique used in data analysis. The thesis aims to explore the potential of MPC for secure linear regression, address the challenges and limitations, and provide insights for future research in this area.

Chapter 1 provides an introduction to the study, including the background, problem statement, objectives, limitations, scope, significance, and structure of the thesis. The chapter also defines key terms related to secure multi-party computation and linear regression.

Chapter 2 presents a comprehensive literature review on secure multi-party computation, its applications in linear regression, privacy-preserving techniques, challenges, existing solutions, security considerations, and recent developments. The chapter highlights gaps in the literature and suggests future research directions.

Chapter 3 outlines the research methodology, including research design, data collection and preparation, selection of MPC protocol, implementation considerations, evaluation metrics, ethical considerations, risk management, data analysis techniques, and validation methods.

Chapter 4 discusses the findings of the study, including the overview of the dataset, performance evaluation of the MPC protocol, comparison with traditional linear regression methods, privacy and security analysis, scalability and efficiency considerations, interpretation of results, implications for practice, and recommendations for future research.

Chapter 5 concludes the thesis by summarizing the findings, discussing the contributions to the field, highlighting practical implications, outlining limitations of the study, providing recommendations for practitioners and future research, and concluding the study on secure multi-party computation for secure linear regression.

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