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**Chapter 1: Introduction**

1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

**Chapter 2: Literature Review**

2.1 Introduction to Privacy-preserving deep learning
2.2 Secure Multi-Party Computation
2.3 Applications of Privacy-preserving deep learning
2.4 Challenges in Privacy-preserving deep learning
2.5 Existing techniques and methods
2.6 Advantages of using Secure Multi-Party Computation in deep learning
2.7 Disadvantages of using Secure Multi-Party Computation in deep learning
2.8 Comparison of different Privacy-preserving techniques
2.9 Future trends in Privacy-preserving deep learning
2.10 Gaps in existing literature

**Chapter 3: Research Methodology**

3.1 Introduction to Research Methodology
3.2 Research design
3.3 Data collection methods
3.4 Data analysis techniques
3.5 Implementation of Secure Multi-Party Computation
3.6 Evaluation metrics
3.7 Ethical considerations
3.8 Limitations of the methodology

**Chapter 4: Discussion of Findings**

4.1 Introduction to Findings
4.2 Analysis of results
4.3 Comparison with existing literature
4.4 Implications of findings
4.5 Recommendations for future research
4.6 Practical implications
4.7 Challenges encountered
4.8 Suggestions for improvement
4.9 Validation of results
4.10 Contributions to the field

**Chapter 5: Conclusion and Summary**

5.1 Summary of key findings
5.2 Achievements of the study
5.3 Contributions to the field
5.4 Implications for practice
5.5 Recommendations for future research
5.6 Conclusion

**Thesis Overview on Privacy-preserving deep learning using secure multi-party computation**

Privacy-preserving deep learning is a rapidly evolving field that aims to protect the privacy of sensitive data while allowing for the training of deep neural networks. One promising approach in this area is Secure Multi-Party Computation, which enables multiple parties to jointly compute a function while keeping their inputs private. This thesis explores the use of Secure Multi-Party Computation in the context of deep learning and its applications.

The introduction provides an overview of the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance, and the structure of the thesis. The literature review delves into existing research on Privacy-preserving deep learning, Secure Multi-Party Computation, applications, challenges, techniques, advantages, disadvantages, comparisons, trends, and gaps in the literature.

The research methodology chapter outlines the research design, data collection methods, analysis techniques, implementation of Secure Multi-Party Computation, evaluation metrics, ethical considerations, and limitations of the methodology. The discussion of findings chapter presents the analysis of results, comparisons with existing literature, implications, recommendations, practical implications, challenges, suggestions for improvement, and contributions to the field.

In conclusion, this thesis contributes to the growing body of knowledge on Privacy-preserving deep learning using Secure Multi-Party Computation. It provides insights into the advantages, challenges, and future trends in this field, as well as recommendations for future research and practical implications. This research aims to advance the development of privacy-preserving techniques in deep learning and promote the use of Secure Multi-Party Computation for secure collaborative computation in neural networks.

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