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Introduction

In recent years, machine learning has revolutionized various industries by enabling computers to learn from data and make decisions without explicit programming. However, the use of machine learning in a multi-party setting where data is distributed among multiple parties poses significant challenges in terms of data privacy and security. Homomorphic encryption has emerged as a promising solution to address these challenges by allowing computation on encrypted data without the need to decrypt it. This thesis focuses on exploring the use of homomorphic encryption for secure multi-party machine learning.

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 Overview of Homomorphic Encryption

2.2 Multi-Party Computation in Machine Learning

2.3 Privacy-Preserving Machine Learning Techniques

2.4 Applications of Homomorphic Encryption in Multi-Party Machine Learning

2.5 Challenges and Limitations of Homomorphic Encryption

2.6 Existing research on Homomorphic Encryption for Multi-Party Machine Learning

2.7 Comparison of Homomorphic Encryption with other Privacy-Preserving Techniques

2.8 Security Analysis of Homomorphic Encryption in Multi-Party Machine Learning

2.9 Future Directions in Homomorphic Encryption for Secure Multi-Party Machine Learning

2.10 Conclusion

Chapter 3: System Design and Methodology

3.1 Data Preprocessing and Feature Selection

3.2 Homomorphic Encryption Scheme Selection

3.3 Data Encryption and Distribution among Parties

3.4 Computation on Encrypted Data

3.5 Model Training and Updating

3.6 Evaluation Metrics for Secure Multi-Party Machine Learning

3.7 Performance Analysis of the Proposed System

3.8 Comparison with Existing Systems

Chapter 4: System Implementation

4.1 Implementation of Homomorphic Encryption Library

4.2 Integration with Machine Learning Algorithms

4.3 Testing and Validation of the System

4.4 Performance Optimization Techniques

4.5 Security Measures and Threat Mitigation Strategies

4.6 Scalability of the System

4.7 User Interface Design

4.8 Deployment and Maintenance Considerations

Chapter 5: Conclusion

5.1 Summary of Findings

5.2 Contributions of the Study

5.3 Implications for Practice

5.4 Limitations and Future Research Directions

5.5 Conclusion

Thesis Overview:

Homomorphic encryption has emerged as a powerful tool for secure multi-party machine learning, allowing parties to collaborate on building machine learning models without compromising the privacy of their data. This thesis explores the use of homomorphic encryption for secure multi-party machine learning, discussing the background of the study, problem statement, objectives, limitations, scope, significance, and structure of the thesis.

The literature review provides a comprehensive overview of homomorphic encryption, multi-party computation in machine learning, privacy-preserving techniques, and applications of homomorphic encryption in secure multi-party machine learning. It also discusses existing research, challenges, and future directions in the field.

The system design and methodology chapter outline the data preprocessing, homomorphic encryption scheme selection, data encryption, computation on encrypted data, model training, evaluation metrics, and performance analysis of the proposed system. The system implementation chapter details the implementation of the homomorphic encryption library, integration with machine learning algorithms, testing, security measures, scalability, and deployment considerations.

The conclusion chapter summarizes the findings, contributions, implications for practice, limitations, and future research directions of the study. Overall, this thesis aims to contribute to the growing body of knowledge on homomorphic encryption for secure multi-party machine learning, providing insights and recommendations for researchers and practitioners in the field.

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