[ad_1]

Thesis Overview:

In recent years, deep learning models have shown remarkable success in various applications such as image recognition, speech recognition, and natural language processing. However, the widespread adoption of these models raises concerns about privacy and data security, especially when sensitive or personal data is involved. Secure multi-party computation (MPC) offers a solution to these concerns by allowing multiple parties to jointly compute a function over their private inputs without revealing any information about these inputs.

This thesis focuses on the application of secure multi-party computation for privacy-preserving deep neural networks. The primary objective of this research is to develop practical and efficient methods for training and evaluating deep learning models while preserving the privacy of the data used in the process. By utilizing MPC techniques, we aim to ensure that sensitive information is not exposed to any single party involved in the computation.

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 Deep Neural Networks
2.2 Privacy in Deep Learning
2.3 Multi-Party Computation
2.4 Privacy-Preserving Machine Learning
2.5 Secure Aggregation Techniques
2.6 Federated Learning
2.7 Homomorphic Encryption
2.8 Differential Privacy
2.9 Secure Neural Network Architectures
2.10 Challenges and Future Directions

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Privacy-Preserving Model Training
3.4 Model Evaluation
3.5 Performance Metrics
3.6 Security Analysis
3.7 Implementation Details
3.8 Benchmarking Methods

Chapter 4: Discussion of Findings
4.1 Privacy-Preserving Model Training Results
4.2 Performance Comparison with Conventional Methods
4.3 Security Evaluation
4.4 Computational Efficiency
4.5 Scalability Analysis
4.6 Robustness to Adversarial Attacks
4.7 Practical Considerations
4.8 Real-World Applications

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

This thesis aims to contribute to the growing research on privacy-preserving deep learning by exploring the potential of secure multi-party computation techniques. By developing efficient and practical methods for training deep neural networks while maintaining data privacy, we hope to address the challenges associated with the growing concerns of data security in the era of artificial intelligence.

[ad_2]

---

Purchase Detail

Download the complete project materials to this project with Abstract, Chapters 1 – 5, References and Appendix (Questionaire, Charts, etc), Click Here to place an order via whatsapp. Got question or enquiry; Click here to chat us up via Whatsapp.
You can also call 08111770269 or +2348059541956 to place an order or use the whatsapp button below to chat us up.
Bank details are stated below.

Bank: UBA
Account No: 1021412898
Account Name: Starnet Innovations Limited

The Blazingprojects Mobile App

Download and install the Blazingprojects Mobile App from Google Play to enjoy over 50,000 project topics and materials from 73 departments, completely offline (no internet needed) with monthly update to topics, click here to install.