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Introduction:

Collaborative filtering is a widely used technique in recommendation systems, which is designed to predict a user’s preferences for a particular item based on the preferences of other users with similar tastes. However, privacy concerns have become increasingly important in today’s digital age, where users are wary of sharing their personal data. Secure multi-party computation (SMPC) provides a promising solution to this issue by allowing multiple parties to jointly compute a function over their private inputs without revealing any individual data. In this thesis, we will explore the application of SMPC for collaborative filtering, aiming to enhance privacy and security in recommendation systems.

Table of content:

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 Overview of collaborative filtering
2.2 Privacy and security in recommendation systems
2.3 Introduction to secure multi-party computation
2.4 Applications of SMPC in collaborative filtering
2.5 Existing research on SMPC for collaborative filtering
2.6 Challenges and limitations of SMPC in collaborative filtering
2.7 Comparison with other privacy-preserving techniques
2.8 Future directions in SMPC for collaborative filtering
2.9 Summary of literature review

Chapter 3: System Design and Methodology
3.1 System architecture and components
3.2 Data preprocessing and feature selection
3.3 Secure computation protocol selection
3.4 Algorithm design for collaborative filtering
3.5 Privacy and security considerations
3.6 Experimental setup and evaluation metrics
3.7 Performance optimization techniques
3.8 Validation and testing procedures
3.9 Ethical considerations

Chapter 4: System Implementation
4.1 Implementation of secure multi-party computation framework
4.2 Integration with collaborative filtering algorithm
4.3 Data encryption and decryption procedures
4.4 User interface design
4.5 Testing and validation of the system
4.6 Performance evaluation and benchmarking
4.7 Security analysis and threat modeling
4.8 Scalability and extensibility of the system
4.9 Documentation and user guide

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contribution of the study
5.3 Implications for future research
5.4 Conclusion and recommendations
5.5 Limitations of the study
5.6 Final remarks

Thesis Overview:

In this thesis, we will explore the application of secure multi-party computation (SMPC) for collaborative filtering in recommendation systems. Collaborative filtering is a popular technique used to predict user preferences based on the preferences of similar users, but it raises privacy concerns due to the sharing of personal data. SMPC offers a solution by allowing multiple parties to jointly compute a function without revealing individual inputs. The thesis will begin with an introduction to the topic, followed by a literature review on collaborative filtering, privacy in recommendation systems, and SMPC. The system design and methodology chapter will outline the architecture, data preprocessing, protocol selection, and algorithm design. The system implementation chapter will detail the development and testing of the SMPC framework. Finally, the conclusion chapter will summarize the findings, contribute to future research directions, and provide recommendations for implementation and further study. This thesis aims to address the gap in research on privacy-preserving collaborative filtering and contribute to the field of secure data sharing in recommendation systems.

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