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Introduction

In recent years, with the rapid growth of online social networks and e-commerce platforms, collaborative filtering has become a popular technique for personalized recommendations. However, privacy concerns have emerged as a critical issue in collaborative filtering, as users are often reluctant to disclose their sensitive data to centralized servers. To address this concern, secure multi-party computation (SMPC) has been proposed as a promising solution for privacy-preserving collaborative filtering.

This thesis aims to investigate the application of secure multi-party computation for privacy-preserving collaborative filtering. The study will explore how SMPC can enable multiple parties to jointly compute a recommendation model without revealing their individual preferences to each other. By leveraging cryptographic techniques, SMPC ensures that the privacy of users’ data is protected throughout the recommendation process.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of the 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 Concerns in Collaborative Filtering
2.3 Secure Multi-Party Computation
2.4 Applications of SMPC in Privacy-Preserving Collaborative Filtering
2.5 Comparison with Existing Techniques
2.6 Security and Privacy Guarantees of SMPC
2.7 Challenges and Limitations of SMPC
2.8 Future Research Directions
2.9 Summary of Literature Review

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Implementation of SMPC for Collaborative Filtering
3.5 Performance Evaluation Metrics
3.6 Experimental Setup
3.7 Evaluation Criteria
3.8 Data Analysis Techniques
3.9 Validation of Results

Chapter 4: Discussion of Findings
4.1 Experimental Results
4.2 Performance Evaluation
4.3 Comparison with Existing Methods
4.4 Analysis of Privacy Guarantees
4.5 Interpretation of Results
4.6 Implications for Future Research
4.7 Recommendations for Practitioners
4.8 Insights for Policy Makers

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions of the Study
5.3 Implications for Research and Practice
5.4 Limitations and Future Research Directions
5.5 Conclusion

Thesis Overview

Secure multi-party computation (SMPC) has emerged as a promising solution for privacy-preserving collaborative filtering in the era of online social networks and e-commerce platforms. This thesis aims to investigate the application of SMPC in collaborative filtering to address the privacy concerns of users.

Chapter 1 provides an introduction to the topic, including the background of the study, problem statement, objectives, scope, and significance of the research. The chapter also outlines the structure of the thesis and defines key terms.

Chapter 2 presents a comprehensive literature review on collaborative filtering, privacy concerns, secure multi-party computation, applications of SMPC in collaborative filtering, security and privacy guarantees, challenges, and future research directions.

Chapter 3 describes the research methodology, including research design, data collection, preprocessing, implementation of SMPC, performance evaluation metrics, experimental setup, evaluation criteria, data analysis techniques, and validation of results.

Chapter 4 discusses the findings of the study, including experimental results, performance evaluation, comparison with existing methods, analysis of privacy guarantees, interpretation of results, implications for future research, recommendations for practitioners, and insights for policy makers.

Chapter 5 concludes the thesis by summarizing the findings, highlighting the contributions of the study, discussing implications for research and practice, identifying limitations and future research directions, and providing a conclusion. Overall, this thesis aims to contribute to the advancement of privacy-preserving collaborative filtering using secure multi-party computation.

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