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Introduction

Privacy-preserving recommender systems have gained significant attention in recent years due to the growing concern over privacy violations in the online world. As more and more users rely on recommender systems to make decisions about what to buy, watch, or read, the need to protect their sensitive information has become paramount. Recommender systems are algorithms that predict a user’s preference for a certain item based on their past behavior or the behavior of a group of users with similar tastes. However, in order to make accurate recommendations, these systems often require access to a user’s personal data, raising concerns about privacy infringement.

This thesis aims to explore the challenges and opportunities in developing privacy-preserving recommender systems that can provide accurate recommendations while protecting the privacy of users. By implementing various privacy-preserving techniques such as anonymization, encryption, and differential privacy, this research seeks to strike a balance between recommendation accuracy and user privacy.

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 recommender systems
2.2 Privacy concerns in recommender systems
2.3 Privacy-preserving techniques in recommender systems
2.4 Anonymization methods
2.5 Encryption techniques
2.6 Differential privacy
2.7 Collaborative filtering
2.8 Content-based filtering
2.9 Hybrid recommendation approaches
2.10 Evaluation metrics for recommender systems

Chapter 3: System Design and Methodology
3.1 System architecture
3.2 Data collection and preprocessing
3.3 Privacy-preserving techniques implementation
3.4 Recommendation algorithm selection
3.5 Performance evaluation methodology
3.6 User study design
3.7 Ethical considerations
3.8 Data analysis techniques

Chapter 4: System Implementation
4.1 Data acquisition
4.2 Data preprocessing
4.3 Anonymization process
4.4 Encryption implementation
4.5 Differential privacy integration
4.6 Collaborative filtering algorithm implementation
4.7 Content-based filtering algorithm implementation
4.8 Hybrid recommendation system development

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions to the field
5.3 Implications for future research
5.4 Limitations of the study
5.5 Conclusion

Thesis Overview:

Privacy-preserving recommender systems have become an essential area of research due to the increasing concern about privacy violations in the online world. This thesis aims to address the challenges and opportunities in developing recommender systems that protect user privacy while providing accurate recommendations. The literature review will provide an overview of existing recommender systems, privacy concerns, and privacy-preserving techniques. The system design and methodology chapter will outline the architecture, data collection, privacy-preserving techniques implementation, and evaluation methodology. The system implementation chapter will detail the data acquisition, preprocessing, encryption, differential privacy, and recommendation algorithm implementation. Finally, the conclusion and summary chapter will summarize the findings, discuss contributions to the field, suggest future research directions, and acknowledge study limitations.

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