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Introduction:

In the digital age, the amount of data being generated and shared on social networks is growing exponentially. This data contains valuable insights that can be used for social network analysis to understand human behavior, relationships, and interactions. However, the sharing of personal information on social networks raises serious privacy concerns for users. It is crucial to develop a framework that allows for the publication of data for analysis while preserving the privacy of individuals. This thesis aims to design a privacy-preserving data publishing framework for social network analysis to address this critical issue.

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 Two: Literature Review
2.1 Introduction to Social Network Analysis
2.2 Privacy Preservation Techniques
2.3 Data Publishing Frameworks
2.4 Anonymization and De-anonymization
2.5 Differential Privacy
2.6 Homomorphic Encryption
2.7 Privacy-Preserving Algorithms
2.8 Secure Multiparty Computation
2.9 Privacy Regulations and Compliance
2.10 Ethical Considerations in Data Publishing

Chapter Three: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Analysis Techniques
3.4 Privacy-Preserving Models
3.5 Evaluation Metrics
3.6 Experimental Setup
3.7 Data Security Measures
3.8 Case Study Implementation

Chapter Four: Discussion of Findings
4.1 Privacy-Preserving Data Publishing Framework
4.2 Evaluation of Privacy Preservation Techniques
4.3 Impact on Social Network Analysis
4.4 Comparison with Existing Frameworks
4.5 User Acceptance and Usability
4.6 Performance Evaluation
4.7 Security Vulnerabilities
4.8 Future Research Directions

Chapter Five: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Implications for Practice
5.4 Recommendations for Future Research
5.5 Conclusion

Thesis Overview:

The exponential growth of data being generated on social networks has raised serious privacy concerns for users. This thesis aims to design a privacy-preserving data publishing framework for social network analysis to address these concerns. The research will include a comprehensive literature review on social network analysis, privacy preservation techniques, data publishing frameworks, and ethical considerations. The methodology will involve research design, data collection, analysis techniques, privacy-preserving models, evaluation metrics, experimental setup, data security measures, and a case study implementation.

The findings of the study will include the development and evaluation of a privacy-preserving data publishing framework, impact on social network analysis, comparison with existing frameworks, user acceptance and usability, performance evaluation, security vulnerabilities, and future research directions. The thesis will conclude with a summary of findings, contributions to the field, implications for practice, recommendations for future research, and a conclusion. Overall, the thesis will provide valuable insights into designing a privacy-preserving data publishing framework for social network analysis.

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