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Introduction
In today’s digital age, privacy concerns have become increasingly prevalent as individuals and organizations seek to protect their sensitive data from unauthorized access. Secure multi-party computation (SMPC) is a cryptographic technique that enables multiple parties to jointly compute a function over their private inputs without revealing any information about their inputs to each other. This technology has gained significant attention in recent years as a means of conducting privacy-preserving analytics, allowing organizations to analyze sensitive data while ensuring the privacy of the individuals involved.
Background of Study
The concept of SMPC was first introduced in the 1980s as a theoretical solution to the problem of securely computing functions over private data. Since then, significant advancements have been made in the field, with researchers developing practical protocols and tools for implementing SMPC in real-world applications.
Problem Statement
Despite the potential benefits of SMPC for privacy-preserving analytics, there are still challenges that need to be addressed. These include the high computational cost of SMPC protocols, the complexity of implementing and integrating SMPC into existing systems, and the lack of awareness and understanding of SMPC among potential users.
Objective of Study
The main objective of this thesis is to explore the use of SMPC for privacy-preserving analytics and to investigate the feasibility and practicality of implementing SMPC in real-world scenarios.
Limitation of Study
This study focuses on the theoretical aspects of SMPC and does not include a detailed analysis of specific SMPC protocols or implementations.
Scope of Study
The scope of this study includes a review of the literature on SMPC and privacy-preserving analytics, an analysis of the challenges and limitations of SMPC, and a discussion of potential solutions and recommendations for future research.
Significance of Study
This study is significant as it contributes to the growing body of research on privacy-preserving analytics and provides insights into the use of SMPC as a tool for protecting sensitive data.
Structure of the Thesis
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 SMPC
2.2 Applications of SMPC
2.3 Privacy-preserving analytics
2.4 Challenges of SMPC
2.5 Recent advances in SMPC
2.6 Comparison of SMPC with other privacy-preserving technologies
2.7 Adoption of SMPC in industry
2.8 Case studies of SMPC implementation
2.9 Ethical considerations in SMPC research
2.10 Future directions in SMPC research
Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection methods
3.3 Data analysis techniques
3.4 Sample selection
3.5 Experimental setup
3.6 Measurement of performance metrics
3.7 Validation of results
3.8 Ethical considerations
Chapter 4: Discussion of Findings
4.1 Analysis of research findings
4.2 Comparison of results with existing literature
4.3 Implications for practice
4.4 Recommendations for future research
4.5 Limitations of the study
4.6 Conclusion
Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Implications for the field
5.3 Contributions to knowledge
5.4 Recommendations for practice
5.5 Conclusion
Thesis Overview on Secure multi-party computation for privacy-preserving analytics
Secure multi-party computation (SMPC) is a cryptographic technology that enables multiple parties to jointly compute a function over their private inputs without revealing any information about their inputs to each other. This thesis explores the use of SMPC for privacy-preserving analytics, with a focus on the theoretical aspects of SMPC protocols and implementations.
The study begins with an introduction to the concept of SMPC, providing background information on the development of SMPC and its potential applications in privacy-preserving analytics. The problem statement highlights the challenges and limitations of SMPC, while the objective of the study is to investigate the feasibility and practicality of implementing SMPC in real-world scenarios.
The literature review chapter provides an overview of existing research on SMPC and privacy-preserving analytics, including recent advances in the field, comparisons with other privacy-preserving technologies, and case studies of SMPC implementation. The research methodology chapter outlines the research design, data collection methods, and analysis techniques used in the study, as well as ethical considerations and sample selection criteria.
The discussion of findings chapter analyzes the research results, compares them with existing literature, and discusses implications for practice and recommendations for future research. The conclusion and summary chapter summarizes the key findings of the study, highlights contributions to knowledge, provides recommendations for practice, and concludes the thesis.
Overall, this thesis aims to contribute to the growing body of research on privacy-preserving analytics and to provide insights into the use of SMPC as a tool for protecting sensitive data in a digital age where privacy concerns are of utmost importance.
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