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Introduction

The increasing demand for data privacy and security in collaborative learning environments has led to the development of secure multi-party computation (MPC) techniques. These techniques allow multiple parties to jointly compute a function over their private inputs without revealing any individual data. In the context of collaborative learning, this can enable participants to learn from each other’s data while preserving the privacy of sensitive information. This thesis explores the application of secure MPC in collaborative learning settings, aiming to provide a comprehensive understanding of the challenges, opportunities, and implications of this approach.

1.2 Background of Study

This chapter provides an overview of the existing literature on collaborative learning, data privacy, and secure multi-party computation. It discusses the motivations behind the use of MPC in collaborative learning environments and highlights the key contributions of previous research in this area.

1.3 Problem Statement

Collaborative learning environments often require participants to share sensitive data, such as personal information or proprietary algorithms. Protecting the privacy and confidentiality of this data is a critical challenge, especially in the age of increasing data breaches and privacy concerns. Secure MPC offers a promising solution to this problem, but there are still gaps in our understanding of how to effectively implement and deploy these techniques in real-world settings.

1.4 Objective of Study

The primary objective of this thesis is to investigate the feasibility and effectiveness of using secure MPC for collaborative learning. This includes assessing the security and privacy guarantees of MPC protocols, evaluating the performance implications of using MPC in collaborative learning scenarios, and identifying potential challenges and limitations of this approach.

1.5 Limitation of study

This study is limited to examining the feasibility of using secure MPC for collaborative learning and does not consider other privacy-preserving techniques or models. Additionally, the evaluation of MPC protocols may be limited by computational resources and constraints.

1.6 Scope of Study

The scope of this study includes a comprehensive literature review of collaborative learning, data privacy, and secure MPC techniques. The research will also involve the design, implementation, and evaluation of an MPC-based collaborative learning system. The study will focus on assessing the security, privacy, and performance implications of using MPC in a real-world collaborative learning environment.

1.7 Significance of Study

This research has the potential to contribute to the advancement of secure collaborative learning environments by providing insights into the feasibility and effectiveness of using MPC techniques. The findings of this study may have implications for educators, researchers, and practitioners in the field of collaborative learning and data privacy.

1.8 Structure of the Thesis

Chapter 2 provides a comprehensive literature review of collaborative learning, data privacy, and secure MPC techniques. Chapter 3 presents the system design and methodology for implementing secure MPC in a collaborative learning environment. Chapter 4 details the implementation of the MPC-based system and evaluates its performance. Chapter 5 concludes the thesis with a summary of findings and recommendations for future research.

1.9 Definition of Terms

– Secure Multi-Party Computation (MPC): A cryptographic protocol that allows multiple parties to jointly compute a function over their private inputs without revealing any individual data.
– Collaborative Learning: A learning approach that involves multiple participants working together towards a common learning goal, often involving the sharing of knowledge and resources.
– Data Privacy: The protection of sensitive information from unauthorized access, use, or disclosure.

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