Design and Implementation of a Privacy-Preserving Data Aggregation Framework
Table Of Contents
<p>1. Introduction<br> - Background and Motivation<br> - Problem Statement<br> - Objectives<br>2. Literature Review<br> - Privacy-Preserving Data Analysis Techniques<br> - Secure Multi-Party Computation Protocols<br> - Cryptographic Primitives for Data Aggregation<br>3. System Architecture and Design<br> - Data Encryption and Secure Storage<br> - Secure Data Aggregation Protocols<br> - Integration with Data Analysis Tools<br>4. Implementation Details<br> - Cryptographic Libraries and Tools<br> - Secure Communication Protocols<br> - Data Aggregation Algorithms<br>5. Evaluation and Testing<br> - Performance Metrics<br> - Security Analysis<br> - Use Case Scenarios<br></p>
Thesis Abstract
<p> The project aims to develop a privacy-preserving data aggregation framework that allows multiple parties to collaboratively analyze sensitive data without compromising individual privacy. The framework will utilize advanced cryptographic techniques and secure multi-party computation protocols to aggregate and analyze data while ensuring that the raw data remains encrypted and private. The system will be designed to support various data analysis tasks, such as statistical computations, machine learning algorithms, and data mining, while preserving the confidentiality and integrity of the underlying data. <br></p>Thesis Overview
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