Thesis Abstract

This project aims to design and implement a privacy-preserving data aggregation framework that enables secure and efficient aggregation of sensitive data from multiple sources while preserving the privacy and confidentiality of individual data contributors. The framework will employ cryptographic techniques, differential privacy, and secure multi-party computation to facilitate privacy-preserving data aggregation for various applications, such as healthcare analytics, financial reporting, and statistical analysis.

Thesis Overview