Design and implementation of a privacy-preserving data aggregation and analysis platform

Thesis Abstract

This project aims to design and implement a privacy-preserving data aggregation and analysis platform to enable organizations to derive insights from sensitive data while preserving individual privacy. The platform will employ secure aggregation techniques and differential privacy mechanisms to ensure that aggregated data does not reveal sensitive information about individual contributors. By providing a secure environment for data analysis and reporting, the platform will enable organizations to harness the value of their data assets without compromising privacy or regulatory compliance. The project seeks to address the growing demand for privacy-preserving analytics solutions, offering a robust platform for organizations to unlock the potential of their data while upholding privacy principles.