Enhancing Privacy Preservation in Machine Learning

This research project aims to address the critical need for privacy-enhancing techniques in machine learning (ML) applications, particularly in scenarios involving sensitive or confidential data. With the widespread adoption of ML algorithms for data analysis and decision-making, preserving the privacy of individuals' data has become a paramount concern.

The project focuses on exploring innovative approaches to enhance privacy in ML models, algorithms, and workflows, with a particular emphasis on preserving confidentiality while maintaining the utility and accuracy of the learned models. Leveraging techniques such as federated learning, differential privacy, and secure multiparty computation, the goal is to enable collaborative ML tasks without compromising the privacy of individual data contributors.