Differentially private co-clustering: review and improved algorithms

This thesis presents advancements in the field of differential private co-clustering algorithms. Our research builds upon the foundation laid by Dr. Battaglia, focusing on improving her results. Through experimentation and analysis, the proposed research aims to elevate the algorithm's efficacy in accurately co-clustering sensitive datasets, thus contributing to the progression of privacy-preserving data analysis methodologies.