Dr. Hafiz Imtiaz

Several matrix factorization algorithms are employed in machine learning applications. Among these, Non-negative Matrix Factorization (NMF) gained attention due to the ability to extract meaningful features from inherently non-negative data, such as documents, images or videos. In this work we propose a novel method and demonstrate our results in such a way that the clients/data holders have the control to select the degree of privacy guarantee based on the required utility. We show the effectiveness of our proposed algorithm on six real datasets. Our experimental results show that our proposed method easily outperforms conventional privacy-preserving scheme, while achieving close approximation of the non-privacy-preserving approach under some parameter choices. This work led to this Springer Signal, Image and Video Processing publication.