Dr. Hafiz Imtiaz

Developing differentially private machine learning algorithms typically involve adding randomness into the algorithm pipeline, which evidently degrades the performance of the algorithm—giving raise to privacy-utility trade-off. Existing differentially private matrix factorization algorithms offer poor privacy-utility trade-off for use in practical systems. Motivated by this, we propose two differentially private matrix factorization algorithms for application in recommendation systems, which provide better privacy-utility trade-off compared to the existing approaches. Our first algorithm adopts the framework of noisy gradient descent using the Gaussian Mechanism, whereas our second algorithm extends the Functional Mechanism framework to incorporate it into matrix factorization. In both cases, we perform theoretical analysis of the privacy of the algorithms. This work led to this Springer International Journal of Machine Learning and Cybernetics publication.