3 accepted papers
Differentially-private (DP) Generative Adversarial Networks (GAN) can be used to protect the privacy of training data and support public downstream learning tasks with synthetic data. However, typical DP mechanisms add noise to the training process and can lead to various convergence problems. We pr…
Despite recent promising results on semi-supervised learning (SSL), data imbalance, particularly in the unlabeled dataset, could significantly impact the training performance of a SSL algorithm if there is a mismatch between the expected and actual class distributions. The efforts on how to construc…
Secure multi-party computation (MPC) has been established as the de facto paradigm for protecting privacy in distributed computation. Information-theoretic secure MPC protocols, though more efficient than their computationally secure counterparts, require at least three computational parties and are…