Title: ADVERSARY-AWARE PARTIAL LABEL LEARNING WITH LABEL DISTILLATION. Abstract: To ensure that the data collected from human subjects is entrusted with a secret, rival labels are introduced to conceal the information provided by the participants on purpose. The corresponding learning task can be formulated as a noisy partial-label learning problem. However, conventional partial-label learning (PLL) methods are still vulnerable to the high ratio of noisy partial labels, especially in a large labelling space. To learn a more robust model, we present Adversary-Aware Partial Label Learning and introduce the $\textit{rival}$, a set of noisy labels, to the collection of candidate labels for each instance. By introducing the rival label, the predictive distribution of PLL is factorised such that a reasonably good predictive label is achieved with less uncertainty coming from the transition matrix, assuming its generation process is known. Nonetheless, the predictive accuracy is still insufficient to produce an adequately good set of positive samples to minimise the loss function. Moreover, the inclusion of rivals also brings an inconsistency issue for the classifier and risk function due to the intractability of the transition matrix. Consequently, the immature teacher within momentum (ITWM) disambiguation algorithm is proposed to cope with the situation. We utilise the confidence score mapping from the instance space to approximate the intractable term, allowing us to obtain a provably consistent classifier and risk function. Extensive experiments demonstrate that our method achieves promising results on the CIFAR10, CIFAR100 and CUB200 datasets.