  Collaborative learning has gained great popularity due to its benefit of data
privacy protection: participants can jointly train a Deep Learning model
without sharing their training sets. However, recent works discovered that an
adversary can fully recover the sensitive training samples from the shared
gradients. Such reconstruction attacks pose severe threats to collaborative
learning. Hence, effective mitigation solutions are urgently desired.
  In this paper, we propose to leverage data augmentation to defeat
reconstruction attacks: by preprocessing sensitive images with
carefully-selected transformation policies, it becomes infeasible for the
adversary to extract any useful information from the corresponding gradients.
We design a novel search method to automatically discover qualified policies.
We adopt two new metrics to quantify the impacts of transformations on data
privacy and model usability, which can significantly accelerate the search
speed. Comprehensive evaluations demonstrate that the policies discovered by
our method can defeat existing reconstruction attacks in collaborative
learning, with high efficiency and negligible impact on the model performance.
