Towards Generalizable Data Protection With Transferable Unlearnable
  Examples
Bin Fang Bo Li Shuang Wu Tianyi Zheng Shouhong Ding Ran Yi Lizhuang Ma
Artificial Intelligence (AI) is making a profound impact in almost every
domain. One of the crucial factors contributing to this success has been the
access to an abundance of high-quality data for constructing machine learning
models. Lately, as the role of data in artificial intelligence has been
significantly magnified, concerns have arisen regarding the secure utilization
of data, particularly in the context of unauthorized data usage. To mitigate
data exploitation, data unlearning have been introduced to render data
unexploitable. However, current unlearnable examples lack the generalization
required for wide applicability. In this paper, we present a novel,
generalizable data protection method by generating transferable unlearnable
examples. To the best of our knowledge, this is the first solution that
examines data privacy from the perspective of data distribution. Through
extensive experimentation, we substantiate the enhanced generalizable
protection capabilities of our proposed method.