Towards Generalizable Data Protection With Transferable Unlearnable Examples
This addresses data privacy concerns in AI by improving the generalization of unlearnable examples, though it appears incremental as it builds on existing unlearnable example methods.
The paper tackles the problem of protecting data from unauthorized usage by introducing a generalizable method for generating transferable unlearnable examples, showing enhanced protection capabilities through extensive experiments.
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.