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.

翻译：人工智能（AI）正在对几乎所有领域产生深远影响。构建机器学习模型所需的高质量数据的充分获取，是促成这一成功的关键因素之一。近年来，随着数据在人工智能中的作用被显著放大，关于数据安全利用的担忧也随之出现，尤其是在未经授权使用数据的场景中。为遏制数据滥用，数据不可学习技术已被引入，以使数据无法被利用。然而，当前的不可学习示例缺乏广泛适用所需的泛化能力。本文提出了一种新颖的、泛化的数据保护方法，通过生成可迁移的不可学习示例来实现。据我们所知，这是首个从数据分布角度审视数据隐私问题的解决方案。通过大量实验，我们验证了所提方法在增强泛化保护能力方面的有效性。