The training of contemporary deep learning models heavily relies on publicly
available data, posing a risk of unauthorized access to online data and raising
concerns about data privacy. Current approaches to creating unlearnable data
involve incorporating small, specially designed noises, but these methods
strictly limit data usability, overlooking its potential usage in authorized
scenarios. In this paper, we extend the concept of unlearnable data to
conditional data learnability and introduce \textbf{U}n\textbf{G}eneralizable
\textbf{E}xamples (UGEs). UGEs exhibit learnability for authorized users while
maintaining unlearnability for potential hackers. The protector defines the
authorized network and optimizes UGEs to match the gradients of the original
data and its ungeneralizable version, ensuring learnability. To prevent
unauthorized learning, UGEs are trained by maximizing a designated distance
loss in a common feature space. Additionally, to further safeguard the
authorized side from potential attacks, we introduce additional undistillation
optimization. Experimental results on multiple datasets and various networks
demonstrate that the proposed UGEs framework preserves data usability while
reducing training performance on hacker networks, even under different types of
attacks.

通过引入一种名为 UGE 的方法，本文提出了一个保护训练过程中数据隐私的框架，其旨在在授权用户具备可学习性的同时，对潜在黑客具备不可学习性，从而确保数据的可用性和保护数据的隐私。

非普适例子

Ungeneralizable Examples

The prevalence of data scraping from social media as a means to obtain
datasets has led to growing concerns regarding unauthorized use of data. Data
poisoning attacks have been proposed as a bulwark against scraping, as they
make data "unlearnable" by adding small, imperceptible perturbations.
Unfortunately, existing methods require knowledge of both the target
architecture and the complete dataset so that a surrogate network can be
trained, the parameters of which are used to generate the attack. In this work,
we introduce autoregressive (AR) poisoning, a method that can generate poisoned
data without access to the broader dataset. The proposed AR perturbations are
generic, can be applied across different datasets, and can poison different
architectures. Compared to existing unlearnable methods, our AR poisons are
more resistant against common defenses such as adversarial training and strong
data augmentations. Our analysis further provides insight into what makes an
effective data poison.

该研究介绍了自回归（AR）中毒的方法，可以生成具有毒性的数据，而不需要访问更广泛的数据集，比起现有的不可学习方法，我们的 AR 毒药更加抵抗对抗训练以及强数据扩充等常见的防御。