Sharing private data for learning tasks is pivotal for transparent and secure
machine learning applications. Many privacy-preserving techniques have been
proposed for this task aiming to transform the data while ensuring the privacy
of individuals. Some of these techniques have been incorporated into tools,
whereas others are accessed through various online platforms. However, such
tools require manual configuration, which can be complex and time-consuming.
Moreover, they require substantial expertise, potentially restricting their use
to those with advanced technical knowledge. In this paper, we propose AUTOPRIV,
the first automated privacy-preservation method, that eliminates the need for
any manual configuration. AUTOPRIV employs meta-learning to automate the
de-identification process, facilitating the secure release of data for machine
learning tasks. The main goal is to anticipate the predictive performance and
privacy risk of a large set of privacy configurations. We provide a ranked list
of the most promising solutions, which are likely to achieve an optimal
approximation within a new domain. AUTOPRIV is highly effective as it reduces
computational complexity and energy consumption considerably.

AUTOPRIV 是第一个自动化的隐私保护方法，利用元学习自动化进行去身份化过程，有助于安全发布用于机器学习任务的数据。它通过提供一系列有前景的解决方案来预测预测性能和隐私风险，从而在新领域内实现最佳近似。通过将计算复杂性和能源消耗大大降低，AUTOPRIV 具有很高的效能。