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.

翻译：为学习任务共享私有数据对于透明且安全的机器学习应用至关重要。为此已提出多种隐私保护技术，旨在转换数据的同时确保个体隐私。部分技术已集成至工具中，另一些则通过各类在线平台提供。然而，此类工具需手动配置，过程复杂耗时，且要求使用者具备相当的专业知识，可能将使用范围局限于具备高级技术背景的人员。本文提出首个自动化隐私保护方法AUTOPRIV，该方法无需任何手动配置。AUTOPRIV采用元学习实现去标识化流程的自动化，从而促进机器学习任务的数据安全发布。其主要目标在于预测大量隐私配置的预测性能与隐私风险。我们提供了最优化解决方案的排序列表，这些方案有望在新领域内实现最优近似。AUTOPRIV通过显著降低计算复杂度与能耗，展现出卓越的有效性。