With the advent of machine learning in applications of critical infrastructure such as healthcare and energy, privacy is a growing concern in the minds of stakeholders. It is pivotal to ensure that neither the model nor the data can be used to extract sensitive information used by attackers against individuals or to harm whole societies through the exploitation of critical infrastructure. The applicability of machine learning in these domains is mostly limited due to a lack of trust regarding the transparency and the privacy constraints. Various safety-critical use cases (mostly relying on time-series data) are currently underrepresented in privacy-related considerations. By evaluating several privacy-preserving methods regarding their applicability on time-series data, we validated the inefficacy of encryption for deep learning, the strong dataset dependence of differential privacy, and the broad applicability of federated methods.

翻译：随着在医疗和能源等关键基础设施的应用方面机器学习的到来,隐私日益成为利益攸关方心目中日益关切的一个问题,关键是确保模型和数据都无法用于提取攻击者针对个人使用的敏感信息,或通过利用关键基础设施损害整个社会。由于对透明度和隐私限制缺乏信任,机器学习在这些方面的适用性大都有限。各种安全关键使用案例(主要依赖时间序列数据)目前在与隐私有关的考虑中代表性不足。我们通过评价关于这些案例对时间序列数据适用性的若干隐私保留方法,验证了加密对深层学习的无效性、差异隐私对数据集的强烈依赖以及联邦方法的广泛适用性。