由于共享数据也可能揭示敏感信息，因此数据收集爆炸提高了用户的严重隐私问题。隐私保留机制的主要目标是防止恶意第三方推断敏感信息，同时保持共享数据有用。在本文中，我们在时间序列数据和智能仪表（SMS）功耗测量的背景下研究了这个问题。虽然私有和释放变量之间的互信息（MI）已被用作常见的信息理论隐私度测量，但它无法捕获功耗时间序列数据中存在的因果时间依赖性。为了克服这种限制，我们将定向信息（DI）介绍在所考虑的环境中的一种更有意义的隐私措施，并提出了一种新的损失功能。然后使用对抗的侵犯框架进行优化，其中两个经常性神经网络（RNN），称为释放器和对手，受到相反的目标训练。我们对攻击者可以访问释放器使用的所有培训数据集的最坏情况下，从SMS测量中的实证研究从SMS测量，验证所提出的方法并显示隐私和实用程序之间的现有权衡。

智能仪表（SMS）能够几乎实时与实用程序提供者的功耗。这些细粒度的信号携带有关用户的敏感信息，从隐私观点提出了严重问题。在本文中，我们专注于实时隐私威胁，即尝试以在线方式从短信数据推断敏感信息的潜在攻击者。我们采用信息理论隐私措施，并表明它有效地限制了任何攻击者的表现。然后，我们提出了一种普遍的制定来设计一种私有化机制，可以通过向SMS测量增加最小的失真量来提供目标水平。另一方面，为了应对不同的应用，考虑灵活的失真度量。该配方导致一般损失函数，其使用深入学习的对抗性框架进行了优化，其中两个神经网络 - 被称为释放器和对手 - 受到相反的目标训练。然后执行详尽的经验研究以验证所提出的方法的性能，并将其与最先进的方法进行比较，以便占用检测隐私问题。最后，我们还研究了释放者和攻击者之间数据不匹配的影响。

The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosis of many lung diseases. A tremendous number of X-ray imaging studies accompanied by radiological reports are accumulated and stored in many modern hospitals' Picture Archiving and Communication Systems (PACS). On the other side, it is still an open question how this type of hospital-size knowledge database containing invaluable imaging informatics (i.e., loosely labeled) can be used to facilitate the data-hungry deep learning paradigms in building truly large-scale high precision computer-aided diagnosis (CAD) systems.In this paper, we present a new chest X-ray database, namely "ChestX-ray8", which comprises 108,948 frontalview X-ray images of 32,717 unique patients with the textmined eight disease image labels (where each image can have multi-labels), from the associated radiological reports using natural language processing. Importantly, we demonstrate that these commonly occurring thoracic diseases can be detected and even spatially-located via a unified weaklysupervised multi-label image classification and disease localization framework, which is validated using our proposed dataset. Although the initial quantitative results are promising as reported, deep convolutional neural network based "reading chest X-rays" (i.e., recognizing and locating the common disease patterns trained with only image-level labels) remains a strenuous task for fully-automated high precision CAD systems.