The rapid rise of IoT and Big Data has facilitated copious data driven applications to enhance our quality of life. However, the omnipresent and all-encompassing nature of the data collection can generate privacy concerns. Hence, there is a strong need to develop techniques that ensure the data serve only the intended purposes, giving users control over the information they share. To this end, this paper studies new variants of supervised and adversarial learning methods, which remove the sensitive information in the data before they are sent out for a particular application. The explored methods optimize privacy preserving feature mappings and predictive models simultaneously in an end-to-end fashion. Additionally, the models are built with an emphasis on placing little computational burden on the user side so that the data can be desensitized on device in a cheap manner. Experimental results on mobile sensing and face datasets demonstrate that our models can successfully maintain the utility performances of predictive models while causing sensitive predictions to perform poorly.
翻译:IoT 和 Big Data 的迅速崛起为大量数据驱动应用提供了便利,以提高我们的生活质量。然而,数据收集的无处不在和包罗万象的性质可能会引起隐私问题。因此,非常需要开发技术,确保数据只服务于预期目的,使用户能够控制他们共享的信息。为此,本文研究了受监管和对立学习方法的新变体,这些变体在将数据中的敏感信息送出用于特定应用之前就清除了这些信息。探索的方法优化了隐私保护特征图谱和预测模型,同时以端至端的方式进行。此外,模型的建立重点是将少量的计算负担放在用户一边,以便数据能够以廉价的方式在设备上淡化。移动感测和脸部数据集的实验结果表明,我们的模型可以成功地保持预测模型的实用性,同时造成敏感预测效果不佳。