论文标题
通过删除不需要的依赖性来增强机器学习
Privacy Enhancing Machine Learning via Removal of Unwanted Dependencies
论文作者
论文摘要
物联网和大数据的快速崛起促进了大量数据驱动的应用程序，以提高我们的生活质量。但是，数据收集的无所不在和无所不包的性质可能会引起隐私问题。因此，非常需要开发技术来确保数据仅服务于预期目的，从而使用户控制他们共享的信息。为此，本文研究了有监督和对抗性学习方法的新变体，这些变体在将数据发送到特定应用程序中之前删除数据中的敏感信息。探索方法以端到端的方式同时优化了保留特征映射和预测模型的隐私。此外，这些模型的构建是强调在用户端几乎没有计算负担，以便可以以便宜的方式在设备上脱敏。移动传感和面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效用性能，同时导致敏感预测的性能较差。
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.
