Deep Learning Based Inference of Private Information Using Embedded Sensors in Smart Devices
Deep Learning Based Inference of Private Information Using Embedded Sensors in Smart Devices
复制标题
DOI：
10.1109/mnet.2018.1700349
复制
发表时间：
2018-07-01
期刊：
影响因子：
9.3
通讯作者：
Li, Yingshu
中科院分区：
文献类型：
--
作者：
Liang, Yi;Cai, Zhipeng;Li, Yingshu
智能移动的设备和移动的应用程序在过去十年中迅速推出，将这些设备转变为方便和通用的计算平台。来自智能设备的感官数据是滋养移动的服务的重要资源，它们被认为是可以在没有用户许可的情况下获得的无害信息。在这篇文章中，我们表明，这些看似无害的信息可能会导致严重的隐私问题。首先，我们证明了用户在智能设备屏幕上的点击位置可以通过采用一些深度学习技术基于感官数据来识别。其次，示出了可以收集用于每种类型的应用的点击流简档，使得可以准确地推断用户的应用使用习惯。在我们的实验中，收集了102名志愿者的感官数据和移动的应用程序使用信息。实验结果表明，利用卷积神经网络进行分接头位置推理，其预测精度可达90%以上。此外，基于所推断的轻击位置信息，可以高准确度地推断用户的应用使用习惯和密码。
Smart mobile devices and mobile apps have been rolling out at swift speeds over the last decade, turning these devices into convenient and general-purpose computing platforms. Sensory data from smart devices are important resources to nourish mobile services, and they are regarded as innocuous information that can be obtained without user permissions. In this article, we show that this seemingly innocuous information could cause serious privacy issues. First, we demonstrate that users' tap positions on the screens of smart devices can be identified based on sensory data by employing some deep learning techniques. Second, it is shown that tap stream profiles for each type of apps can be collected, so that a user's app usage habit can be accurately inferred. In our experiments, the sensory data and mobile app usage information of 102 volunteers are collected. The experiment results demonstrate that the prediction accuracy of tap position inference can be at least 90 percent by utilizing convolutional neural networks. Furthermore, based on the inferred tap position information, users' app usage habits and passwords may be inferred with high accuracy.