Are You Really Muted?: A Privacy Analysis of Mute Buttons in Video Conferencing Apps
Are You Really Muted?: A Privacy Analysis of Mute Buttons in Video Conferencing Apps
复制标题
DOI：
10.48550/arxiv.2204.06128
复制
发表时间：
2022-04
期刊：
影响因子：
--
通讯作者：
Yucheng Yang;Jack West;G. Thiruvathukal;Neil Klingensmith;Kassem Fawaz
中科院分区：
文献类型：
--
作者：
Yucheng Yang;Jack West;G. Thiruvathukal;Neil Klingensmith;Kassem Fawaz
视频会议应用程序（vca）使以前的私人空间——卧室、客厅和厨房——成为办公室的半公共扩展成为可能。在大多数情况下，用户在他们的个人空间中接受了这些应用程序，而没有考虑在会议期间管理他们私人数据使用的权限模型。虽然对设备视频摄像头的访问受到严格控制，但在确保访问麦克风的隐私程度方面，几乎没有采取任何措施。在这项工作中，我们提出了一个问题：当用户点击VCA中的静音按钮时，麦克风数据会发生什么变化？我们首先进行用户研究，分析用户对静音键权限模型的理解情况。然后，使用运行时二进制分析工具，我们在许多流行的vca中跟踪原始音频流，因为它从音频驱动程序遍历应用程序到网络。我们发现在vca之间处理麦克风数据的分散策略-一些在静音期间连续监控麦克风输入，而另一些则定期这样做。一个应用程序在静音状态下将音频统计数据传输到其遥测服务器。使用我们在通往遥测服务器的途中拦截的网络流量，我们实现了一个概念验证后台活动分类器，并演示了在会议期间推断正在进行的后台活动（烹饪、清洁、打字等）的可行性。当用户处于静音状态时，我们使用拦截的传出遥测数据包识别六种常见后台活动，实现了81.9%的宏观精度。
Video conferencing apps (VCAs) make it possible for previously private spaces — bedrooms, living rooms, and kitchens — into semi-public extensions of the office. For the most part, users have accepted these apps in their personal space without much thought about the permission models that govern the use of their private data during meetings. While access to a device’s video camera is carefully controlled, little has been done to ensure the same level of privacy for accessing the microphone. In this work, we ask the question: what happens to the microphone data when a user clicks the mute button in a VCA? We first conduct a user study to analyze users’ understanding of the permission model of the mute button. Then, using runtime binary analysis tools, we trace raw audio flow in many popular VCAs as it traverses the app from the audio driver to the network. We find fragmented policies for dealing with microphone data among VCAs — some continuously monitor the microphone input during mute, and others do so periodically. One app transmits statistics of the audio to its telemetry servers while the app is muted. Using network traffic that we intercept en route to the telemetry server, we implement a proof-of-concept background activity classifier and demonstrate the feasibility of inferring the ongoing background activity during a meeting — cooking, cleaning, typing, etc. We achieved 81.9% macro accuracy on identifying six common background activities using intercepted outgoing telemetry packets when a user is muted.