The Curious Case of the PDF Converter that Likes Mozart: Dissecting and Mitigating the Privacy Risk of Personal Cloud Apps
The Curious Case of the PDF Converter that Likes Mozart: Dissecting and Mitigating the Privacy Risk of Personal Cloud Apps
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喜欢莫扎特的 PDF 转换器的奇案：剖析并降低个人云应用程序的隐私风险
DOI：
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发表时间：
2016
影响因子：
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通讯作者：
K. Aberer
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文献类型：
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作者：
Hamza Harkous;Rameez Rahman;Bojan Karlas;K. Aberer
在个人云服务上运行的第三方应用，如Google Drive和Drop-Box，需要访问用户的数据才能提供一些功能。通过对来自谷歌Chrome商店的100款流行的Google Drive应用程序的详细分析，我们发现现有的权限模型经常被滥用：大约三分之二的分析应用程序具有过度特权，即它们访问的数据比它们运行所需的数据更多。在这项工作中，我们分析了三种不同的权限模型，旨在阻止用户安装过度特权的应用程序。在对210个真实用户的实验中，我们发现最成功的权限模型是我们的新集成方法，我们称之为深远洞察力。影响深远的洞察向用户提供应用程序可以提供的数据驱动型洞察(例如，他们感兴趣的主题、协作和活动模式等)。因此，他们寻求弥合第三方对用户的实际了解和用户对其隐私泄露的感知之间的差距。我们的结果证明了深远洞察在弥合这一差距方面的有效性，因为在阻止用户安装过于特权的应用程序方面，深远洞察的平均效率是目前模型的两倍。为了提高普遍的隐私意识，我们部署了PrivySeal，这是一个公开提供的、专注于隐私的应用程序商店，它使用了深远的见解。基于从商店用户数据中提取的知识(来自1440名用户和662个已安装应用程序的超过115 GB的Google Drive数据)，我们还从开发者和云提供商的角度描绘了第三方云应用程序的生态系统。最后，我们提出了几个一般性的建议，可以指导其他未来在云隐私领域的工作。据我们所知，我们的工作是第一次在如此深入的情况下解决云平台上第三方应用程序带来的隐私风险。
Abstract Third party apps that work on top of personal cloud services, such as Google Drive and Drop-box, require access to the user’s data in order to provide some functionality. Through detailed analysis of a hundred popular Google Drive apps from Google’s Chrome store, we discover that the existing permission model is quite often misused: around two-thirds of analyzed apps are over-privileged, i.e., they access more data than is needed for them to function. In this work, we analyze three different permission models that aim to discourage users from installing over-privileged apps. In experiments with 210 real users, we discover that the most successful permission model is our novel ensemble method that we call Far-reaching Insights. Far-reaching Insights inform the users about the data-driven insights that apps can make about them (e.g., their topics of interest, collaboration and activity patterns etc.) Thus, they seek to bridge the gap between what third parties can actually know about users and users’ perception of their privacy leakage. The efficacy of Far-reaching Insights in bridging this gap is demonstrated by our results, as Far-reaching Insights prove to be, on average, twice as effective as the current model in discouraging users from installing over-privileged apps. In an effort to promote general privacy awareness, we deployed PrivySeal, a publicly available privacy-focused app store that uses Far-reaching Insights. Based on the knowledge extracted from data of the store’s users (over 115 gigabytes of Google Drive data from 1440 users with 662 installed apps), we also delineate the ecosystem for 3rd party cloud apps from the standpoint of developers and cloud providers. Finally, we present several general recommendations that can guide other future works in the area of privacy for the cloud. To the best of our knowledge, ours is the first work that tackles the privacy risk posed by 3rd party apps on cloud platforms in such depth.