BrowserFlow
BrowserFlow
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浏览器流
DOI：
10.1145/2988336.2988345
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Papagiannis I
中科院分区：
文献类型：
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作者：
Papagiannis I
随着在企业环境中使用Google Docs或Evernote等外部云服务，失去对敏感数据的控制成为组织的主要担忧。普通用户通常会意外违反数据披露策略，例如，在浏览器选项卡中的文档之间共享文本时。我们的目标是帮助这些用户遵守数据披露政策：我们希望提醒他们从受信任的云服务到不受信任的云服务的潜在未经授权的数据泄露。当用户可以以任意方式修改数据时，这尤其具有挑战性，他们使用多个云服务，并且云服务不能改变。为了稳健地跟踪文本数据在云服务中的传播，我们引入了不精确的数据流跟踪，通过检测和量化文本片段之间的相似性来隐式识别数据流。为了对违反数据披露策略的行为进行推理，我们描述了一种新的文本披露模型，该模型基于相似性，将Web浏览器中的文本片段与安全标签相关联，并识别流向不可信服务的未经授权的数据流。我们通过BrowserFlow演示了不精确数据跟踪的适用性，BrowserFlow是一个基于浏览器的中间件，当用户将潜在的敏感文本暴露给不受信任的云服务时，它会发出警报。我们的实验表明，BrowserFlow能够稳健地跟踪数据流和管理文档的安全标签，而不会对性能产生明显的影响。
With the use of external cloud services such as Google Docs or Evernote in an enterprise setting, the loss of control over sensitive data becomes a major concern for organisations. It is typical for regular users to violate data disclosure policies accidentally, e.g. when sharing text between documents in browser tabs. Our goal is to help such users comply with data disclosure policies: we want to alert them about potentially unauthorised data disclosure from trusted to untrusted cloud services. This is particularly challenging when users can modify data in arbitrary ways, they employ multiple cloud services, and cloud services cannot be changed.To track the propagation of text data robustly across cloud services, we introduce imprecise data flow tracking, which identifies data flows implicitly by detecting and quantifying the similarity between text fragments. To reason about violations of data disclosure policies, we describe a new text disclosure model that, based on similarity, associates text fragments in web browsers with security tags and identifies unauthorised data flows to untrusted services. We demonstrate the applicability of imprecise data tracking through BrowserFlow, a browser-based middleware that alerts users when they expose potentially sensitive text to an untrusted cloud service. Our experiments show that BrowserFlow can robustly track data flows and manage security tags for documents with no noticeable performance impact.