Data-Driven Privacy Indicators
Data-Driven Privacy Indicators
复制标题
数据驱动的隐私指标
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
K. Aberer
中科院分区：
文献类型：
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作者：
Hamza Harkous;Rameez Rahman;K. Aberer
第三方应用程序在托管用户数据的现有平台之上运行。尽管这些应用程序访问这些数据是为了向用户提供特定服务，但它们也可以将其用于货币化或分析目的。在实践中，用户的隐私期望与第三方应用程序的实际访问级别之间存在显着差距，而第三方应用程序的权限往往过高。由于现有隐私指标存在缺陷，用户通常不太清楚这些应用程序获取了哪些数据。更重要的是，我们正在见证逆向隐私的兴起：第三方收集数据，使他们能够了解用户不知道、不记得或无法访问的用户信息。在本文中，我们描述了我们最近设计和评估数据驱动隐私指标（DDPI）的经验，这是一种试图缩小上述隐私差距的方法。 DDPI是通过可信方（例如应用平台）分析用户数据并将分析结果集成到隐私指标界面中来实现的。我们在云平台上的第三方应用程序（例如 Google Drive 和 Dropbox）的背景下讨论 DDPI。具体来说，我们介绍了我们最近在“深远的见解”方面的工作，它向用户展示了应用程序可以推断出的关于他们的见解（例如，他们感兴趣的主题、协作和活动模式等）。然后，我们提出基于历史的见解，这是一种新颖的隐私指标，可以根据用户或其协作者之前安装的应用程序，告知用户应用程序供应商已经可以访问哪些数据。我们进一步讨论了关于新 DDPI 的未来想法，并概述了大规模部署此类指标所面临的挑战。
Third party applications work on top of existing platforms that host users’ data. Although these apps access this data to provide users with specific services, they can also use it for monetization or profiling purposes. In practice, there is a significant gap between users’ privacy expectations and the actual access levels of 3rd party apps, which are often over-privileged. Due to weaknesses in the existing privacy indicators, users are generally not well-informed on what data these apps get. Even more, we are witnessing the rise of inverse privacy: 3rd parties collect data that enables them to know information about users that users do not know, cannot remember, or cannot reach. In this paper, we describe our recent experiences with the design and evaluation of Data-Driven Privacy Indicators (DDPIs), an approach attempting to reduce the aforementioned privacy gap. DDPIs are realized through analyzing user’s data by a trusted party (e.g., the app platform) and integrating the analysis results in the privacy indicator’s interface. We discuss DDPIs in the context of 3rd party apps on cloud platforms, such as Google Drive and Dropbox. Specifically, we present our recent work on Far-reaching Insights, which show users the insights that apps can infer about them (e.g., their topics of interest, collaboration and activity patterns etc.). Then we present History-based insights, a novel privacy indicator which informs the user on what data is already accessible by an app vendor, based on previous app installations by the user or her collaborators. We further discuss future ideas on new DDPIs, and we outline the challenges facing the wide-scale deployment of such indicators.