X-Ray细化:支持智能手机应用带来的信息暴露的探索和细化

M. V. Kleek, Reuben Binns, Jun Zhao, Adam Slack, Sauyon Lee, Dean Ottewell, N. Shadbolt
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引用次数: 43

摘要

大多数智能手机应用收集并与各种第一方和第三方共享信息;然而,这种数据收集实践在很大程度上仍然不为最终用户所知,也不受其控制。在本文中,我们试图了解工具的潜力,以帮助人们根据他们的应用使用情况来优化他们对第三方的曝光率。我们设计了一种名为“X-Ray Refine”的交互式、焦点加上下文显示系统,它使用了超过100万个安卓应用的模型,根据用户使用应用的时间长短,将用户的暴露情况可视化。为了支持缓解策略的探索,emprefine可以模拟诸如减少应用程序使用、删除和替换等操作。一项针对emprefine的实验室研究发现,参与者对自己的数据暴露有了较高的理解,并识别出了违反他们预期和隐私偏好的数据收集行为。参与者还制定了实现隐私目标的定制策略,确定了实现这些目标的主要障碍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
X-Ray Refine: Supporting the Exploration and Refinement of Information Exposure Resulting from Smartphone Apps
Most smartphone apps collect and share information with various first and third parties; yet, such data collection practices remain largely unbeknownst to, and outside the control of, end-users. In this paper, we seek to understand the potential for tools to help people refine their exposure to third parties, resulting from their app usage. We designed an interactive, focus-plus-context display called X-Ray Refine (Refine) that uses models of over 1 million Android apps to visualise a person's exposure profile based on their durations of app use. To support exploration of mitigation strategies, emphRefine can simulate actions such as app usage reduction, removal, and substitution. A lab study of emphRefine found participants achieved a high-level understanding of their exposure, and identified data collection behaviours that violated both their expectations and privacy preferences. Participants also devised bespoke strategies to achieve privacy goals, identifying the key barriers to achieving them.
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