Putting mobile application privacy in context: An empirical study of user privacy expectations for mobile devices
Putting mobile application privacy in context: An empirical study of user privacy expectations for mobile devices
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DOI：
10.1080/01972243.2016.1153012
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发表时间：
2016-01-01
影响因子：
3.5
通讯作者：
Shilton, Katie
中科院分区：
文献类型：
--
作者：
Martin, Kirsten;Shilton, Katie
用户越来越多地使用移动设备从事社交活动和商业，从而为公司和营销人员提供了新的数据收集形式。这些新形式的数据收集形式的用户隐私期望尚不清楚。一个特别困难的挑战是满足对上下文完整性的期望，因为用户隐私期望因收集的数据类型和使用上下文而异。本文说明了如何衡量的细粒度，上下文隐私期望。它从一项阶乘小插图调查中提出了发现，该调查测量了各种现实世界中环境（例如，医疗，导航，音乐），数据类型以及数据使用对用户隐私期望的影响。结果表明，个人的一般隐私偏好对于在特定情况下预测其隐私判断的意义有限。取而代之的是，结果介绍了特定上下文因素和信息用途的相对重要性的细微肖像，并证明了如何找到和测量这些上下文因素。结果还表明，移动应用公司的当前常见活动，例如收获和重复使用位置数据，图像和联系人列表，并不能满足用户的隐私期望。了解用户隐私期望如何根据上下文，数据类型和数据使用突出显示需要政府和行业更严格的隐私保护的领域。
Users increasingly use mobile devices to engage in social activity and commerce, enabling new forms of data collection by firms and marketers. User privacy expectations for these new forms of data collection remain unclear. A particularly difficult challenge is meeting expectations for contextual integrity, as user privacy expectations vary depending upon data type collected and context of use. This article illustrates how fine-grained, contextual privacy expectations can be measured. It presents findings from a factorial vignette survey that measured the impact of diverse real-world contexts (e.g., medical, navigation, music), data types, and data uses on user privacy expectations. Results demonstrate that individuals' general privacy preferences are of limited significance for predicting their privacy judgments in specific scenarios. Instead, the results present a nuanced portrait of the relative importance of particular contextual factors and information uses, and demonstrate how those contextual factors can be found and measured. The results also suggest that current common activities of mobile application companies, such as harvesting and reusing location data, images, and contact lists, do not meet users' privacy expectations. Understanding how user privacy expectations vary according to context, data types, and data uses highlights areas requiring stricter privacy protections by governments and industry.