Analyzing Privacy Policies at Scale
Analyzing Privacy Policies at Scale
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大规模分析隐私政策
DOI：
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发表时间：
2018
影响因子：
3.5
通讯作者：
Noah A. Smith
中科院分区：
文献类型：
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作者：
Shomir Wilson;F. Schaub;Frederick Liu;Kanthashree Mysore Sathyendra;Daniel Smullen;Sebastian Zimmeck;R. Ramanath;Peter Story;Fei Liu;N. Sadeh;Noah A. Smith
网站的隐私政策通常很长，很难理解。虽然研究表明，互联网用户关心他们的隐私，但他们没有时间去了解他们访问的每个网站的政策，而且大多数用户几乎从不阅读隐私政策。最近的一些努力旨在结合众包、机器学习和自然语言处理来大规模地解释隐私政策，从而产生用于界面的注释，告知互联网用户重要的政策细节。然而，很少有人关注众包隐私策略注释的准确性，如何提高众包工作者的工作效率，以及自动分析隐私策略的粒度级别。在本文中，我们将介绍解决这些主题的工作轨迹。我们分析了众包工作者的表现，评估了一种使众包工作者更容易完成隐私政策导向任务的方法，一种用描述性主题标记策略文本片段的粗粒度方法，以及一种识别策略文本中描述的用户选择的细粒度方法。总之，这些努力的结果表明，使用自动化和半自动方法从隐私政策中提取数据实践细节对互联网用户的利益是显著的是有效的。
Website privacy policies are often long and difficult to understand. While research shows that Internet users care about their privacy, they do not have the time to understand the policies of every website they visit, and most users hardly ever read privacy policies. Some recent efforts have aimed to use a combination of crowdsourcing, machine learning, and natural language processing to interpret privacy policies at scale, thus producing annotations for use in interfaces that inform Internet users of salient policy details. However, little attention has been devoted to studying the accuracy of crowdsourced privacy policy annotations, how crowdworker productivity can be enhanced for such a task, and the levels of granularity that are feasible for automatic analysis of privacy policies. In this article, we present a trajectory of work addressing each of these topics. We include analyses of crowdworker performance, evaluation of a method to make a privacy-policy oriented task easier for crowdworkers, a coarse-grained approach to labeling segments of policy text with descriptive themes, and a fine-grained approach to identifying user choices described in policy text. Together, the results from these efforts show the effectiveness of using automated and semi-automated methods for extracting from privacy policies the data practice details that are salient to Internet users’ interests.
影响因子：
1.6
作者：
Das, Anupam;Degeling, Martin;Sadeh, Norman
通讯作者：
Sadeh, Norman