Helping Users Understand Privacy Notices with Automated Query Answering Functionality : An Exploratory Study
Helping Users Understand Privacy Notices with Automated Query Answering Functionality : An Exploratory Study
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通过自动查询回答功能帮助用户理解隐私声明：一项探索性研究
DOI：
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发表时间：
2018
期刊：
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通讯作者：
N. Sadeh
中科院分区：
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作者：
Kanthashree Mysore Sathyendra;Abhilasha Ravichander;Peter Story;A. Black;N. Sadeh
隐私声明是默认机制，用于告知用户有关技术（例如网站、移动应用程序、物联网设备）和与之交互的流程的数据收集和使用实践。事实证明，这些政策的篇幅及其通常令人费解的语言阻碍了大多数用户阅读它们。自然语言处理和机器学习的最新进展为开发能够从隐私政策文本中自动提取语句（或“注释”）的技术打开了大门。这些技术可以帮助用户快速识别他们关心的隐私声明中的那些元素，而无需他们阅读声明全文。在本文中，我们回顾了与查询应答功能开发相关的要求，该功能使用户能够提出有关隐私声明特定方面的问题（例如，此应用程序是否与第三方共享我的位置？我是否能够查看该网站收集的有关我的信息？我可以删除我的帐户吗？该公司将保留我的信息多长时间？）。我们讨论支持此类功能的不同可能方法，以及它们与自动注释隐私声明的最新进展如何相关。给出了通过不同的机器学习/自然语言处理技术获得的初步结果，表明查询应答功能可能是一种特别有前途的方法来告知用户隐私实践。特别是，与旨在从隐私声明文本中提取详细陈述的自动注释技术相比，查询应答功能可以配置为返回从隐私声明中提取的短文本片段，并依靠用户（而不是计算机）来解释这些片段中文本的一些细微差别。这种方法可能比全自动注释技术更强大，至少在目前，全自动注释技术难以解释更细微的差别。本文还简要讨论了与查询应答功能的可能扩展相关的机遇和挑战，这些功能以隐私助理的形式存在，能够与用户进行有趣的对话，以澄清他们的一些问题，并帮助他们了解隐私声明文本在多大程度上明确解决了（或没有）他们的担忧。与全自动注释技术相比，此类功能可以提供更大的鲁棒性和可用性，并且最终还可以利用用户已经知道和/或关心的模型。
Privacy notices are the default mechanism used to inform users about the data collection and use practices of technologies (e.g., websites, mobile apps, Internet of Things devices) and processes with which they interact. The length of these policies and their often convoluted language have been shown to discourage most users from reading them. Recent progress in natural language processing and machine learning has opened the door to the development of technologies that are capable of automatically extracting statements (or “annotations”) from the text of privacy policies. These technologies could help users quickly identify those elements of a privacy notice they care about without requiring them to read the full text of the notice. In this article, we review the requirements associated with the development of Query Answering functionality that would enable users to ask questions about specific aspects of privacy notices (e.g. Does this app share my location with third parties? Am I able to review the information this website collects about me? Can I delete my account? For how long is my information going to be retained by this company?). We discuss different possible approaches to supporting such functionality and how they relate to recent advances in automatically annotating privacy notices. Initial results obtained with different machine learning/natural language processing techniques are presented, suggesting that Query Answering functionality could be a particularly promising approach to informing users about privacy practices. In particular, in contrast to automated annotation techniques that aim to extract detailed statements from the text of privacy notices, Query Answering functionality could be configured to return short text fragments extracted from privacy notices and rely on the user (rather than the computer) to interpret some of the finer nuances of the text found in these fragments. Such an approach could potentially prove more robust than fully automated annotation techniques, which at least at this time struggle with the interpretation of finer nuances. This article also includes a brief discussion of opportunities and challenges associated with possible extensions of Query Answering functionality in the form of privacy assistants capable of entertaining dialogues with users to clarify some of their questions and help them understand to what extent their concerns are explicitly addressed (or not) by the text of privacy notices. Such functionality could provide for yet greater robustness and usability than fully automated annotation techniques, and could eventually also leverage models of what the user already knows and/or cares about.