隐私策略中模糊词句的自动检测
Automatic Detection of Vague Words and Sentences in Privacy Policies
摘要：
网站隐私政策是用户衡量公司如何收集、使用和共享其个人数据的唯一最重要的信息来源。然而，隐私政策往往是模糊的，人们很难理解的内容。它们的不透明性对用户和政策监管者都构成了重大挑战。在本文中，我们试图确定隐私政策中的模糊内容。我们构建了第一个人工标注的模糊词语和句子语料库，并对自动模糊检测进行了实证研究。特别是，我们调查上下文感知和上下文不可知模型预测模糊的话，并探索分类器生成对抗网络的特点句子的模糊性。我们的实验结果证明了所提出的方法的有效性。最后，本文提出了解决隐私策略不合理性和提高隐私策略可用性的建议。
英文摘要：
Website privacy policies represent the single most important source of information for users to gauge how their personal data are collected, used and shared by companies. However, privacy policies are often vague and people struggle to understand the content. Their opaqueness poses a significant challenge to both users and policy regulators. In this paper, we seek to identify vague content in privacy policies. We construct the first corpus of human-annotated vague words and sentences and present empirical studies on automatic vagueness detection. In particular, we investigate context-aware and context-agnostic models for predicting vague words, and explore auxiliary-classifier generative adversarial networks for characterizing sentence vagueness. Our experimental results demonstrate the effectiveness of proposed approaches. Finally, we provide suggestions for resolving vagueness and improving the usability of privacy policies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
