用于隐私策略覆盖分析的序列分类模型比较研究
A Comparative Study of Sequence Classification Models for Privacy Policy Coverage Analysis
摘要：
隐私政策是描述网站如何收集、使用和分发用户数据的法律文件。不幸的是，这些文档通常过于复杂，充满了法律术语，使用户难以完全理解收集的究竟是什么以及为什么。我们对这个问题的解决方案是使用广泛的经典机器学习和深度学习技术为用户提供给定网站隐私政策的覆盖分析。给定网站的隐私策略，分类器识别每个逻辑段的相关数据实践。这些数据实践/标签直接取自ESTA-115语料库。例如，数据实践“数据保留”是指网站存储用户信息的时间。覆盖率分析允许用户确定十种可能的数据实践中有多少被覆盖，以及确定与特别感兴趣的数据实践相对应的部分。
英文摘要：
Privacy policies are legal documents that describe how a website will collect, use, and distribute a user's data. Unfortunately, such documents are often overly complicated and filled with legal jargon; making it difficult for users to fully grasp what exactly is being collected and why. Our solution to this problem is to provide users with a coverage analysis of a given website's privacy policy using a wide range of classical machine learning and deep learning techniques. Given a website's privacy policy, the classifier identifies the associated data practice for each logical segment. These data practices/labels are taken directly from the OPP-115 corpus. For example, the data practice "Data Retention" refers to how long a website stores a user's information. The coverage analysis allows users to determine how many of the ten possible data practices are covered, along with identifying the sections that correspond to the data practices of particular interest.
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。
