As the adoption of smart devices continues to permeate all aspects of our lives, concerns surrounding user privacy have become more pertinent than ever before. While privacy policies define the data management practices of their manufacturers, previous work has shown that they are rarely read and understood by users. Hence, automatic analysis of privacy policies has been shown to help provide users with appropriate insights. Previous research has extensively analyzed privacy policies of websites, e-commerce, and mobile applications, but privacy policies of smart devices, present some differences and specific challenges such as the difficulty to find and collect them. We present PrivacyLens, a novel framework for discovering and collecting past, present, and future smart device privacy policies and harnessing NLP and ML algorithms to analyze them. PrivacyLens is currently deployed, collecting, analyzing, and publishing insights about privacy policies to assist different stakeholders of smart devices, such as users, policy authors, and regulators. We show several examples of analytical tasks enabled by PrivacyLens, including comparisons of devices per type and manufacturing country, categorization of privacy policies, and impact of data regulations on data practices. At the time of submitting this paper, PrivacyLens had collected and analyzed more than 1,200 privacy policies for 7,300 smart device

翻译：随着智能设备日益渗透到我们生活的各个方面，用户隐私问题变得比以往任何时候都更加紧迫。尽管隐私政策定义了制造商的数据管理实践，但以往研究表明，用户很少阅读和理解这些政策。因此，隐私政策的自动分析已被证明有助于为用户提供适当的见解。以往研究已广泛分析了网站、电子商务和移动应用的隐私政策，但智能设备的隐私政策存在一些差异和具体挑战，例如难以查找和收集。我们提出了PrivacyLens，这是一个新颖的框架，用于发现和收集过去、现在及未来的智能设备隐私政策，并利用自然语言处理和机器学习算法进行分析。PrivacyLens目前已部署运行，收集、分析并发布关于隐私政策的见解，以帮助智能设备的不同利益相关者，如用户、政策制定者和监管机构。我们展示了由PrivacyLens支持的若干分析任务示例，包括按类型和制造国对设备进行比较、隐私政策分类以及数据法规对数据实践的影响。在提交本文时，PrivacyLens已为7,300台智能设备收集并分析了超过1,200份隐私政策。