Being able to understand the security and privacy (S&P) concerns of IoT users brings benefits to both developers and users. To learn about users' views, we examine Amazon IoT reviews - one of the biggest IoT markets. This work presents a state-of-the-art methodology to identify and categorize reviews in which users express S&P concerns. We developed an automated pipeline by fine-tuning GPT-3.5-Turbo to build two models: the Classifier-Rationalizer-Categorizer and the Thematic Mapper. By leveraging dynamic few-shot prompting and the model's large context size, our pipeline achieved over 97% precision and recall, significantly outperforming keyword-based and classical ML methods. We applied our pipeline to 91K Amazon reviews about fitness trackers, smart speakers and cameras, over multiple years. We found that on average 5% contained S&P concerns, while security camera exhibited the highest prevalence at 10%. Our method detected significantly more S&P-relevant reviews than prior works: 15x more for fitness trackers, 29% more for smart speakers, and 70% more for cameras. Our longitudinal analysis reveals that concerns like surveillance and data control have persisted for years, suggesting limited industry progress. We demonstrate that across all device types, users consistently demand more precise control over what data is collected and shared. We uncover challenges in multi-user and multi-device interactions, identifying two previously unreported themes concerning inadequate controls for account separation and data access. These findings, ranging from broad persistent trends to specific instances of customer loss, offer actionable insights for developers to improve user satisfaction and trust.

翻译：理解物联网用户的安全与隐私关切对开发者和用户均具有重要价值。为探究用户观点，本研究以亚马逊物联网评论——全球最大的物联网市场之一——为分析对象，提出了一种前沿方法以识别并分类表达安全与隐私关切的用户评论。我们通过微调GPT-3.5-Turbo构建了包含分类器-归因器-分类器与主题映射器的自动化分析流程。借助动态少样本提示技术及模型的大上下文容量，该流程实现了超过97%的精确率与召回率，显著优于基于关键词和传统机器学习的方法。我们将该流程应用于跨越数年的9.1万条亚马逊智能手环、智能音箱与智能摄像头评论，发现平均5%的评论包含安全隐私关切，其中智能摄像头的关切比例最高（10%）。本方法检测到的安全隐私相关评论数量远超既有研究：智能手环提升15倍，智能音箱增加29%，智能摄像头增长70%。纵向分析表明，监控与数据控制等关切问题持续存在多年，暗示行业进展有限。研究发现，在所有设备类型中，用户始终要求对数据收集与共享行为实施更精确的控制。我们揭示了多用户与多设备交互中的挑战，识别出两个先前未被报告的议题：账户分离控制不足与数据访问权限缺陷。这些发现既涵盖广泛的持续性趋势，也包含具体的用户流失案例，为开发者提升用户满意度与信任度提供了可操作的洞见。