Researchers are building Internet of Things (IoT) systems that aim to raise users’ privacy awareness, so that these users can make informed privacy decisions. However, there is a lack of empirical research on the practical implications of informed privacy decision-making in IoT. To gain deeper insights into this question, we conducted an online study ( N = 488) of people’s privacy decision-making as well as their levels of privacy awareness toward diverse IoT service scenarios. Statistical analysis on the collected data confirmed that people who are well aware of potential privacy risks in a scenario tend to make more conservative and confident privacy decisions. Machine learning (ML) experiments also revealed that individuals overall privacy awareness is the most important feature when predicting their privacy decisions. We verified that ML models trained on privacy decisions made with confidence can produce highly accurate privacy recommendations for users (area under the ROC curve (AUC) of 87%). Based on these findings, we propose functional requirements for privacy-aware systems to facilitate well-informed privacy decision-making in IoT, which results in conservative and confident decisions that enjoy high consistency.

中文翻译：

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物联网环境中自信的隐私决策

研究人员正在构建旨在提高用户隐私意识的物联网 (IoT) 系统，以便这些用户能够做出明智的隐私决策。然而，缺乏对物联网中知情隐私决策的实际影响的实证研究。为了更深入地了解这个问题，我们进行了一项在线研究（ñ= 488) 的人们的隐私决策以及他们对各种物联网服务场景的隐私意识水平。对收集到的数据的统计分析证实，对某个场景中潜在的隐私风险有充分了解的人往往会做出更保守和自信的隐私决策。机器学习 (ML) 实验还表明，个人整体隐私意识是预测其隐私决策时最重要的特征。我们验证了基于信心做出的隐私决策训练的 ML 模型可以为用户生成高度准确的隐私建议（ROC 曲线下面积 (AUC) 为 87%）。基于这些发现，我们提出了隐私感知系统的功能要求，以促进物联网中知情的隐私决策，从而导致保守和自信的具有高度一致性的决策。