While IoT sensors in physical spaces have provided utility and comfort in our lives, their instrumentation in private and personal spaces has led to growing concerns regarding privacy. The existing notion behind IoT privacy is that the sensors whose data can easily be understood and interpreted by humans (such as cameras) are more privacy-invasive than sensors that are not human-understandable, such as RF (radio-frequency) sensors. However, given recent advancements in machine learning, we can not only make sensitive inferences on RF data but also translate between modalities. Thus, the existing notions of privacy for IoT sensors need to be revisited. In this paper, our goal is to understand what factors affect the privacy notions of a non-expert user (someone who is not well-versed in privacy concepts). To this regard, we conduct an online study of 162 participants from the USA to find out what factors affect the privacy perception of a user regarding an RF-based device or a sensor. Our findings show that a user's perception of privacy not only depends upon the data collected by the sensor but also on the inferences that can be made on that data, familiarity with the device and its form factor as well as the control a user has over the device design and its data policies. When the data collected by the sensor is not human-interpretable, it is the inferences that can be made on the data and not the data itself that users care about when making informed decisions regarding device privacy.

翻译：尽管物理空间中的物联网传感器为我们的生活提供了便利和舒适，但它们在私密和个人空间中的部署引发了日益增长的隐私担忧。当前关于物联网隐私的观念认为，数据易于被人类理解和解读的传感器（如摄像头）比人类无法理解的传感器（如射频传感器）更具隐私侵犯性。然而，鉴于机器学习的最新进展，我们不仅能够从射频数据中做出敏感推断，还可以在不同模态之间进行转换。因此，需要重新审视现有的物联网传感器隐私观念。本文旨在理解影响非专业用户（不熟悉隐私概念的用户）隐私观念的因素。为此，我们对来自美国的162名参与者进行了一项在线研究，以找出影响用户对基于射频的设备或传感器隐私感知的因素。我们的研究结果表明，用户对隐私的感知不仅取决于传感器收集的数据，还取决于基于这些数据可以做出的推断、对设备及其形态的熟悉程度，以及用户对设备设计及其数据策略的控制能力。当传感器收集的数据对人类来说不可直接理解时，用户在做出关于设备隐私的知情决策时，真正关心的是基于数据可以做出的推断，而非数据本身。