Understanding factors behind IoT privacy – A user's perspective on RF sensors
CoRR(2024)
摘要
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.
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