Abstract
Users of fitness trackers regularly share their data with a variety of people and entities and do not consider this data as very sensitive. Yet, this data could be used to infer additional information, such as mood, health status, or even identity. We conducted interviews and a survey with fitness tracker users to examine their awareness and attitudes towards multiple inference scenarios. Our results demonstrate that participants have a higher willingness to share individual primary data over information inferred from that data, providing evidence that users are not considering potential inferences in their sharing decisions. Our findings also identify a number of factors related to users’ attitudes towards inferences.
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Alqhatani, A., Lipford, H.R. (2023). Look Before You Leap! Perceptions and Attitudes Towards Inferences in Wearable Fitness Trackers. In: Moallem, A. (eds) HCI for Cybersecurity, Privacy and Trust. HCII 2023. Lecture Notes in Computer Science, vol 14045. Springer, Cham. https://doi.org/10.1007/978-3-031-35822-7_27
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