The number of users of wearable activity trackers (WATs) has rapidly increased over the last decade. Although these devices enable their users to monitor their activities and health, they also raise new security and privacy concerns, given the sensitive data (e.g., steps, heart rate) they collect and the information that can be inferred from this data (e.g., diseases). In addition to sharing with the service providers (e.g., Fitbit), WAT users can share their fitness data with third-party applications (TPAs) and individuals. Understanding how and with whom users share their fitness data and what kind of approaches they take to preserve their privacy are key to assessing the underlying privacy risks and to further designing appropriate privacy-enhancing techniques. In this work, we perform, through a large-scale survey of N=628 WAT users, the first quantitative and qualitative analysis of users' awareness, understanding, attitudes, and behaviors toward fitness-data sharing with TPAs and individuals. By asking these users to draw their thoughts, we explore, in particular, users' practices and actual behaviors toward fitness-data sharing and their mental models. Our empirical results show that about half of WAT users underestimate the number of TPAs to which they have granted access to their data, and 63% share data with at least one TPA that they do not actively use (anymore). Furthermore, 29% of the users do not revoke TPA access to their data because they forget they gave access to it in the first place, and 8% were not even aware they could revoke access to their data. Finally, their mental models, as well as some of their answers, demonstrate substantial gaps in their understanding of the data-sharing process. Importantly, 67% of the respondents think that TPAs cannot access the fitness data that was collected before they granted access to it, whereas TPAs actually can do this.

Fitness trackers are increasingly popular. The data they collect provides substantial benefits to their users, but it also creates privacy risks. In this work, we investigate how fitness-tracker users perceive the utility of the features they provide and the associated privacy-inference risks. We conduct a longitudinal study composed of a four-month period of fitness-tracker use (N = 227), followed by an online survey (N = 227) and interviews (N = 19). We assess the users' knowledge of concrete privacy threats that fitness-tracker users are exposed to (as demonstrated by previous work), possible privacy-preserving actions users can take, and perceptions of utility of the features provided by the fitness trackers. We study the potential for data minimization and the users' mental models of how the fitness tracking ecosystem works. Our findings show that the participants are aware that some types of information might be inferred from the data collected by the fitness trackers. For instance, the participants correctly guessed that sexual activity could be inferred from heart-rate data. However, the participants did not realize that also the non-physiological information could be inferred from the data. Our findings demonstrate a high potential for data minimization, either by processing data locally or by decreasing the temporal granularity of the data sent to the service provider. Furthermore, we identify the participants' lack of understanding and common misconceptions about how the Fitbit ecosystem works.