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1. Privacy attitudes and data valuation among fitness tracker users

   https://doi.org/10.1007/978-3-319-78105-1_27  

   Vitak, J.; Liao, Y.; Kumar, P.; Zimmer, M.; Kritikos, K. (January 2018, Lecture notes in computer science)

   Fitness trackers are an increasingly popular tool for tracking one’s health and physical activity. While research has evaluated the potential benefits of these devices for health and well-being, few studies have empirically evaluated users’ behaviors when sharing personal fitness information (PFI) and the privacy concerns that stem from the collection, aggregation, and sharing of PFI. In this study, we present findings from a survey of Fitbit and Jawbone users (N=361) to understand how concerns about privacy in general and user- generated data in particular affect users’ mental models of PFI privacy, tracking, and sharing. Findings highlight the complex relationship between users’ demographics, sharing behaviors, privacy concerns, and internet skills with how valuable and sensitive they rate their PFI. We conclude with a discussion of opportunities to increase user awareness of privacy and PFI. 

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2. Analysis of Privacy Protections in Fitness Tracking Social Networks -or You can run, but can you hide?

   Hassan, Wajih Ul; Hussain, Saad; Bates, Adam (August 2018, 27th USENIX Security Symposium)

   Mobile fitness tracking apps allow users to track their workouts and share them with friends through online social networks. Although the sharing of personal data is an inherent risk in all social networks, the dangers presented by sharing personal workouts comprised of geospatial and health data may prove especially grave. While fitness apps offer a variety of privacy features, at present it is unclear if these countermeasures are sufficient to thwart a determined attacker, nor is it clear how many of these services’ users are at risk. In this work, we perform a systematic analysis of privacy behaviors and threats in fitness tracking social networks. Collecting a month-long snapshot of public posts of a popular fitness tracking service (21 million posts, 3 million users), we observe that 16.5% of users make use of Endpoint Privacy Zones (EPZs), which conceal fitness activity near user-designated sensitive locations (e.g., home, office). We go on to develop an attack against EPZs that infers users’ protected locations from the remaining available information in public posts, discovering that 95.1% of moderately active users are at risk of having their protected locations extracted by an attacker. Finally, we consider the efficacy of state-of-the-art privacy mechanisms through adapting geo-indistinguishability techniques as well as developing a novel EPZ fuzzing technique. The affected companies have been notified of the discovered vulnerabilities and at the time of publication have incorporated our proposed countermeasures into their production systems. 

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3. Supporting Caring among Intergenerational Family Members through Family Fitness Tracking

   https://doi.org/10.1145/3421937.3422018  

   Li, Qingyang; Caldeira, Clara; Epstein, Daniel A.; Chen, Yunan (May 2020, Proceedings of the 14th EAI International Conference on Pervasive Computing Technologies for Healthcare)

   null (Ed.)

   We present results from a qualitative study involving eight intergenerational families (27 participants) to understand how a family tracking intervention can help support care among intergenerational family members. Our findings show that family members communicate and stay aware of each other's' health through shared fitness data and conversations triggered by fitness sharing. We identified different challenges and preferences among the three age groups in our study: older adults enjoyed family fitness sharing but often encountered various technical challenges, the middle-aged group served as a key person to care for the rest of the family members, and the young generation could not fully engage in fitness sharing due to their busy schedule and privacy concerns. These findings suggest the design of family fitness sharing to account for the age differences in intergenerational families and support the unique needs of family fitness sharing. 

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4. A Study of Users' Privacy Preferences for Data Sharing on Symptoms-Tracking/Health App

   https://doi.org/10.1145/3559613.3563202  

   Asif, Hafiz; Vaidya, Jaideep (November 2022, Proceedings of the 21st Workshop on Privacy in the Electronic Society)

   Symptoms-tracking applications allow crowdsensing of health and location related data from individuals to track the spread and outbreaks of infectious diseases. During the COVID-19 pandemic, for the first time in history, these apps were widely adopted across the world to combat the pandemic. However, due to the sensitive nature of the data collected by these apps, serious privacy concerns were raised and apps were critiqued for their insufficient privacy safeguards. The Covid Nearby project was launched to develop a privacy-focused symptoms-tracking app and to understand the privacy preferences of users in health emergencies. In this work, we draw on the insights from the Covid Nearby users' data, and present an analysis of the significantly varying trends in users' privacy preferences with respect to demographics, attitude towards information sharing, and health concerns, e.g. after being possibly exposed to COVID-19. These results and insights can inform health informatics researchers and policy designers in developing more socially acceptable health apps in the future. 

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5. A recommendation approach for user privacy preferences in the fitness domain

   https://doi.org/10.1007/s11257-019-09246-3  

   Sanchez, Odnan Ref; Torre, Ilaria; He, Yangyang; Knijnenburg, Bart P. (October 2019, User Modeling and User-Adapted Interaction)

   Fitness trackers are undoubtedly gaining in popularity. As fitness-related data are persistently captured, stored, and processed by these devices, the need to ensure users’ privacy is becoming increasingly urgent. In this paper, we apply a data-driven approach to the development of privacy-setting recommendations for fitness devices. We first present a fitness data privacy model that we defined to represent users’ privacy preferences in a way that is unambiguous, compliant with the European Union’s General Data Protection Regulation (GDPR), and able to represent both the user and the third party preferences. Our crowdsourced dataset is collected using current scenarios in the fitness domain and used to identify privacy profiles by applying machine learning techniques. We then examine different personal tracking data and user traits which can potentially drive the recommendation of privacy profiles to the users. Finally, a set of privacy-setting recommendation strategies with different guidance styles are designed based on the resulting profiles. Interestingly, our results show several semantic relationships among users’ traits, characteristics, and attitudes that are useful in providing privacy recommendations. Even though several works exist on privacy preference modeling, this paper makes a contribution in modeling privacy preferences for data sharing and processing in the IoT and fitness domain, with specific attention to GDPR compliance. Moreover, the identification of well-identified clusters of preferences and predictors of such clusters is a relevant contribution for user profiling and for the design of interactive recommendation strategies that aim to balance users’ control over their privacy permissions and the simplicity of setting these permissions. 

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