Are Those Steps Worth Your Privacy?: Fitness-Tracker Users' Perceptions of Privacy and Utility
Lev Velykoivanenko, Kavous Salehzadeh Niksirat, Noé Zufferey, Mathias Humbert, Kévin Huguenin, Mauro Cherubini
摘要
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.
CCS Concepts: • Security and privacy → Usability in security and privacy; Privacy protections; • Human-centered computing → Empirical studies in ubiquitous and mobile computing.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper8
- Exploring Privacy Practices of Female mHealth Apps in a Post-Roe WorldLisa Mekioussa Malki, Ina Kaleva, Dilisha Patel, Mark Warner 等CHI 2024 · 被引用 44 次
- Understanding Adolescents' Perceptions of Benefits and Risks in Health AI Technologies through Design FictionJamie Lee, Kyuha Jung, Erin Gregg Newman, Emilie Chow 等CHI 2025 · 被引用 17 次
- SeRaNDiP: Leveraging Inherent Sensor Random Noise for Differential Privacy Preservation in Wearable Community Sensing ApplicationsAyanga Imesha Kumari Kalupahana, Ananta Narayanan Balaji, Xiaokui Xiao, Li-Shiuan PehUbiComp 2023 · 被引用 9 次
- Designing a Data-Driven Survey System: Leveraging Participants' Online Data to Personalize SurveysLev Velykoivanenko, Kavous Salehzadeh Niksirat, Stefan Teofanovic, Bertil Chapuis 等CHI 2024 · 被引用 5 次
- The Role of Privacy Guarantees in Voluntary Donation of Private Health Data for Altruistic GoalsRuizhe Wang, Roberta De Viti, Aarushi Dubey, Elissa M. RedmilesNDSS 2026
它引用的顶会 Paper4
- Obstacles to the Adoption of Secure Communication ToolsRuba Abu-Salma, M. Angela Sasse, Joseph Bonneau, Anastasia Danilova 等S&P 2017 · 被引用 170 次
- "If HTTPS Were Secure, I Wouldn't Need 2FA" - End User and Administrator Mental Models of HTTPSKatharina Krombholz, Karoline Busse, Katharina Pfeffer, Matthew Smith 等S&P 2019 · 被引用 105 次
- Understanding Fitness Tracker Users' Security and Privacy Knowledge, Attitudes and BehavioursSandra Gabriele, Sonia ChiassonCHI 2020 · 被引用 61 次
- Understanding Users' Perception Towards Automated Personality Detection with Group-specific Behavioral DataSeoyoung Kim, Arti Thakur, Juho KimCHI 2020 · 被引用 10 次
相关 Paper
- Users Can Deduce Sensitive Locations Protected by Privacy Zones on Fitness Tracking AppsJaron Mink, Amanda Rose Yuile, Uma Pal, Adam J. Aviv 等CHI 2022 · 被引用 11 次
- "I'm not as afraid as a woman might be about sharing my exact location: " On the Intersection of Identity and Privacy Concerns in Fitness TrackingYeeun Jo, Mahnoor Jameel, Camille Cobb, Adam BatesCHI 2025 · 被引用 1 次
- From Options to Action: Evaluating Adoption of Privacy Features in Fitness - Tracking PlatformsPantelina Ioannou, Angeliki Aktypi, Elias AthanasopoulosCHI 2026 · 被引用 1 次
- Designing Reflective Derived Metrics for Fitness TrackersMarit Bentvelzen, Jasmin Niess, Pawel W. WozniakUbiComp 2023 · 被引用 54 次
- Bolder is Better: Raising User Awareness through Salient and Concise Privacy NoticesNico Ebert, Kurt Alexander Ackermann, Björn SchepplerCHI 2021 · 被引用 4 次
