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User testing is often used to inform the development of user interfaces (UIs). But what if an interface needs to be developed for a system that does not yet exist? In that case, existing datasets can provide valuable input for UI development. We apply a data-driven approach to the development of a privacy-setting interface for Internet-of-Things (IoT) devices. Applying machine learning techniques to an existing dataset of users' sharing preferences in IoT scenarios, we develop a set of "smart" default profiles. Our resulting interface asks users to choose among these profiles, which capture their preferences with an accuracy of 82%---a 14% improvement over a naive default setting and a 12% improvement over a single smart default setting for all users.
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A recommendation approach for user privacy preferences in the fitness domain
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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- Award ID(s):
- 1640664
- PAR ID:
- 10127962
- Date Published:
- Journal Name:
- User Modeling and User-Adapted Interaction
- ISSN:
- 0924-1868
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s11257-019-09246-3
