A recommendation approach for user privacy preferences in the fitness domain

Sanchez, Odnan Ref; Torre, Ilaria; He, Yangyang; Knijnenburg, Bart P.

doi:10.1007/s11257-019-09246-3

Cited by 31 publications

(20 citation statements)

References 54 publications

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“…A series of studies in the broader context of the Internet of Things built similar user models clustering users' privacy decisions into a number of privacy profiles [13,19,31]. For instance, Bahirat et al [13], developed a set of three "smart" default profiles that captured users' preferences towards sharing data with public IoT systems.…”

Section: A Self-adaptive Approach: Usermentioning

confidence: 99%

The Effectiveness of Adaptation Methods in Improving User Engagement and Privacy Protection on Social Network Sites

Namara

Sloan

Knijnenburg

2021

Proceedings on Privacy Enhancing Technologies

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Research finds that the users of Social Networking Sites (SNSs) often fail to comprehensively engage with the plethora of available privacy features— arguably due to their sheer number and the fact that they are often hidden from sight. As different users are likely interested in engaging with different subsets of privacy features, an SNS could improve privacy management practices by adapting its interface in a way that proactively assists, guides, or prompts users to engage with the subset of privacy features they are most likely to benefit from. Whereas recent work presents algorithmic implementations of such privacy adaptation methods, this study investigates the optimal user interface mechanism to present such adaptations. In particular, we tested three proposed “adaptation methods” (automation, suggestions, highlights) in an online between-subjects user experiment in which 406 participants used a carefully controlled SNS prototype. We systematically evaluate the effect of these adaptation methods on participants’ engagement with the privacy features, their tendency to set stricter settings (protection), and their subjective evaluation of the assigned adaptation method. We find that the automation of privacy features afforded users the most privacy protection, while giving privacy suggestions caused the highest level of engagement with the features and the highest subjective ratings (as long as awkward suggestions are avoided). We discuss the practical implications of these findings in the effectiveness of adaptations improving user awareness of, and engagement with, privacy features on social media.

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Section: A Self-adaptive Approach: Usermentioning

confidence: 99%

The Effectiveness of Adaptation Methods in Improving User Engagement and Privacy Protection on Social Network Sites

Namara

Sloan

Knijnenburg

2021

Proceedings on Privacy Enhancing Technologies

Self Cite

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“…Unfortunately, the sheer number of contextual parameters to consider in this decision will likely substantially increase the complexity of the privacy-setting interfaces of IoT devices. In response, researchers have attempted to reduce the apparent variety of IoT privacy decisions to a small set of concise privacy profiles for users to choose from [90,96,106], thereby reducing the complexity of the privacysetting task.…”

Section: Context-adaptive and User-tailored Privacymentioning

confidence: 99%

Privacy and the Internet of Things

Lipford

Tabassum

Bahirat

et al. 2021

Modern Socio-Technical Perspectives on Privacy

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Using networks of Internet-connected sensors, the Internet of Things (IoT) makes technologies “smart” by enabling automation, personalization, and remote control. At the same time, IoT technologies introduce challenging privacy issues that may frustrate their widespread adoption. This chapter addresses the privacy challenges of IoT technologies from a user-centered perspective and demonstrates these prevalent issues in the domains of wearables (e.g., fitness trackers), household technologies (e.g., smart voice assistants), and devices that exist in the public domain (e.g., security cameras). The chapter ends with a comprehensive list of solutions and guidelines that can help researchers and practitioners introduce usable privacy to the domain of IoT.

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“…Another recent publication showed that it is possible to cluster users into five groups of users using a questionnaire, allowing to assign each of them a privacy policy which is tailored for their respective privacy needs (Lynn Dupree et al 2016). This can also be done for the fitness domain using recommender systems and machine learning (Ref Sanchez et al 2020). Privacy decisions can be predicted by what the authors call cognitive heuristics (Shyam Sundar et al 2020), which are shortcuts that allow a fast decision-making for the user.…”

Section: Related Workmentioning

confidence: 99%

Transferring recommendations through privacy user models across domains

Raber

Krüger

2021

User Model User-Adap Inter

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Although privacy settings are important not only for data privacy, but also to prevent hacking attacks like social engineering that depend on leaked private data, most users do not care about them. Research has tried to help users in setting their privacy settings by using some settings that have already been adapted by the user or individual factors like personality to predict the remaining settings. But in some cases, neither is available. However, the user might have already done privacy settings in another domain, for example, she already adapted the privacy settings on the smartphone, but not on her social network account. In this article, we investigate with the example of four domains (social network posts, location sharing, smartphone app permission settings and data of an intelligent retail store), whether and how precise privacy settings of a domain can be predicted across domains. We performed an exploratory study to examine which privacy settings of the aforementioned domains could be useful, and validated our findings in a validation study. Our results indicate that such an approach works with a prediction precision about 15%–20% better than random and a prediction without input coefficients. We identified clusters of domains that allow model transfer between their members, and discuss which kind of privacy settings (general or context-based) leads to a better prediction accuracy. Based on the results, we would like to conduct user studies to find out whether the prediction precision is perceived by users as a significant improvement over a “one-size-fits-all” solution, where every user is given the same privacy settings.

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A recommendation approach for user privacy preferences in the fitness domain

Cited by 31 publications

References 54 publications

The Effectiveness of Adaptation Methods in Improving User Engagement and Privacy Protection on Social Network Sites

The Effectiveness of Adaptation Methods in Improving User Engagement and Privacy Protection on Social Network Sites

Privacy and the Internet of Things

Transferring recommendations through privacy user models across domains

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