PriviFy: Designing Tangible Interfaces for Configuring IoT Privacy Preferences
<|reference_start|>PriviFy: Designing Tangible Interfaces for Configuring IoT Privacy Preferences: The Internet of Things (IoT) devices, such as smart speakers can collect sensitive user data, necessitating the need for users to manage their privacy preferences. However, configuring these preferences presents users with multiple challenges. Existing privacy controls often lack transparency, are hard to understand, and do not provide meaningful choices. On top of that, users struggle to locate privacy settings due to multiple menus or confusing labeling, which discourages them from using these controls. We introduce PriviFy (Privacy Simplify-er), a novel and user-friendly tangible interface that can simplify the configuration of smart devices privacy settings. PriviFy is designed to propose an enhancement to existing hardware by integrating additional features that improve privacy management. We envision that positive feedback and user experiences from our study will inspire consumer product developers and smart device manufacturers to incorporate the useful design elements we have identified. Using fidelity prototyping, we iteratively designed PriviFy prototype with 20 participants to include interactive features such as knobs, buttons, lights, and notifications that allow users to configure their data privacy preferences and receive confirmation of their choices. We further evaluated PriviFy high-fidelity prototype with 20 more participants. Our results show that PriviFy helps simplify the complexity of privacy preferences configuration with a significant usability score at p < .05 (P = 0.000000017, t = -8.8639). PriviFy successfully met users privacy needs and enabled them to regain control over their data. We conclude by recommending the importance of designing specific privacy configuration options.<|reference_end|>
@article{muhander2024privify:,
    title={PriviFy: Designing Tangible Interfaces for Configuring IoT Privacy
  Preferences},
    author={Bayan Al Muhander, Omer Rana, Charith Perera},
    journal={arXiv preprint arXiv:2406.05459},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05459},
    primaryClass={cs.CR cs.ET}
}