Abstract
Digital applications typically describe their privacy policy in lengthy and vague documents (called PrPs), but these are rarely read by users, who remain unaware of privacy risks associated with the use of these digital applications. Thus, users need to become more aware of digital applications’ policies and, thus, more confident about their choices. To raise privacy awareness, we implemented the CAP-A portal, a crowdsourcing platform which aggregates knowledge as extracted from PrP documents and motivates users in performing privacy-related tasks. The Rewarding Framework is one of the most critical components of the platform. It enhances user motivation and engagement by combining features from existing successful rewarding theories. In this work, we describe this Rewarding Framework, and show how it supports users to increase their privacy knowledge level by engaging them to perform privacy-related tasks, such as annotating PrP documents in a crowdsourcing environment. The proposed Rewarding Framework was validated by pilots ran in the frame of the European project CAP-A and by a user evaluation focused on its impact in terms of engagement and raising privacy awareness. The results show that the Rewarding Framework improves engagement and motivation, and increases users’ privacy awareness.
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Acknowledgement
This work has been supported by the CAP-A project which has received funding from the European Union’s Horizon 2020 research and innovation programme under the NGI_TRUST grant agreement no 825618. The described research activities were also funded by Ghent University, imec, Flanders Innovation & Entrepreneurship (VLAIO). Ruben Verborgh is a postdoctoral fellow of the Research Foundation – Flanders (FWO).
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Appendix: RF Implementation Details
Appendix: RF Implementation Details
We present below details regarding the rules that we applied for task levels, tiers and for the introduced badges in the RF implementation.
Badges
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Social/Buddy: users who invited more than five friends to join the system or to accomplish a specific task within a month;
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Super Star: users who completed at least ten tasks in a month;
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On Fire: users who completed more than three tasks in the last week;
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Ambassador: users with high expertise on various tasks or on privacy issues; ambassadors are invited/suggested by the crowd or by other ambassadors.
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Inactive: users who joined the system but did not start/complete any task;
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Sleepy: users who completed at least one task but did not start a new one for the last three months.
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Chrysakis, I. et al. (2021). A Rewarding Framework for Crowdsourcing to Increase Privacy Awareness. In: Barker, K., Ghazinour, K. (eds) Data and Applications Security and Privacy XXXV. DBSec 2021. Lecture Notes in Computer Science(), vol 12840. Springer, Cham. https://doi.org/10.1007/978-3-030-81242-3_15
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DOI: https://doi.org/10.1007/978-3-030-81242-3_15
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