A Rewarding Framework for Crowdsourcing to Increase Privacy Awareness
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 content 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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Published in 2021 in Proceedings of the 35th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy.
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Cite this article easily using its BibTeX entry:
@inproceedings{chrysakis_dbsec_2021,
author = {Chrysakis, Ioannis and Flouris, Giorgos and Makridaki, Maria and Patkos, Theodore and Roussakis, Yannis and Samaritakis, Georgios and Tsabanaki, Nikoleta and Tzortzakakis, Elias and Ymeralli, Elisjana and Seymoens, Tom and Dimou, Anastasia and Verborgh, Ruben},
title = {A Rewarding Framework for Crowdsourcing to Increase Privacy Awareness},
booktitle = {Proceedings of the 35th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy},
editor = {Barker, Ken and Ghazinour, Kambiz},
year = 2021,
pages = {259--277},
series = {Lecture Notes in Computer Science},
volume = 12840,
publisher = {Springer},
url = {https://link.springer.com/chapter/10.1007/978-3-030-81242-3_15},
doi = {10.1007/978-3-030-81242-3_15},
}
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- ACM
- Ioannis Chrysakis, Giorgos Flouris, Maria Makridaki, Theodore Patkos, Yannis Roussakis, Georgios Samaritakis, Nikoleta Tsabanaki, Elias Tzortzakakis, Elisjana Ymeralli, Tom Seymoens, Anastasia Dimou, and Ruben Verborgh. 2021. A Rewarding Framework for Crowdsourcing to Increase Privacy Awareness. In Proceedings of the 35th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy (Lecture Notes in Computer Science), Springer, 259–277.
- APA
- Chrysakis, I., Flouris, G., Makridaki, M., Patkos, T., Roussakis, Y., Samaritakis, G., Tsabanaki, N., Tzortzakakis, E., Ymeralli, E., Seymoens, T., Dimou, A., & Verborgh, R. (2021). A Rewarding Framework for Crowdsourcing to Increase Privacy Awareness. In K. Barker & K. Ghazinour (Eds.), Proceedings of the 35th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy (Vol. 12840, pp. 259–277). Springer.
- IEEE
- I. Chrysakis et al., “A Rewarding Framework for Crowdsourcing to Increase Privacy Awareness,” in Proceedings of the 35th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy, 2021, vol. 12840, pp. 259–277.
- LNCS
- Chrysakis, I., Flouris, G., Makridaki, M., Patkos, T., Roussakis, Y., Samaritakis, G., Tsabanaki, N., Tzortzakakis, E., Ymeralli, E., Seymoens, T., Dimou, A., Verborgh, R.: A Rewarding Framework for Crowdsourcing to Increase Privacy Awareness. In: Barker, K. and Ghazinour, K. (eds.) Proceedings of the 35th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy. pp. 259–277. Springer (2021).
- MLA
- Chrysakis, Ioannis, et al. “A Rewarding Framework for Crowdsourcing to Increase Privacy Awareness.” Proceedings of the 35th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy, edited by Ken Barker and Kambiz Ghazinour, vol. 12840, Springer, 2021, pp. 259–77.
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