A rewarding framework for crowdsourcing to increase privacy awareness
(2021)
Data and Applications Security and Privacy XXXV.
In Lecture Notes in Computer Science
12840.
p.259-277
- Author
- Ioannis Chrysakis (UGent) , Giorgos Flouris, Maria Makridaki, Theodore Patkos, Yannis Roussakis, Georgios Samaritakis, Nikoleta Tsampanaki, Elias Tzortzakakis, Elisjana Ymeralli, Tom Seymoens, Anastasia Dimou (UGent) and Ruben Verborgh (UGent)
- Organization
- 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.
- Keywords
- FUTURE, Data privacy, Privacy awareness, Privacy policies, GDPR, Crowdsourcing, Rewarding, Collective intelligence
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8720546
- MLA
- Chrysakis, Ioannis, et al. “A Rewarding Framework for Crowdsourcing to Increase Privacy Awareness.” Data and Applications Security and Privacy XXXV, edited by K Barker and K Ghazinour, vol. 12840, Springer, 2021, pp. 259–77, doi:10.1007/978-3-030-81242-3_15.
- APA
- Chrysakis, I., Flouris, G., Makridaki, M., Patkos, T., Roussakis, Y., Samaritakis, G., … Verborgh, R. (2021). A rewarding framework for crowdsourcing to increase privacy awareness. In K. Barker & K. Ghazinour (Eds.), Data and Applications Security and Privacy XXXV (Vol. 12840, pp. 259–277). https://doi.org/10.1007/978-3-030-81242-3_15
- Chicago author-date
- Chrysakis, Ioannis, Giorgos Flouris, Maria Makridaki, Theodore Patkos, Yannis Roussakis, Georgios Samaritakis, Nikoleta Tsampanaki, et al. 2021. “A Rewarding Framework for Crowdsourcing to Increase Privacy Awareness.” In Data and Applications Security and Privacy XXXV, edited by K Barker and K Ghazinour, 12840:259–77. Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-030-81242-3_15.
- Chicago author-date (all authors)
- Chrysakis, Ioannis, Giorgos Flouris, Maria Makridaki, Theodore Patkos, Yannis Roussakis, Georgios Samaritakis, Nikoleta Tsampanaki, Elias Tzortzakakis, Elisjana Ymeralli, Tom Seymoens, Anastasia Dimou, and Ruben Verborgh. 2021. “A Rewarding Framework for Crowdsourcing to Increase Privacy Awareness.” In Data and Applications Security and Privacy XXXV, ed by. K Barker and K Ghazinour, 12840:259–277. Cham, Switzerland: Springer. doi:10.1007/978-3-030-81242-3_15.
- Vancouver
- 1.Chrysakis I, Flouris G, Makridaki M, Patkos T, Roussakis Y, Samaritakis G, et al. A rewarding framework for crowdsourcing to increase privacy awareness. In: Barker K, Ghazinour K, editors. Data and Applications Security and Privacy XXXV. Cham, Switzerland: Springer; 2021. p. 259–77.
- IEEE
- [1]I. Chrysakis et al., “A rewarding framework for crowdsourcing to increase privacy awareness,” in Data and Applications Security and Privacy XXXV, 2021, vol. 12840, pp. 259–277.
@inproceedings{8720546,
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.}},
author = {{Chrysakis, Ioannis and Flouris, Giorgos and Makridaki, Maria and Patkos, Theodore and Roussakis, Yannis and Samaritakis, Georgios and Tsampanaki, Nikoleta and Tzortzakakis, Elias and Ymeralli, Elisjana and Seymoens, Tom and Dimou, Anastasia and Verborgh, Ruben}},
booktitle = {{Data and Applications Security and Privacy XXXV}},
editor = {{Barker, K and Ghazinour, K}},
isbn = {{9783030812416}},
issn = {{0302-9743}},
keywords = {{FUTURE,Data privacy,Privacy awareness,Privacy policies,GDPR,Crowdsourcing,Rewarding,Collective intelligence}},
language = {{eng}},
pages = {{259--277}},
publisher = {{Springer}},
title = {{A rewarding framework for crowdsourcing to increase privacy awareness}},
url = {{http://doi.org/10.1007/978-3-030-81242-3_15}},
volume = {{12840}},
year = {{2021}},
}
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