Opportunistic data sharing allows users to receive real-time, dynamic data directly from peers. These systems not only allow large-scale cooperative sensing but they also empower users to fully control what information is sensed, stored, and shared, enhancing an individual's control over their own potentially private data. While there exist context-aware frameworks that allow individual users to define when and what shared information peers can consume, these approaches have limited expressiveness and do not allow data owners to modulate the granularity of the information released depending on a particular peer or situation. In addition, these frameworks do not consider the consuming peers' privacy, i.e., how much information they have to provide to get access to some desired data. In this paper, we present PADEC, a context-sensitive, privacy-aware framework that allows users to define rich access control rules over their resources and to attach levels of granularity to each rule in order to precisely define who has access to what data when and at what level of detail. Our evaluation shows that PADEC is more expressive than other access control mechanisms and protects the provider's privacy up to 90% more.

Herrera, J.L., Chen, H., Berrocal, J., Murillo, J.M., Julien, C. (2021). Privacy-Aware and Context-Sensitive Access Control for Opportunistic Data Sharing. 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA : IEEE COMPUTER SOC [10.1109/ccgrid51090.2021.00092].

Privacy-Aware and Context-Sensitive Access Control for Opportunistic Data Sharing

Herrera, Juan Luis;
2021

Abstract

Opportunistic data sharing allows users to receive real-time, dynamic data directly from peers. These systems not only allow large-scale cooperative sensing but they also empower users to fully control what information is sensed, stored, and shared, enhancing an individual's control over their own potentially private data. While there exist context-aware frameworks that allow individual users to define when and what shared information peers can consume, these approaches have limited expressiveness and do not allow data owners to modulate the granularity of the information released depending on a particular peer or situation. In addition, these frameworks do not consider the consuming peers' privacy, i.e., how much information they have to provide to get access to some desired data. In this paper, we present PADEC, a context-sensitive, privacy-aware framework that allows users to define rich access control rules over their resources and to attach levels of granularity to each rule in order to precisely define who has access to what data when and at what level of detail. Our evaluation shows that PADEC is more expressive than other access control mechanisms and protects the provider's privacy up to 90% more.
2021
2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid)
762
769
Herrera, J.L., Chen, H., Berrocal, J., Murillo, J.M., Julien, C. (2021). Privacy-Aware and Context-Sensitive Access Control for Opportunistic Data Sharing. 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA : IEEE COMPUTER SOC [10.1109/ccgrid51090.2021.00092].
Herrera, Juan Luis; Chen, Hsiao-Yuan; Berrocal, Javier; Murillo, Juan M.; Julien, Christine
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/959600
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