Edge computing is an emerging computational paradigm where edge nodes provide services to users performing computation-in-place. It allows faster computation, better support for real-time applications and can simplify the implementation of security measures. Concerning individual privacy, a relevant requirement is giving users more control over how their data is used. It is important to check compliance between user privacy preferences and provider privacy policy. However, the typical edge computing application scenario is dynamic, with users in constant motion, changing their location and time at which they connect to the edge node as well as the situation under which they connect. This makes the common notion of privacy preference compliance insufficient. To address this issue, we provide a framework for allowing users to define their privacy preferences according to a rich set of contextual features. We also demonstrate the feasibility of our solution through realistic and synthetic tests.
ConPrEF: A Context-based Privacy Enforcement Framework for Edge Computing
Sirigu, GiorgiaConceptualization
;Carminati, BarbaraSupervision
;Ferrari, ElenaSupervision
2023-01-01
Abstract
Edge computing is an emerging computational paradigm where edge nodes provide services to users performing computation-in-place. It allows faster computation, better support for real-time applications and can simplify the implementation of security measures. Concerning individual privacy, a relevant requirement is giving users more control over how their data is used. It is important to check compliance between user privacy preferences and provider privacy policy. However, the typical edge computing application scenario is dynamic, with users in constant motion, changing their location and time at which they connect to the edge node as well as the situation under which they connect. This makes the common notion of privacy preference compliance insufficient. To address this issue, we provide a framework for allowing users to define their privacy preferences according to a rich set of contextual features. We also demonstrate the feasibility of our solution through realistic and synthetic tests.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.