Context-aware Semantic Reasoning Approach For Dynamic Privacy Risk Inference

Privacy Risk Reasoner

Approach Overview

With the rapid expansion of smart cyber–physical systems and environments, users become more and more concerned about their privacy, and ask for more involvement in the protection of their data. However, users may not be necessarily aware of the direct and indirect privacy risks they take to properly protect their privacy. Consequently, we propose a context-aware semantic reasoning approach, capable of providing users with a dynamic overview of the privacy risks taken as their context evolves. To do so, the system continuously models, according to the proposed uCSN Ontology, the context information collected about the user and his surroundings. In parallel, it performs continuous reasoning over modeled information, while relying on the set of defined/imported privacy rules, in order to dynamically infer the privacy risks involved in the relevant context. To validate our approach, we developed a Java-based prototype based on semantic web tools such as OWL API, SWRL API and the inference engine Pellet. The prototype shows how the system performs dynamically to infer the risks involved, and how it can monitor in real time the evolution of these risks according to the evolution of the user-context. We evaluated the performance of our approach by considering several use cases. Results show that the proposed solution provides scalability and low computational and storage complexity. Also, it handles reasoning in real-time, which makes it able to support the user in various contexts, including ephemeral ones (i.e., contexts with small timescales).

Research Areas

  • Privacy Engineering
  • Privacy Risk
  • Semantic Reasoning
  • Context-aware Computing
  • Internet of Things

PhD Student

Karam Bou Chaaya

Project Members

  • Richard Chbeir
  • Philippe Arnould
  • Mahmoud Barhamgi
  • Djamal Benslimane

Privacy Risk Reasoner Prototype