IoT devices generate data over time, which is going to be shared with other parties to provide high-level services. Smart City is one of its applications which aims to manage cities automatically. Because of the large number of devices, three critical challenges come up: heterogeneity, privacy-preserving of generated data, and providing high-level services. The existing solutions cannot even solve two of the mentioned challenges simultaneously. In this paper, we propose a three-module framework, named “Ontology-Based Privacy-Preserving” (OBPP) to address these issues. The first module includes an ontology, a data storage model, to address the heterogeneity issue while keeping the privacy information of IoT devices. The second one contains semantic reasoning rules to find abnormal patterns while addressing the quality of provided services. The third module provides a privacy rules manager to address the privacy-preserving challenges of IoT devices achieved by dynamically changing privacy behaviors of the devices. Extensive simulations on a synthetic smart city dataset demonstrate the superior performance of our approach compared to the existing solutions while providing affordability and robustness against information leakages. Thus, it can be widely applied to smart cities.

OBPP: An ontology-based framework for privacy-preserving in IoT-based smart city

Castiglione A.
2021-01-01

Abstract

IoT devices generate data over time, which is going to be shared with other parties to provide high-level services. Smart City is one of its applications which aims to manage cities automatically. Because of the large number of devices, three critical challenges come up: heterogeneity, privacy-preserving of generated data, and providing high-level services. The existing solutions cannot even solve two of the mentioned challenges simultaneously. In this paper, we propose a three-module framework, named “Ontology-Based Privacy-Preserving” (OBPP) to address these issues. The first module includes an ontology, a data storage model, to address the heterogeneity issue while keeping the privacy information of IoT devices. The second one contains semantic reasoning rules to find abnormal patterns while addressing the quality of provided services. The third module provides a privacy rules manager to address the privacy-preserving challenges of IoT devices achieved by dynamically changing privacy behaviors of the devices. Extensive simulations on a synthetic smart city dataset demonstrate the superior performance of our approach compared to the existing solutions while providing affordability and robustness against information leakages. Thus, it can be widely applied to smart cities.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4810930
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