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.

物联网设备会随着时间的推移生成数据，这些数据将与其他方共享以提供高级服务。智能城市是其旨在自动管理城市的应用程序之一。由于设备数量众多，因此面临三个关键挑战：异构性，生成数据的隐私保护以及提供高级服务。现有的解决方案甚至不能同时解决上述两个挑战。在本文中，我们提出了一个名为“基于本体的隐私保护”（OBPP）的三模块框架来解决这些问题。第一个模块包括一个本体，一个数据存储模型，用于解决异构性问题，同时保留物联网设备的隐私信息。第二个包含语义推理规则，以查找异常模式，同时解决所提供服务的质量。第三个模块提供了隐私规则管理器，以解决通过动态更改设备的隐私行为而实现的IoT设备的隐私保护挑战。在合成智能城市数据集上进行的大量仿真表明，与现有解决方案相比，我们的方法具有出色的性能，同时提供了可承受性和针对信息泄漏的鲁棒性。因此，它可以广泛地应用于智慧城市。