OBPP: An ontology-based framework for privacy-preserving in IoT-based smart city
Introduction
IoT is a system of all types of connected objects, animals, or people, that are equipped with unique identifiers such as IPs [1]. They can transfer their generated data over a network without demanding human-to-human or human-to-computer interaction to provide high-level e-services by information gathering and processing [2]. Not only devices should share their data with others, but also services should be interfaced to other computerized things such as other information sources [3].
One application of IoT-enabled devices is the smart city that has seen significant advancement nowadays, a city that is running smartly without any human intervention. The smart city employs IoT with the aim of:
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Improving different aspects of its services that are important to its economic strength, security, safety, environmental impression, and quality of life.
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Responding to the city community’s changing needs flexibly.
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Collaborating with other communities if needed.
Moreover, based on the predictions, billions of devices will collaborate by 2020 [4]. Due to the abundance of devices that are collaborating and producing data over time, we are facing an era named “Big Data”. One of its main characteristics is the large volume of data [5]. As a result, three main challenges come up when dealing with the massive amount of data generated from various devices:
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Heterogeneity: IoT devices generate data in smart city applications. They are most probably stemmed from diverse vendors with different interests, causing inconsistency and diversity among devices. Even devices created from the same vendor can cause this issue because each one may provide different data formats [6]. The main question here is how to overcome the heterogeneous IoT devices as a critical issue in smart cities.
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High-level services: In general, an increase in the amount of data results in more opportunity to provide high-level services. Thus, we should leverage this opportunity and propose solutions to enhance the quality of provided services. In a smart city domain, high-level services refer to the city management services such as managing wastes automatically, controlling traffic jams, and finding abnormal conditions. Here, we want to propose a solution to answer a question about how to support high-level services in smart cities.
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Privacy-preserving: IoT devices may sense private or sensitive information. The sensitive data can be the place of a crowd, the number of alive people in a smart building, etc. They should not be revealed to unrelated and unwanted parties, as publishing them without any further processing may lead to system vulnerabilities. This practice is called privacy-preserving [7], [8]. One of the biggest concerns, hindering users from trusting the IoT-based smart city and from involving in, is privacy-preserving that must be addressed well [9], [10], [11]. In this paper, we present a new solution to answer a question about how to preserve the privacy of IoT devices clients in smart cities.
Fig. 1 depicts a system overview of traditional solutions.
As Fig. 1 illustrates, all IoT devices apply one common privacy-preserving rule in traditional solutions. In other words, the entire system uses one privacy rule. After applying, they send their data to the Cloud Computing space, a paradigm that enhances IoT capabilities, for further analysis [12], [13], [14].
Through observation, we discover that existing solutions are unable to cover all of the mentioned challenges at the same time. To best of the authors’ knowledge, only two approaches have been developed to solve the two of the mentioned challenges simultaneously [15], [16]. The rests focus on just one issue. Even those related works which only try to address the privacy-preserving issue in IoT-based environments have imperfections and cannot be applied to the highly-dynamic smart city environments. In fact, they do not consider the context of the flowing data based on the current condition and context-awareness [17], [18]. In highly-dynamic IoT-based environments such as smart cities, we have to make high-level decisions; context-awareness of the solutions is vital. It means that the solutions should be able to make decisions based on the situation of the environment and the flowing data.
Furthermore, they apply one static privacy rule and one common privacy rule for the entire system. Using one static privacy rule to the whole system causes unacceptable privacy-preserving status. This is because of the possible collaboration among unwanted activities that are trying to find sensitive data. Moreover, if the system is penetrated once, it will be easy for other malicious activities to find sensitive data.
On the other side, better performances can be achieved not only through proposing an efficient algorithm but also by storing and representing knowledge more effectively. Several knowledge models exist to represent knowledge in efficient manners. Some of these techniques are eXtensible Markup Language (XML) [19], Resource Description Framework (RDF) [20], and ontology [21]. Each of these models has its advantages and disadvantages. The ontology data model is considered to be the most efficient model because it not only addresses the heterogeneity issue but also brings context-awareness to the system.
Here, we propose an Ontology-based Privacy-Preserving (OBPP) framework to address the mentioned challenges: privacy-preserving of IoT devices, providing high-level services, and addressing the heterogeneity among various devices at the same time. Our approach stems from bringing context-awareness and dynamism to the solution. Moreover, extensive simulations show its affordability IoT applications from the overload point of view and its robustness against malicious activities. Therefore, it can prevent unintentional disclosure of sensitive data.
As a consequence, it can be widely used in the smart city domain while people feel relaxed that dealing with IoT devices will not disclose their data. It consists of three modules: (1) “Ontology” that consists of an ontology to address the heterogeneity issue among various devices while storing private information and bringing context-awareness, (2) “Reasoning Engine” module that leverages stored information in the “ontology” to enhance quality of provided services and making high-level decisions, and (3) “OBPP Procedure” that preserves the privacy of devices with the help of ontology information by managing privacy rules of devices in a dynamic manner. Note that the privacy-preserving rules of devices are changing over time.
This paper makes the following five-fold contributions:
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We define a framework for addressing privacy-preserving, heterogeneity, and high-level service issues concurrently in the smart city environment, i.e., “OBPP”.
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To generate “OBPP”, we define a novel “Ontology” rather than existing ontologies so that it keeps information of the IoT and alarm devices, and their related privacy information. These devices are collecting sensitive data. Therefore, the IoT devices will have a common understanding of the structure of devices’ knowledge, and it can address the heterogeneity issue.
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To generate “OBPP” on top of “Ontology”, we propose a “Reasoning Engine” to provide high-level services through finding abnormal conditions in the smart city environment.
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Also, we devise an “OBPP Procedure” developed to manage privacy rules of devices such that privacy rules of IoT devices in the smart city change frequently.
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Finally, we validate “OBPP” through extensive simulations. “OBPP” achieves superior performance in terms of addressing the three mentioned issues while providing affordability to many IoT devices and robustness against disclosing sensitive data to unwanted parties.
This paper is organized as follows. Section 2 reviews related work. Section 3 presents the system model and ecology. Section 4 focuses on OBPP development and its components. Performance evaluation of OBPP is described in Section 5. Discussion about OBPP performance is depicted in Section 6. Finally, Section 7 concludes the paper and also presents possible future trends for having a more efficient smart city.
Section snippets
Related work
In the network world, mainly two types of solutions exist, network-centric and context-centric. Network-centric solutions refer to the solutions where decisions are made based on the network structure information such as their topology. By comparison, decisions are made based on the content of the flowing data in context-centric solutions [22].
Regretfully, most of the related work is network-centric; they only pay attention to the network configurations that cause making the decisions puzzling.
System model
In this section, we describe the system model and its components. Ecology of our scenario consists of three parts: (1) IoT-based smart city devices, (2) OBPP, and (3) Cloud space. An overview of the system in schematic form is shown in Fig. 2.
Each part is described as the following:
OBPP modules
In this section, we first pay attention to the input data that is used to design our scenario. Then, OBPP components are described comprehensively. This section provides a clear answer to the following questions:
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How to overcome the heterogeneous IoT devices as a critical issue in smart cities?
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How to support the high-level services in smart cities?
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How to preserve the privacy of IoT devices clients in the smart cities
Performance evaluation
In this section, we focus on the performance evaluation of OBPP from different aspects and compare it to a case where devices apply one static privacy rule. We simulate 12 IoT devices in the smart city where OBPP manages their privacy behaviors. There, OBPP can find abnormal conditions and address the heterogeneity among various IoT devices. To the best of authors’ knowledge, the last two have a more significant impact among possible assessment metrics. Therefore, we calculate the amount of
Discussion
We proposed a framework, OBPP, to address three significant challenges for IoT-based smart city environments. (1) Heterogeneity issue: comfortable interactions among heterogeneous devices in a systematic way. This is achieved through its “Ontology”. (2) Quality service issue: providing high-level e-services in a smart city space such as finding abnormal conditions. It is achieved through OBPP “Reasoning Engine”. And, (3) Privacy-preserving issue: the issue of keeping generated sensitive data
Conclusion and future work
In this paper, we proposed a framework for the IoT-based smart city environment that addressed three significant challenges: heterogeneity, quality services, and privacy-preserving. First, we proposed an ontology that keeps properties information of devices along with their private information. Besides, we proposed a reasoning engine to provide high-level e-services by finding abnormal conditions, and a privacy-preserving module that preserves the privacy of IoT devices by frequently changing
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
This work was financially supported by the Government of the Russian Federation through the ITMO Fellowship and Professorship Program.
Mehdi Gheisari is a Ph.D. Candidate at Guangzhou University. He is currently doing research on Privacy-preserving in IoT. Previously, he was working on WSN. Furthermore, he has published several papers in several domains with his colleagues in highly ranked journals (Computers and Security, Computer and Electrical Engineering, Wireless Network, IEEE access, ijcs, ETT) and in several ranked conferences. His profile can be accessed via: //scholar.google.com.sg/citations?user=tmWQt9UAAAAJ%26hl=en
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Mehdi Gheisari is a Ph.D. Candidate at Guangzhou University. He is currently doing research on Privacy-preserving in IoT. Previously, he was working on WSN. Furthermore, he has published several papers in several domains with his colleagues in highly ranked journals (Computers and Security, Computer and Electrical Engineering, Wireless Network, IEEE access, ijcs, ETT) and in several ranked conferences. His profile can be accessed via: https://scholar.google.com.sg/citations?user=tmWQt9UAAAAJ&hl=en