Enhancing Privacy in Internet of Things using Software Defined Network

Zangaraki, Shahrbanoo; Erfani, S. Hossein; Sahafi, Amir

doi:10.22061/jecei.2025.11621.821

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	Enhancing Privacy in Internet of Things using Software Defined Network
Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 13، دوره 14، شماره 1، مهر 2026، صفحه 163-180 اصل مقاله (1.17 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2025.11621.821
نویسندگان
Shahrbanoo Zangaraki؛ S. Hossein Erfani^* ؛ Amir Sahafi
Department of Computer Engineering, ST.C., Islamic Azad University, Tehran, Iran
تاریخ دریافت: 19 خرداد 1404، تاریخ بازنگری: 16 شهریور 1404، تاریخ پذیرش: 27 شهریور 1404
چکیده
Background and Objectives: The Internet of Things (IoT) serves as a fundamental communication model, enabling objects to deliver data and services to users. With the rapid expansion of IoT, ensuring privacy and preventing the disclosure of sensitive data during message exchanges between objects has become increasingly challenging. This paper presents an attribute-based framework designed to enhance privacy protection in IoT environments by leveraging software-defined networking (SDN) technology. Methods: By leveraging the SDN and the Attribute-Based Privacy Preserving (ABPP) model, our proposed framework employs an advanced algorithm to enhance privacy for client requests accessing IoT services. It focuses on protecting sensitive information during message transmission by implementing techniques for anonymity, unlinkability, and untraceability, tailored to the sensitivity level of each message. To further enhance message privacy within the IoT network, our framework incorporates IP aliasing, dynamic channel switching, and payload encryption. Results: Our proposed framework significantly enhances privacy protection in IoT networks by dynamically applying anonymity and concealment techniques tailored to the sensitivity of CoAP messages. Simulation results using CloudSimSDN confirm the framework's effectiveness in safeguarding sensitive information while maintaining optimal communication performance. Employing three privacy-preserving techniques results in an average CPU utilization that is 0.14 units higher compared to using a single technique. We provide a security evaluation that includes formal verification techniques and informal analysis, and show that the proposed framework is secure against anonymity and MITM attacks, replay attacks, Sybil, and IP spoofing. Conclusion: In this paper, we present a four-layer SDN-based framework designed to enhance privacy in IoT networks through the use of the Attribute-Based Privacy Preserving (ABPP) model. The framework employs IP aliasing, dynamic routing, and content encryption techniques tailored to the sensitivity of CoAP messages to ensure data protection. Our implementation and experiments conducted with CloudSimSDN validate the framework's effectiveness in safeguarding sensitive information.
کلیدواژه‌ها
IoT؛ SDN؛ Privacy Preserving

مراجع
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Enhancing Privacy in Internet of Things using Software Defined Network