Ensuring Privacy and Integrity in IoT Supply Chains through Blockchain and Homomorphic Encryption
Din, Ikram Ud Almogren, Ahmad Han, Zhu Guizani, Mohsen
Din, Ikram Ud
Almogren, Ahmad
Han, Zhu
Guizani, Mohsen
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Department
Machine Learning
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Type
Journal article
Date
2025
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Language
English
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Abstract
Ensuring data security and privacy in Internet of Things (IoT) is increasingly critical due to the growing interconnectedness of devices and the sensitivity of the data they handle. This paper presents a novel approach to enhancing data security in IoT through the integration of homomorphic encryption and blockchain technology. We conduct simulations using the Kaggle Smart Home Dataset to evaluate the effectiveness of our proposed methodology on smart home devices and wearable technology. Our approach not only secures data transmission but also guarantees data integrity and privacy through decentralized verification and secure aggregation techniques. Specifically, our evaluation demonstrates an encrypted data transmission rate exceeding 99.5%, a complete absence of unauthorized access instances in the simulated environment, and a verified data integrity rate of over 99.8%. Additionally, our method supports real-time processing and scalability, making it suitable for various IoT applications, including smart contract applications in IoT for privacy and security in supply chain transactions. The study highlights the robustness of combining homomorphic encryption and blockchain to protect sensitive data throughout its lifecycle.
Citation
I. U. Din, A. Almogren, Z. Han, and M. Guizani, “Ensuring Privacy and Integrity in IoT Supply Chains through Blockchain and Homomorphic Encryption,” IEEE Internet Things J, pp. 1–1, 2025, doi: 10.1109/JIOT.2025.3558794.
Source
IEEE Internet of Things Journal
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Keywords
Homomorphic Encryption, Blockchain, Data Security, Consumer Electronics, Privacy Preservation
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Publisher
IEEE