An Efficient Decentralized Fine-grained Access Control for IoT Ecosystems over NDN

Named Data Networking (NDN) is an emerging Information-Centric Networking (ICN) architecture that offers a fresh and dynamic approach to Internet communication. This architecture is well-suited for the IoT ecosystem due to its efficient data distribution strategies, which provide a secure and scalable data-centric environment. Nonetheless, NDN’s philosophy results in less control over data access. To address this issue, several proposals in the literature tend to utilize encryption schemes. However, most of them fail to offer fine-grained access control for heterogeneous and multi-domain architecture. In addition, several proposals do not provide efficient revocation mechanisms that are suitable for scalable and resource-constrained environments. Moreover, the proposed encryption scheme do not usually accommodate NDN’s and data caching and group communication features. Therefore, to overcome these challenges, we propose in this paper a new decentralized attribute-based access control scheme for IoT ecosystems over NDN. Our solution considers continuous data sharing scenario and ensures fine grained access to data. Furthermore, it enables a fast data access, through a secure key derivation mechanism, and an efficient and direct revocation. The encryption and decryption processes that follows the revocation are optimized in order to reduce the revocation overhead and maintain a fast access to data. Finally, our experimental results demonstrate that our solution outperforms existing ones.

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Publication Details

Published
2024-09-26
DOI
https://doi.org/10.23919/softcom62040.2024.10721767
Citations
1
Primary Topic
IoT and Edge/Fog Computing
Type
article
Field-Weighted Citation Impact
0.35
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article

An Efficient Decentralized Fine-grained Access Control for IoT Ecosystems over NDN

Mohamed Ahmed M. Hail, Antoine Gallais, Youcef Imine, Ferhat Mecerhed et al.
1 citations
IoT and Edge/Fog Computing
0.35
article

An Efficient Decentralized Fine-grained Access Control for IoT Ecosystems over NDN

Mohamed Ahmed M. Hail, Antoine Gallais, Youcef Imine, Ferhat Mecerhed, Stefan Fischer
article en
1 citations

Abstract

Named Data Networking (NDN) is an emerging Information-Centric Networking (ICN) architecture that offers a fresh and dynamic approach to Internet communication. This architecture is well-suited for the IoT ecosystem due to its efficient data distribution strategies, which provide a secure and scalable data-centric environment. Nonetheless, NDN’s philosophy results in less control over data access. To address this issue, several proposals in the literature tend to utilize encryption schemes. However, most of them fail to offer fine-grained access control for heterogeneous and multi-domain architecture. In addition, several proposals do not provide efficient revocation mechanisms that are suitable for scalable and resource-constrained environments. Moreover, the proposed encryption scheme do not usually accommodate NDN’s and data caching and group communication features. Therefore, to overcome these challenges, we propose in this paper a new decentralized attribute-based access control scheme for IoT ecosystems over NDN. Our solution considers continuous data sharing scenario and ensures fine grained access to data. Furthermore, it enables a fast data access, through a secure key derivation mechanism, and an efficient and direct revocation. The encryption and decryption processes that follows the revocation are optimized in order to reduce the revocation overhead and maintain a fast access to data. Finally, our experimental results demonstrate that our solution outperforms existing ones.

Centre National de la Recherche Scientifique (FR), Laboratoire d'Automatique, de Mécanique et d'Informatique Industrielles et Humaines (FR), Université Polytechnique Hauts-de-France (FR)
Openalex Percentile: Top 38%
IoT and Edge/Fog Computing
0.35
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An Efficient Decentralized Fine-grained Access Control for IoT Ecosystems over NDN — Mohamed Ahmed M. Hail, Antoine Gallais, et al. · (2024) | TGRS Research Map | TGRS