Blockchain-Enhanced Attribute-Based Encryption Architecture With Feasibility Analysis

In today’s digital landscape, data security is critical, particularly in the Internet of Things (IoT), where large volumes of sensitive data are exchanged. Traditional encryption methods like RSA and AES face challenges in balancing security and performance, exposing systems to advanced cyber threats. To address these issues, blockchain technology offers decentralized, tamper-resistant data protection that enhances trust and transparency. Attribute-Based Encryption (ABE) schemes have been developed, often combining asymmetric and symmetric encryption for efficiency and security. However, gaps remain in practical deployment due to underexplored network architectures and limited feasibility simulations. This study proposes an end-to-end security architecture integrating ABE with Linear Secret Sharing Scheme (LSSS) access policies and blockchain-based distributed key management. The system’s feasibility was evaluated using Network Simulator 3 (NS3) within a simulated IoT network. Results demonstrate a lightweight and scalable solution suitable for constrained environments. Numerical simulations showed consensus times as low as 0.25 seconds for key agreement and 0.7 seconds for message consensus, even in resource-constrained settings. For large networks, consensus times reached as low as 0.75 seconds. The system also achieved an average throughput of 0.3 transactions per second in low-resource environments. These outcomes highlight the system’s potential for secure, efficient data transmission in IoT and other distributed systems.

Authors

Institutions

Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3554643
Citations
2
Primary Topic
Cryptography and Data Security
Type
article
Field-Weighted Citation Impact
4.96
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Blockchain-Enhanced Attribute-Based Encryption Architecture With Feasibility Analysis

B. T. Maharaj, M. C. Hlophe, A. Ferrer-Rojas
2 citations
IEEE Access
Cryptography and Data Security
4.96
article

Blockchain-Enhanced Attribute-Based Encryption Architecture With Feasibility Analysis

B. T. Maharaj, M. C. Hlophe, A. Ferrer-Rojas
article en
2 citations

Abstract

In today’s digital landscape, data security is critical, particularly in the Internet of Things (IoT), where large volumes of sensitive data are exchanged. Traditional encryption methods like RSA and AES face challenges in balancing security and performance, exposing systems to advanced cyber threats. To address these issues, blockchain technology offers decentralized, tamper-resistant data protection that enhances trust and transparency. Attribute-Based Encryption (ABE) schemes have been developed, often combining asymmetric and symmetric encryption for efficiency and security. However, gaps remain in practical deployment due to underexplored network architectures and limited feasibility simulations. This study proposes an end-to-end security architecture integrating ABE with Linear Secret Sharing Scheme (LSSS) access policies and blockchain-based distributed key management. The system’s feasibility was evaluated using Network Simulator 3 (NS3) within a simulated IoT network. Results demonstrate a lightweight and scalable solution suitable for constrained environments. Numerical simulations showed consensus times as low as 0.25 seconds for key agreement and 0.7 seconds for message consensus, even in resource-constrained settings. For large networks, consensus times reached as low as 0.75 seconds. The system also achieved an average throughput of 0.3 transactions per second in low-resource environments. These outcomes highlight the system’s potential for secure, efficient data transmission in IoT and other distributed systems.

IEEE AccessVol. 13
University of Pretoria (ZA)
Openalex Percentile: Top 6%
Cryptography and Data Security
4.96
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.