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Next-generation security for big data analytics in healthcare IoT using hybrid cryptographic techniques

Abdullah Alharbi et al.

Health Informatics Journal2026https://doi.org/10.1177/14604582261417493article
ABDC C
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0.50

What the paper says

Big Data in Internet of Healthcare Things (IoHT) environments includes large volumes of structured and unstructured clinical information. The Hadoop Distributed File System (HDFS) is widely used for its scalability and ability to run on commodity hardware. However, it offers limited native encryption, leaving data vulnerable to security risks. Although several encryption techniques exist, traditional algorithms still face performance and security limitations with large-scale medical datasets. Therefore, this study introduces a hybrid encryption framework designed to enhance security in IoHT environments that process large-scale medical Big Data. The framework combines Attribute-Based Encryption (ABE) with the Blowfish cipher to secure data generated by heterogeneous medical devices across the IoHT infrastructure. The proposed approach is benchmarked against established hybrid schemes-CP-ABE + HE, HE + BF, and CP-ABE + AES-to provide a comparative assessment of its security strength and computational performance. The performance assessment employed key computational metrics, including system efficiency, encryption latency, and decryption latency. Experimental results demonstrate that the proposed hybrid scheme delivers superior performance compared to existing approaches, attaining a peak efficiency of 98.5%. The method further achieved encryption and decryption times of 6.8 min and 5.7 min, respectively, indicating improved computational handling of large-scale IoHT data.

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https://doi.org/https://doi.org/10.1177/14604582261417493

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@article{abdullah2026,
  title        = {{Next-generation security for big data analytics in healthcare IoT using hybrid cryptographic techniques}},
  author       = {Abdullah Alharbi et al.},
  journal      = {Health Informatics Journal},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/14604582261417493},
}

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Next-generation security for big data analytics in healthcare IoT using hybrid cryptographic techniques

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.