Lightweight Compression and Chaos-Based Encryption for Secure IoT Healthcare Data Storage on Blockchain

S. Mubeena; P. K. Jawahar

doi:10.48084/etasr.12888

Authors

S. Mubeena Department of Electronics and Communication Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India
P. K. Jawahar Department of Electronics and Communication Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India

Volume: 15 | Issue: 6 | Pages: 29759-29769 | December 2025 | https://doi.org/10.48084/etasr.12888

Received: 23 June 2025 | Revised: 6 August 2025, 29 August 2025, 10 September 2025, and 12 September 2025 | Accepted: 15 September 2025 | Online: 8 December 2025

Corresponding author: P. K. Jawahar

Abstract

The increasing incidence of cyberattacks on healthcare infrastructure has highlighted the critical vulnerability of sensitive patient data, necessitating the implementation of advanced security measures. Although blockchain technology offers a promising solution for ensuring data integrity and confidentiality, its integration into resource-constrained medical devices, especially low-power embedded systems, presents significant challenges. This study addresses these challenges by proposing two novel frameworks: Zlib Hardware Accelerator with Adaptive Dictionary Encoding (ZHA-ADE) for efficient data compression, and Chaotic Hybrid Asymmetric and Symmetric Encryption (CHASE) for lightweight and secure encryption. ZHA-ADE enhances traditional Zlib compression with adaptive dictionary encoding, optimizing biomedical data throughput and reducing the computational load on ARM Cortex-A microcontrollers while maintaining compatibility with blockchain. Simultaneously, CHASE combines chaotic key generation with Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC) techniques to provide high-entropy outputs and strong defense against cryptographic attacks while using minimal processing power, making it ideal for real-time applications in the healthcare industry. The proposed system was evaluated across key metrics, including compression ratio, processing time, energy efficiency, and implementation cost. Results demonstrate that the hardware-optimized blockchain–Internet of Things (IoT) framework significantly improves healthcare data integrity. Compression was accelerated by 98%, enabling the processing of large datasets in 35 ms. Meanwhile, the encryption model achieved outstanding performance, recording the lowest encryption time of 2.8 ms and the highest ciphertext entropy of 8.0 bits per byte. These results establish the proposed architecture as a highly viable solution for future decentralized and real-time healthcare systems, enhancing both the security and accessibility of critical patient data in resource-limited environments.

Keywords:

blockchain, healthcare, Internet of Things (IoT), ARM Cortex, data compression, chaotic encryption, Zlib, patient data security

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References

S. Dhingra, R. Raut, K. Naik, and K. Muduli, "Blockchain Technology Applications in Healthcare Supply Chains—A Review," IEEE Access, vol. 12, pp. 11230–11257, 2024. DOI: https://doi.org/10.1109/ACCESS.2023.3348813

R. Ramani, A. Rosline Mary, S. Edwin Raja, and D. Arun Shunmugam, "Optimized data management and secured federated learning in the Internet of Medical Things (IoMT) with blockchain technology," Biomedical Signal Processing and Control, vol. 93, Jul. 2024, Art. no. 106213. DOI: https://doi.org/10.1016/j.bspc.2024.106213

"India health data faces rising risk of breaches, cyberattacks." ETHealthworld.com. https://health.economictimes.indiatimes.com/news/industry/india-health-data-faces-rising-risk-of-breaches-cyberattacks/102068648.

J. Tan, J. Shi, J. Wan, H.-N. Dai, J. Jin, and R. Zhang, "Blockchain-Based Data Security and Sharing for Resource-Constrained Devices in Manufacturing IoT," IEEE Internet of Things Journal, vol. 11, no. 15, pp. 25558–25567, Aug. 2024. DOI: https://doi.org/10.1109/JIOT.2024.3363013

S. Guan, Y. Cao, and Y. Zhang, "Blockchain-Enhanced Data Privacy Preservation and Secure Sharing Scheme for Healthcare IoT," IEEE Internet of Things Journal, vol. 12, no. 5, pp. 5600–5614, Mar. 2025. DOI: https://doi.org/10.1109/JIOT.2024.3487154

T. A. Alghamdi, R. Khalid, and N. Javaid, "A Survey of Blockchain Based Systems: Scalability Issues and Solutions, Applications and Future Challenges," IEEE Access, vol. 12, pp. 79626–79651, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3408868

O. Popoola, M. A. Rodrigues, J. Marchang, A. Shenfield, A. Ikpehai, and J. Popoola, "An optimized hybrid encryption framework for smart home healthcare: Ensuring data confidentiality and security," Internet of Things, vol. 27, Oct. 2024, Art. no. 101314. DOI: https://doi.org/10.1016/j.iot.2024.101314

G. Sarojini Karuppusamy and M. K. S, "TwoFish-Integrated Blockchain for Secure and Optimized Healthcare Data Processing in IoT-Edge-Cloud System," Transactions on Emerging Telecommunications Technologies, vol. 36, no. 3, Mar. 2025, Art. no. e70076. DOI: https://doi.org/10.1002/ett.70076

I. Masood, A. Daud, Y. Wang, A. Banjar, and R. Alharbey, "A blockchain-based system for patient data privacy and security," Multimedia Tools and Applications, vol. 83, no. 21, pp. 60443–60467, Jun. 2024. DOI: https://doi.org/10.1007/s11042-023-17941-y

B. Halak, Y. Yilmaz, and D. Shiu, "Comparative Analysis of Energy Costs of Asymmetric vs Symmetric Encryption-Based Security Applications," IEEE Access, vol. 10, pp. 76707–76719, 2022. DOI: https://doi.org/10.1109/ACCESS.2022.3192970

S. R. Mallick, R. K. Lenka, P. K. Tripathy, D. C. Rao, S. Sharma, and N. K. Ray, "A Lightweight, Secure, and Scalable Blockchain-Fog-IoMT Healthcare Framework with IPFS Data Storage for Healthcare 4.0," SN Computer Science, vol. 5, no. 1, Jan. 2024, Art. no. 198. DOI: https://doi.org/10.1007/s42979-023-02511-8

R. Gao, Z. Li, G. Tan, and X. Li, "BeeZip: Towards An Organized and Scalable Architecture for Data Compression," in Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3, La Jolla, CA, USA, 2024, pp. 133–148. DOI: https://doi.org/10.1145/3620666.3651323

Y. Li, Q. Wang, and S. Yu, "A novel hybrid scheme for chaotic image encryption," Physica Scripta, vol. 99, no. 4, Mar. 2024, Art. no. 045244. DOI: https://doi.org/10.1088/1402-4896/ad3171

T. M. Ignatius, T. Birjit Singha, and R. Paily Palathinkal, "Power Side-Channel Attacks on Crypto-Core Based on RISC-V ISA for High-Security Applications," IEEE Access, vol. 12, pp. 150230–150248, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3477961

M. A. Caraveo-Cacep, R. Vázquez-Medina, and A. Hernández Zavala, "A review on security implementations in soft-processors for IoT applications," Computers & Security, vol. 139, Apr. 2024, Art. no. 103677. DOI: https://doi.org/10.1016/j.cose.2023.103677

R. Ifrim, D. Loghin, and D. Popescu, "A Systematic Review of Fast, Scalable, and Efficient Hardware Implementations of Elliptic Curve Cryptography for Blockchain," ACM Transactions on Reconfigurable Technology and Systems, vol. 17, no. 4, Nov. 2024, Art. no. 62. DOI: https://doi.org/10.1145/3696422

"Docker Reference Documentation." Dockerdocs. https://docs.docker.com/reference/.

"Blockscout Docs." Blockscout. https://docs.blockscout.com.

C. Marcon, A. S. Mete, P. V. Gemmeren, and L. Carminati, "Optimizing ATLAS data storage: The impact of compression algorithms on ATLAS physics analysis data formats," EPJ Web of Conferences, vol. 295, May 2024, Art. no. 03027. DOI: https://doi.org/10.1051/epjconf/202429503027

M. Hema and S. P. Shyry, "Efficient Compression of Multimedia Data using Lempel–Ziv–Markov Chain Adaptive Block Compressive Sensing (LZMC-ABCS)," Wireless Personal Communications, May 2024. DOI: https://doi.org/10.1007/s11277-024-11187-z

F. Novanto, A. Nugraha, J. C. Kurniawan, and A. I. Prayogo, "Optimizing Digital Image Steganography through Hybridization of LSB and Zstandard Compression," Sinkron : jurnal dan penelitian teknik informatika, vol. 8, no. 1, pp. 75–82, Jan. 2024. DOI: https://doi.org/10.33395/sinkron.v9i1.13187

A. Soboń and S. Stachowiak, "ChaCha20 Cipher Cryptanalysis through SAT Problem Solving," in 2024 IEEE 17th International Scientific Conference on Informatics, Poprad, Slovakia, 2024, pp. 355–361. DOI: https://doi.org/10.1109/Informatics62280.2024.10900867

A. Gupta, S. Namasudra, and P. Kumar, "A secure VM live migration technique in a cloud computing environment using blowfish and blockchain technology," The Journal of Supercomputing, vol. 80, no. 19, pp. 27370–27393, Dec. 2024. DOI: https://doi.org/10.1007/s11227-024-06461-7

Z. A. Mohammed, H. Q. Gheni, Z. J. Hussein, and A. K. M. Al-Qurabat, "Advancing Cloud Image Security via AES Algorithm Enhancement Techniques," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12694–12701, Feb. 2024. DOI: https://doi.org/10.48084/etasr.6601

A. K. Singh, T. K. Jain, P. Pandey, and L. Rzayeva, "LVCMOS Based Low Power Implementation of DES Encryption Algorithm on 28nm FPGA," in 2024 3rd International conference on Power Electronics and IoT Applications in Renewable Energy and its Control, Mathura, India, 2024, pp. 383–386. DOI: https://doi.org/10.1109/PARC59193.2024.10486400

"COVID-19 Radiography Database." Kaggle. [Online]. Available: https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database.

K. Zourmba et al., "Fractional order 1D memristive time-delay chaotic system with application to image encryption and FPGA implementation," Mathematics and Computers in Simulation, vol. 227, pp. 58–84, Jan. 2025. DOI: https://doi.org/10.1016/j.matcom.2024.07.035