Enhancing Internet of Things security in healthcare using a blockchain-driven lightweight hashing system

被引:0
作者
Aboshosha, Bassam W. [1 ]
Zayed, M. Mokhtar [2 ]
Khalifa, Hany S. [3 ]
Ramadan, Rabie A. [4 ,5 ]
机构
[1] Univ Hertfordshire Hosted Global Acad Fdn, New Adm Capital, Cairo, Egypt
[2] El Shorouk Acad, Higher Inst Engn, Cairo, Egypt
[3] Misr Higher Inst Commerce & Comp, Mansoura, Egypt
[4] Univ Nizwa, Coll Econ Management & Informat Syst, Dept Informat Syst, Nizwa, Oman
[5] Cairo Univ, Fac Engn, Comp Engn Dept, Giza, Egypt
关键词
Blockchain; Lightweight hash function; Healthcare; Internet of Things; Hospitals;
D O I
10.1186/s43088-025-00644-8
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
BackgroundThe rapid expansion of Internet of Things applications in healthcare has created new opportunities for improving patient care through real-time monitoring and data sharing. However, this growth also introduces significant challenges related to data security, privacy, and system efficiency, especially for devices with limited processing power and energy resources. To address these issues, this study introduces a blockchain-based lightweight hashing system specifically designed for healthcare environments with resource-constrained devices. The goal is to ensure secure, efficient, and scalable handling of sensitive medical data without overwhelming the capabilities of connected devices.ResultsThe proposed system combines a collision-resistant, lightweight hash function with blockchain technology to enhance data integrity, authentication, and privacy. The hash function minimizes computational demands, making it ideal for wearable and embedded healthcare devices. Blockchain integration enables decentralized data management, preventing unauthorized access and tampering. The system generates unique, immutable patient identifiers and protects electronic health information from common security threats, including collision attacks, Sybil attacks, and cryptographic analysis. Simulation results show improved computational efficiency, lower latency, and effective handling of high transaction volumes with minimal resource usage.ConclusionsThis research presents a secure and efficient framework for managing medical data in healthcare Internet of Things applications. By leveraging lightweight cryptographic techniques and decentralized data structures, the system addresses key limitations in current solutions while supporting scalability and real-world deployment. Potential applications include secure patient monitoring, real-time sharing of health data, and decentralized management of medical records. The proposed approach provides a foundation for future advancements in digital healthcare systems, particularly in remote care, emergency response, and wearable health technologies.
引用
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页数:30
相关论文
共 45 条
[1]   Implication of Lightweight and Robust Hash Function to Support Key Exchange in Health Sensor Networks [J].
Al-Zubaidie, Mishall .
SYMMETRY-BASEL, 2023, 15 (01)
[2]   Security of Blockchain and AI-Empowered Smart Healthcare: Application-Based Analysis [J].
Alabdulatif, Abdulatif ;
Khalil, Ibrahim ;
Rahman, Mohammad Saidur .
APPLIED SCIENCES-BASEL, 2022, 12 (21)
[3]  
Alhija MA., 2024, Indones. J. Electr. Eng. Comput. Sci, V35, P1773, DOI DOI 10.11591/IJEECS.V35.I3.PP1773-1785
[4]   HealthLock: Blockchain-Based Privacy Preservation Using Homomorphic Encryption in Internet of Things Healthcare Applications [J].
Ali, Aitizaz ;
Al-rimy, Bander Ali Saleh ;
Alsubaei, Faisal S. ;
Almazroi, Abdulwahab Ali ;
Almazroi, Abdulaleem Ali .
SENSORS, 2023, 23 (15)
[5]   An Industrial IoT-Based Blockchain-Enabled Secure Searchable Encryption Approach for Healthcare Systems Using Neural Network [J].
Ali, Aitizaz ;
Almaiah, Mohammed Amin ;
Hajjej, Fahima ;
Pasha, Muhammad Fermi ;
Fang, Ong Huey ;
Khan, Rahim ;
Teo, Jason ;
Zakarya, Muhammad .
SENSORS, 2022, 22 (02)
[6]   Planning a secure and reliable IoT-enabled FOG-assisted computing infrastructure for healthcare [J].
Ali, Hafiz Munsub ;
Liu, Jun ;
Bukhari, Syed Ahmad Chan ;
Rauf, Hafiz Tayyab .
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2022, 25 (03) :2143-2161
[7]   A Lightweight Hybrid Deep Learning Privacy Preserving Model for FC-Based Industrial Internet of Medical Things [J].
Almaiah, Mohammed Amin ;
Ali, Aitizaz ;
Hajjej, Fahima ;
Pasha, Muhammad Fermi ;
Alohali, Manal Abdullah .
SENSORS, 2022, 22 (06)
[8]   Blockchain-Powered Deep Learning for Internet of Things With Cloud-Assisted Secure Smart Home Networks [J].
Alruwaili, Fahad F. .
IEEE ACCESS, 2024, 12 :119927-119936
[9]   On the (Im)plausibility of Public-Key Quantum Money from Collision-Resistant Hash Functions [J].
Ananth, Prabhanjan ;
Hu, Zihan ;
Yuen, Henry .
ADVANCES IN CRYPTOLOGY, ASIACRYPT 2023, PT VIII, 2023, 14445 :39-72
[10]   BFLIDS: Blockchain-Driven Federated Learning for Intrusion Detection in IoMT Networks [J].
Begum, Khadija ;
Mozumder, Md Ariful Islam ;
Joo, Moon-Il ;
Kim, Hee-Cheol .
SENSORS, 2024, 24 (14)