A Medical Healthcare System for Privacy Protection Based on IoT
Abstract
China's medical healthcare system has witnessed a great evolution from clinical normalization phase to pervasive informatization phase. Unquestionably, emergence of IoT (Internet of things) technology contributes to the development of medical healthcare informatization. Yet, it is widely researched and accepted that privacy protection has become a bottleneck of smart medical healthcare nowadays. Consequently, how to protect the privacy of users' information in a smart healthcare system remains a pending problem. Under this background, this paper analyses the main problems in current smart healthcare system. Furthermore, a lightweight private homomorphism algorithm and an encryption algorithm improved from DES are designed when considering the characteristic of IoT and privacy protection. Finally, based on the above, we designed and completed a prototype system based on both software and hardware.
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Information collected by a number of healthcare institutions using different IoT devices may be collected using Fed-Health's surgical instruments to enable the development of a powerful AI method that can be used to identify people's behavior and to protect ciphertext data.
“Information collected by a number of healthcare institutions using different IoT devices may be collected using Fed- Health's surgical instruments to enable the development of a powerful AI method that can be used to identify people's behavior and to protect ciphertext data [76].”
From A comprehensive survey on federated learning techniques for healthcare informatics (2023) · cites this paper as [76]
Cites
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Federated Learning for Internet of Things: A Comprehensive Survey
Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Vincent Poor, H. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3075439 · Cited by 1,503 · see citing works
“FedHealth allows to aggregate the data from separate hospital organizations with multiple wearable IoT devices to build a strong AI model for medical tasks, such as human activity recognition, enabled by homomorphic encryption for health data privacy preservation [121].”
BackgroundReview of Security and Privacy for the Internet of Medical Things (IoMT)
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A comprehensive survey on federated learning techniques for healthcare informatics
Dasaradharami Reddy, K., & Gadekallu, T. R. (2023) · Computational Intelligence and Neuroscience · doi:10.1155/2023/8393990 · Cited by 126 · see citing works
- Authors
- Gong, T., Huang, H., Li, P., Zhang, K., & Jiang, H.
- Affiliations
- Nanjing University of Posts and Telecommunications (CN)
- Clemson University (US)
- Venue
- 2015 Seventh International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 217-222
- OpenAlex
- W2248097001
- Cited by
- 103
- References
- 10
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 7 · 2023: 17 · 2022: 20 · 2021: 16 · 2020: 10 · 2019: 13 · 2018: 7 · 2017: 6 · 2016: 1
- FWCI
- 4.05
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Symposium on Parallel Architectures, Algorithms and Programming (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- privacy preservation, Internet of Things, smart healthcare, medical information security, lightweight encryption, healthcare informatization, prototype system
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