期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 18, 期 7, 页码 4798-4807出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2021.3117285
关键词
Data privacy; Medical services; Cloud computing; Security; Legged locomotion; Privacy; Biomedical monitoring; Data analytics; deep learning; IoT-enabled healthcare; privacy preservation
类别
资金
- Fundamental Research Funds for the Central Universities [31020200QD010, D5000210598]
- National Natural Science Foundation of China [61771374, 61771373, 61801360, 62001393]
- Natural Science Basic Research Program of Shaanxi [2020JC-15, 2020JM-109]
- Special Funds for Central Universities Construction of World-Class Universities [0639020GH020114]
- Special Development Guidance [0639020GH020114]
With the development of the industrial Internet of Things (IIoT), intelligent healthcare aims to monitor users' health information remotely. However, the security of privacy information in the data collected from wearable devices has been overlooked. This article proposes a deep learning-based privacy preservation and data analytics system to solve this problem.
With the development of the industrial Internet of Things (IIoT), intelligent healthcare aims to build a platform to monitor users' health-related information based on wearable devices remotely. The evolution of blockchain and artificial intelligence technology also promotes the progress of secure intelligent healthcare. However, since the data are stored in the cloud server, it still faces the risk of being attacked and privacy leakage. Note that little attention has been paid to the security issue of privacy information mixed in raw data collected from large number of distributed and heterogeneous wearable healthcare devices. To solve this problem, in this article, we design a deep learning-based privacy preservation and data analytics system for IoT enabled healthcare. At the user end, we collect raw data and separate the users' privacy information in the privacy-isolation zone. At the cloud end, we analyze the health-related data without users' privacy information and construct a delicate security module based on the convolutional neural network. We also deploy and evaluate the prototype system, where extensive experiments prove its effectiveness and robustness.
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