A new data clustering strategy for enhancing mutual privacy in healthcare IoT systems

In the rapidly growing era of Internet-of-Things (IoT), healthcare systems have enabled a sea of connections of physical sensors. Data analysis methods (e.g., k-means) are often used to process data collected from wireless sensor networks to provide treatment advices for physicians and patients. How...

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Bibliographic Details
Published in:Future generation computer systems 2020-12, Vol.113, p.407-417
Main Authors: Guo, Xuancheng, Lin, Hui, Wu, Yulei, Peng, Min
Format: Article
Language:English
Subjects:
Healthcare IoT systems
Homomorphic encryption
Machine learning
Privacy preservation
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Summary:In the rapidly growing era of Internet-of-Things (IoT), healthcare systems have enabled a sea of connections of physical sensors. Data analysis methods (e.g., k-means) are often used to process data collected from wireless sensor networks to provide treatment advices for physicians and patients. However, many methods pose a threat of privacy leakage during the process of data handling. To address privacy issues, we propose a mutual privacy-preserving k-means strategy (M-PPKS) based on homomorphic encryption that neither discloses the participant’s privacy nor leaks the cluster center’s private data. The proposed M-PPKS divides each iteration of a k-means algorithm into two stages: finding the nearest cluster center for each participant, followed by computing a new center for each cluster. In both phases, the cluster center is confidential to participants, and the private information of each participant is not accessible by an analyst. Besides, M-PPKS introduces a third-party cloud platform to reduce the communication complexity of homomorphic encryption. Extensive privacy analysis and performance evaluation results manifest that the proposed M-PPKS strategy can achieve high performance. In addition, it can obtain approximate clustering results efficiently while preserving mutual private data. •Proposing a data clustering strategy for enhancing the mutual privacy preserving.•Protecting the privacy of both participants and cluster centers.•Reducing the time complexity of homomorphic encryption.•Improving the accuracy of clustering results.•Enhancing the ability of resisting collusion attacks.
ISSN:0167-739X
1872-7115
DOI:10.1016/j.future.2020.07.023