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LSH-aware multitype health data prediction with privacy preservation in edge environment

Abstract

With the increasing development of electronic technology, traditional paper-driven medical systems have been converting to efficient electronic records that can be easily checked and transmitted. However, due to system updating and equipment failure, missing data problems are very common in the healthcare field. Health data can help people evaluate their health status and adjust their fitness. Therefore, predicting missing health data is a current pressing task. There are two challenges when predicting missing data: (1) people’s health data are complex. The data contain multiple data types (such as continuous data, discrete data and Boolean data) and (2) privacy issues are raised at the edge because huge amounts of health data are published while the edge devices can only provide limited computing and storage resources. Therefore, a novel multitype health data privacy-aware prediction approach based on locality-sensitive hashing is proposed in this paper. Through locality-sensitive hashing, our proposed method can realize a good tradeoff between prediction accuracy and privacy preservation. Finally, through a set of experiments deployed on the WISDM dataset, we verify the validity of our approach in dealing with multitype data and attaining user privacy.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61872219) and the Natural Science Foundation of Shandong Province (ZR2019MF001).

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Correspondence to Lianyong Qi.

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This article belongs to the Topical Collection: Special Issue on Resource Management at the Edge for Future Web, Mobile and IoT Applications

Guest Editors: Qiang He, Fang Dong, Chenshu Wu, and Yun Yang

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Cite this article

Kong, L., Wang, L., Gong, W. et al. LSH-aware multitype health data prediction with privacy preservation in edge environment. World Wide Web (2021). https://doi.org/10.1007/s11280-021-00941-z

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Keywords

  • Multitype health data
  • Missing data prediction
  • Privacy
  • Locality-sensitive hashing
  • Edge