A Differentially Private Classification Algorithm with High Utility for Wireless Body Area Networks
Author | Sun, Xianwen |
Author | Shi, Lingyun |
Author | Wu, Longfei |
Author | Guan, Zhitao |
Author | Du, Xiaojiang |
Author | Guizani, Mohsen |
Available date | 2022-12-09T19:15:43Z |
Publication Date | 2020-05-01 |
Publication Name | IEEE Wireless Communications and Networking Conference, WCNC |
Identifier | http://dx.doi.org/10.1109/WCNC45663.2020.9120495 |
Citation | Sun, X., Shi, L., Wu, L., Guan, Z., Du, X., & Guizani, M. (2020, May). A Differentially Private Classification Algorithm With High Utility for Wireless Body Area Networks. In 2020 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1-6). IEEE. |
ISBN | 9781728131061 |
ISSN | 15253511 |
Abstract | The advancement of the wireless body area networks (WBAN) and sensor technologies allows us to collect a variety of physiological and behavioral data from human body. And appropriate application of machine learning methods can greatly promote the development of e-health. Nevertheless, the collected data contains personal privacy information. When using the machine learning methods to analyze the collected data, some information of the training data will be stored in the learning models unconsciously. To handle such information disclosure problem, we propose a differentially private classification algorithm based on ensemble decision tree with high utility for wireless body area networks. In order to improve the accuracy and stableness of classification, the bagging framework of ensemble learning is used in our algorithm. We aggregate the results of multiple private decision trees as the final classification in a weight-based voting way. For each private decision tree trained on the bootstrap samples, we offer a novel privacy budget allocation strategy that allows the nodes in larger depth to get more privacy budget, which can mitigate the problem of excessive noise introduced to leaf nodes to some extent. The better classification accuracy and stableness of this new algorithm, especially on small dataset, are demonstrated by simulation experiments. |
Sponsor | The work is partially supported by the National Natural Science Foundation of China under Grant 61972148, the National Key R and D Program of China under grant 2018YFC0831404, Beijing Natural Science Foundation under grant 4182060. |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | Bagging decision tree Differential privacy wireless body area networks |
Type | Conference Paper |
Volume Number | 2020-May |
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