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المؤلفSun, Xianwen
المؤلفShi, Lingyun
المؤلفWu, Longfei
المؤلفGuan, Zhitao
المؤلفDu, Xiaojiang
المؤلفGuizani, Mohsen
تاريخ الإتاحة2022-12-09T19:15:43Z
تاريخ النشر2020-05-01
اسم المنشورIEEE Wireless Communications and Networking Conference, WCNC
المعرّفhttp://dx.doi.org/10.1109/WCNC45663.2020.9120495
الاقتباسSun, 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.‏
الترقيم الدولي الموحد للكتاب 9781728131061
الرقم المعياري الدولي للكتاب15253511
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85087282652&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/37112
الملخصThe 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.
راعي المشروعThe 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.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعBagging
decision tree
Differential privacy
wireless body area networks
العنوانA Differentially Private Classification Algorithm with High Utility for Wireless Body Area Networks
النوعConference Paper
رقم المجلد2020-May
dc.accessType Abstract Only


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