TY - GEN
T1 - A Differentially Private Classification Algorithm with High Utility for Wireless Body Area Networks
AU - Sun, Xianwen
AU - Shi, Lingyun
AU - Wu, Longfei
AU - Guan, Zhitao
AU - Du, Xiaojiang
AU - Guizani, Mohsen
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - 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.
AB - 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.
KW - Bagging
KW - Differential privacy
KW - decision tree
KW - wireless body area networks
UR - http://www.scopus.com/inward/record.url?scp=85087282652&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85087282652&partnerID=8YFLogxK
U2 - 10.1109/WCNC45663.2020.9120495
DO - 10.1109/WCNC45663.2020.9120495
M3 - Conference contribution
AN - SCOPUS:85087282652
T3 - IEEE Wireless Communications and Networking Conference, WCNC
BT - 2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings
T2 - 2020 IEEE Wireless Communications and Networking Conference, WCNC 2020
Y2 - 25 May 2020 through 28 May 2020
ER -