TY - GEN
T1 - Countermeasure of Polluting Health-Related Dataset for Data Mining
AU - Liu, I-Hsien
AU - Li, Jung Shian
AU - Peng, Yen Chu
AU - Lee, Meng Huan
AU - Liu, Chuan Gang
N1 - Funding Information:
ACKNOWLEDGMENTS The authors gratefully acknowledge the support of the Ministry of Science and Technology in Taiwan under Grant MOST 111-2218-E-006-010-MBK and MOST 108-2221-E-006-110-MY3.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Nowadays, machine learning is widely used in a variety of applications, but it still faces many security challenges. Among them, the security of dataset is particularly important, because the data set is the key factor in achieving high correctness for machine learning. Recently, it becomes more difficult for an attacker to directly modify or attack the machine learning models because these models are setup usually in a well-known and well-designed format. However, the attackers can easily manipulate the dataset in various ways. Therefore, we develop countermeasures of polluting o a health-related dataset for data mining, which is robust Data Washing, an algorithm based on denoising autoencoder. It effectively alleviates damages to datasets caused by poisoning attack. We implement several DNN models for different datasets. The proposed Our robust Data Washing algorithm efficiently recovers the poisoning dataset and detect several attacks with a high accuracy rate.
AB - Nowadays, machine learning is widely used in a variety of applications, but it still faces many security challenges. Among them, the security of dataset is particularly important, because the data set is the key factor in achieving high correctness for machine learning. Recently, it becomes more difficult for an attacker to directly modify or attack the machine learning models because these models are setup usually in a well-known and well-designed format. However, the attackers can easily manipulate the dataset in various ways. Therefore, we develop countermeasures of polluting o a health-related dataset for data mining, which is robust Data Washing, an algorithm based on denoising autoencoder. It effectively alleviates damages to datasets caused by poisoning attack. We implement several DNN models for different datasets. The proposed Our robust Data Washing algorithm efficiently recovers the poisoning dataset and detect several attacks with a high accuracy rate.
UR - http://www.scopus.com/inward/record.url?scp=85143144380&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85143144380&partnerID=8YFLogxK
U2 - 10.1109/ECBIOS54627.2022.9945037
DO - 10.1109/ECBIOS54627.2022.9945037
M3 - Conference contribution
AN - SCOPUS:85143144380
T3 - Proceedings of the 2022 IEEE 4th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2022
SP - 152
EP - 155
BT - Proceedings of the 2022 IEEE 4th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2022
A2 - Meen, Teen-Hang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2022
Y2 - 27 May 2022 through 29 May 2022
ER -
