Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Federated learning-based private medical knowledge graph for epidemic surveillance in internet of things
AU - Wu, Xiaotong
AU - Gao, Jiaquan
AU - Bilal, Muhammad
AU - Dai, Fei
AU - Xu, Xiaolong
AU - Qi, Lianyong
AU - Dou, Wanchun
PY - 2023/6/11
Y1 - 2023/6/11
N2 - With the explosive development of the Internet of Things (IoT), it is convenient and important to collect health data from medical sensors and smart devices and construct medical knowledge graph. The knowledge graph contributes to investigating the connection between patient and disease, especially for epidemic surveillance. However, it is possible to cause the leakage of sensitive health information due to the untrusted data collector or various malicious attackers. In this paper, we attempt to utilise federated learning to construct a special knowledge graph, that is, individual-symptom relationship diagram with local differential privacy (LDP-ISRD), for epidemic risk surveillance, which presents the underlying infectious relationship among individuals. At first, we propose a federated learning-based framework of LDP-ISRD by utilising individuals' smart devices in IoT. Then, we leverage locations to determine the connection among individuals in terms of physical contact. Next, we propose a randomised algorithm PrivISRD to implement federated learning-based LDP-ISRD, which consists of symptom perturbation and aggregation. Finally, extensive experiments evaluate the impact of various parameters and results demonstrate that LDP-ISRD has good performance.
AB - With the explosive development of the Internet of Things (IoT), it is convenient and important to collect health data from medical sensors and smart devices and construct medical knowledge graph. The knowledge graph contributes to investigating the connection between patient and disease, especially for epidemic surveillance. However, it is possible to cause the leakage of sensitive health information due to the untrusted data collector or various malicious attackers. In this paper, we attempt to utilise federated learning to construct a special knowledge graph, that is, individual-symptom relationship diagram with local differential privacy (LDP-ISRD), for epidemic risk surveillance, which presents the underlying infectious relationship among individuals. At first, we propose a federated learning-based framework of LDP-ISRD by utilising individuals' smart devices in IoT. Then, we leverage locations to determine the connection among individuals in terms of physical contact. Next, we propose a randomised algorithm PrivISRD to implement federated learning-based LDP-ISRD, which consists of symptom perturbation and aggregation. Finally, extensive experiments evaluate the impact of various parameters and results demonstrate that LDP-ISRD has good performance.
KW - epidemic surveillance
KW - federated learning
KW - knowledge graph
KW - privacy protection
KW - symptom onset
U2 - 10.1111/exsy.13372
DO - 10.1111/exsy.13372
M3 - Journal article
AN - SCOPUS:85161656085
VL - 42
JO - Expert Systems
JF - Expert Systems
SN - 0266-4720
IS - 1
M1 - e13372
ER -