Achieving privacy-preserving cross-silo anomaly detection using federated XGBoost

Mengwei Yang; Shuqi Liu; Jie Xu; Guozhen Tan; Congduan Li; Linqi Song

doi:10.1016/j.jfranklin.2023.04.002

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JOURNAL ARTICLE

Achieving privacy-preserving cross-silo anomaly detection using federated XGBoost

Mengwei Yang Shuqi Liu Jie Xu Guozhen Tan Congduan Li Linqi Song

Year: 2023 Journal: Journal of the Franklin Institute Vol: 360 (9)Pages: 6194-6210 Publisher: Elsevier BV

DOI: 10.1016/j.jfranklin.2023.04.002

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Keywords:

Differential privacy Computer science Anonymity Context (archaeology) Data mining Federated learning Anomaly detection Cluster (spacecraft) Information privacy Feature (linguistics) Data aggregator Private information retrieval Computer security Computer network Artificial intelligence Geography

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Privacy-Preserving Technologies in Data

Physical Sciences → Computer Science → Artificial Intelligence

Imbalanced Data Classification Techniques

Physical Sciences → Computer Science → Artificial Intelligence

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Achieving privacy-preserving cross-silo anomaly detection using federated XGBoost

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