Poisoning Attack on Federated Knowledge Graph Embedding
Author:
Affiliation:
1. Hong Kong Polytechnic University, Hong Kong, Hong Kong
2. Hong Kong University of Science and Technology, Hong Kong, Hong Kong
3. Xidian University, Xi'an, China
Funder
the National Natural Science Foundation of China
Hong Kong RGC Research Impact Fund
Shenzhen Science and Technology Innovation Commission
National Key Research and Development Program of China
General Research Fund
the Key-Area Research and Development Program of Guangdong Province
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3589334.3645422
Reference55 articles.
1. Deep Learning with Differential Privacy
2. BeamQA: Multi-hop Knowledge Graph Question Answering with Sequence-to-Sequence Prediction and Beam Search
3. Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods
4. Poisoning Knowledge Graph Embeddings via Relation Inference Patterns
5. Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation
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