Wei Xu

Orcid: 0000-0001-8948-9516

Affiliations:
  • Xidian University, Xi'an, Shaanxi, China


According to our database1, Wei Xu authored at least 12 papers between 2023 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
Breaking Beyond One: Mirage Attacks for Highly Accurate Multi-Keyword Query Recovery With Partial Similar Data Against SE.
IEEE Trans. Inf. Forensics Secur., 2026

TCKKS: An Efficient TEE-Assistance CKKS Scheme Without Bootstrapping.
IEEE Trans. Dependable Secur. Comput., 2026

Collusion-Resistant Privacy-Preserving Outsourced Training Under Single Cloud With Semi-Honest TEE.
IEEE Trans. Dependable Secur. Comput., 2026

Kangaroo: A Private and Amortized Inference Framework over WAN for Large-Scale Decision Tree Evaluation.
Proceedings of the 33rd Annual Network and Distributed System Security Symposium, 2026

2025
SGBoost<sup>+</sup>: Efficient and Privacy-Preserving Vertical Boosting Trees for Federated Outsourced Training and Inference.
IEEE Trans. Inf. Forensics Secur., 2025

Achieving Efficient BGV Scheme with Semi-Honest TEE Assistance.
Proceedings of the IEEE International Conference on Communications, 2025

2024
Enhancing paillier to fully homomorphic encryption with semi-honest TEE.
Peer Peer Netw. Appl., September, 2024

ELXGB: An Efficient and Privacy-Preserving XGBoost for Vertical Federated Learning.
IEEE Trans. Serv. Comput., 2024

ToNN: An Oblivious Neural Network Prediction Scheme With Semi-Honest TEE.
IEEE Trans. Inf. Forensics Secur., 2024

Toward Privacy-Preserving and Verifiable XGBoost Training for Horizontal Federated Learning.
Proceedings of the 23rd IEEE International Conference on Trust, 2024

Efficient and Lossless Integrity-preserving Training Scheme for High-dimensional Logistic Regression over Vertical Data.
Proceedings of the 2024 IEEE Global Communications Conference, 2024

2023
SGBoost: An Efficient and Privacy-Preserving Vertical Federated Tree Boosting Framework.
IEEE Trans. Inf. Forensics Secur., 2023


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