Jiaqi Zhao
Orcid: 0000-0002-1604-1953Affiliations:
- Xidian University, School of Cyber Engineering, Xi'an, China
According to our database1,
Jiaqi Zhao authored at least 18 papers
between 2022 and 2026.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
Online presence:
-
on orcid.org
On csauthors.net:
Bibliography
2026
Plog: An Efficient and Privacy-Preserving Collaborative Learning Framework on Vertically Partitioned Graph Data.
IEEE Trans. Knowl. Data Eng., June, 2026
Achieving Privacy-Preserving and High-Accuracy Collection of Key-Value Data With Local Differential Privacy.
IEEE Trans. Inf. Forensics Secur., 2026
SXGB: Secure and Efficient Vertical Federated XGBoost via Trusted Execution Environments.
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
2025
IEEE Trans. Inf. Forensics Secur., 2025
SGBoost<sup>+</sup>: Efficient and Privacy-Preserving Vertical Boosting Trees for Federated Outsourced Training and Inference.
IEEE Trans. Inf. Forensics Secur., 2025
SplitAD: A lightweight and privacy-enhancing vertical federated anomaly detection framework based on hierarchical autoencoders.
Inf. Sci., 2025
2024
IEEE Trans. Serv. Comput., 2024
IEEE Trans. Serv. Comput., 2024
Proceedings of the 23rd IEEE International Conference on Trust, 2024
2023
Efficient and privacy-preserving tree-based inference via additive homomorphic encryption.
Inf. Sci., December, 2023
SGBoost: An Efficient and Privacy-Preserving Vertical Federated Tree Boosting Framework.
IEEE Trans. Inf. Forensics Secur., 2023
VFLR: An Efficient and Privacy-Preserving Vertical Federated Framework for Logistic Regression.
IEEE Trans. Cloud Comput., 2023
Efficient and Privacy-Preserving Logistic Regression Prediction over Vertically Partitioned Data.
Proceedings of the IEEE Global Communications Conference, 2023
2022
PVD-FL: A Privacy-Preserving and Verifiable Decentralized Federated Learning Framework.
IEEE Trans. Inf. Forensics Secur., 2022
CORK: A privacy-preserving and lossless federated learning scheme for deep neural network.
Inf. Sci., 2022
IEEE Internet Things J., 2022
ACCEL: an efficient and privacy-preserving federated logistic regression scheme over vertically partitioned data.
Sci. China Inf. Sci., 2022