Xinyuan Qian

Orcid: 0000-0003-3247-6516

Affiliations:
  • University of Electronic Science and Technology of China, School of Computer Science and Engineering, Chengdu, China


According to our database1, Xinyuan Qian authored at least 14 papers between 2022 and 2025.

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

Timeline

Legend:

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PhD thesis 
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Bibliography

2025
GuardGrid: A Queriable and Privacy-Preserving Aggregation Scheme for Smart Grid via Function Encryption.
IEEE Internet Things J., June, 2025

CP-Guard: A Unified, Probability-Agnostic, and Adaptive Framework for Malicious Agent Detection and Defense in Multi-Agent Embodied Perception Systems.
CoRR, June, 2025

FIGhost: Fluorescent Ink-based Stealthy and Flexible Backdoor Attacks on Physical Traffic Sign Recognition.
CoRR, May, 2025

2024
Efficient and Privacy-Preserving Outsourcing of Gradient Boosting Decision Tree Inference.
IEEE Trans. Serv. Comput., 2024

Decentralized Multi-Client Functional Encryption for Inner Product With Applications to Federated Learning.
IEEE Trans. Dependable Secur. Comput., 2024

OnePath: Efficient and Privacy-Preserving Decision Tree Inference in the Cloud.
CoRR, 2024

Privacy-Preserving Data Evaluation via Functional Encryption, Revisited.
Proceedings of the IEEE INFOCOM 2024, 2024

SecSCS: A User-Centric Secure Smart Camera System Based on Blockchain.
Proceedings of the 44th IEEE International Conference on Distributed Computing Systems, 2024

An Efficient and Secure Privacy-Preserving Federated Learning Via Lattice-Based Functional Encryption.
Proceedings of the IEEE International Conference on Communications, 2024

QPFFL: Advancing Federated Learning with Quantum-Resistance, Privacy, and Fairness.
Proceedings of the 2024 IEEE Global Communications Conference, 2024

2023
ESA-FedGNN: Efficient secure aggregation for federated graph neural networks.
Peer Peer Netw. Appl., March, 2023

Toward Efficient and End-to-End Privacy-Preserving Distributed Gradient Boosting Decision Trees.
Proceedings of the IEEE International Conference on Communications, 2023

2022
CryptoFE: Practical and Privacy-Preserving Federated Learning via Functional Encryption.
Proceedings of the IEEE Global Communications Conference, 2022

Fast Secure Aggregation for Privacy-Preserving Federated Learning.
Proceedings of the IEEE Global Communications Conference, 2022


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