Qiao Zhang
Orcid: 0000-0002-7752-0528Affiliations:
- Old Dominion University, School of Cybersecurity, Norfolk, VA, USA (PhD 2021)
According to our database1,
Qiao Zhang
authored at least 18 papers
between 2018 and 2024.
Collaborative distances:
Collaborative distances:
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Bibliography
2024
IEEE Trans. Inf. Forensics Secur., 2024
Comet: A Communication-efficient and Performant Approximation for Private Transformer Inference.
CoRR, 2024
From Individual Computation to Allied Optimization: Remodeling Privacy-Preserving Neural Inference with Function Input Tuning.
Proceedings of the IEEE Symposium on Security and Privacy, 2024
SPOT: Structure Patching and Overlap Tweaking for Effective Pipelining in Privacy-Preserving MLaaS with Tiny Clients.
Proceedings of the 44th IEEE International Conference on Distributed Computing Systems, 2024
MOSAIC: A Prune-and-Assemble Approach for Efficient Model Pruning in Privacy-Preserving Deep Learning.
Proceedings of the 19th ACM Asia Conference on Computer and Communications Security, 2024
United We Stand: Accelerating Privacy-Preserving Neural Inference by Conjunctive Optimization with Interleaved Nexus.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024
2023
IEEE Netw., November, 2023
PRISC: Privacy-Preserved Pandemic Infection Risk Computation Through Cellular-Enabled IoT Devices.
IEEE Internet Things J., September, 2023
2022
SecureTrain: An Approximation-Free and Computationally Efficient Framework for Privacy-Preserved Neural Network Training.
IEEE Trans. Netw. Sci. Eng., 2022
IEEE Internet Things J., 2022
Joint Linear and Nonlinear Computation across Functions for Efficient Privacy-Preserving Neural Network Inference.
CoRR, 2022
Hunter: HE-Friendly Structured Pruning for Efficient Privacy-Preserving Deep Learning.
Proceedings of the ASIA CCS '22: ACM Asia Conference on Computer and Communications Security, Nagasaki, Japan, 30 May 2022, 2022
2021
Joint Linear and Nonlinear Computation with Data Encryption for Efficient Privacy-Preserving Deep Learning.
PhD thesis, 2021
IEEE Internet Things J., 2021
Proceedings of the 28th Annual Network and Distributed System Security Symposium, 2021
2019
CHEETAH: An Ultra-Fast, Approximation-Free, and Privacy-Preserved Neural Network Framework based on Joint Obscure Linear and Nonlinear Computations.
CoRR, 2019
2018
GELU-Net: A Globally Encrypted, Locally Unencrypted Deep Neural Network for Privacy-Preserved Learning.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018
Marvel: Mann-Whitney Rank-Sum Testing via Segments Labeling for Indoor Pedestrian Localization.
Proceedings of the 2018 IEEE International Conference on Communications, 2018