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DataSeal: Ensuring the Verifiability of Private Computation on Encrypted Data
Muhammad Husni Santriaji, Jiaqi Xue, Yancheng Zhang, Qian Lou and Yan Solihin
IEEE S&P, 2025
This work enhances the verifiability and integrity of private computation in FHE by incorporating algorithm-based fault tolerance.
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HEBridge: Connecting arithmetic and logic operations in FV-style HE schemes
Yancheng Zhang, Xun Chen and Qian Lou
ACM CCS WAHC, 2024
This work enables the continuous evaluation of linear and non-linear operations within the BGV/BFV FHE schemes, enhancing their universality.
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CryptoTrain: Fast Secure Training on Encrypted Dataset
Jiaqi Xue, Yancheng Zhang, Yanshan Wang, Xueqiang Wang, Hao Zheng, Qian Lou
ACM CCS LAMPS, 2024
This work accelerates FHE/MPC-based private training through correlation-aware polynomial convolution.
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Encrypted Data Pruning for Confidential Training of Deep Neural Networks
Yancheng Zhang, Mengxin Zheng, Yuzhang Shang, Xun Chen, Qian Lou
NeurIPS, 2024
This work enhances FHE-based private training by incorporating and optimizing dynamic dataset pruning in the encrypted state.
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CR-UTP: Certified Robustness against Universal Text Perturbations
Qian Lou, Xin Liang, Jiaqi Xue, Yancheng Zhang, Rui Xie, Mengxin Zheng
ACL, 2024
This work improves the certified robustness of language models against universal text perturbations through prompt search and ensemble methods.
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Pure graph-guided multi-view subspace clustering
Hongjie Wu, Shudong Huang, Chenwei Tang, Yancheng Zhang, Jiancheng LV
Pattern Recognition, 2023
This work improves multi-view subspace clustering by leveraging the sparsity and connectivity of affinity graphs.
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Services
Conference Reviewer: ICLR 2025.
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