An Efficient Private GPT Never Autoregressively Decodes
Fuente:
arXiv
Saved in:
| Main Authors: | Li, Zhengyi, Guan, Yue, Yang, Kang, Feng, Yu, Liu, Ning, Yu, Yu, Leng, Jingwen, Guo, Minyi |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
On the (In-)Security of the Shuffling Defense in the Transformer Secure Inference
by: Li, Zhengyi, et al.
Published: (2026)
by: Li, Zhengyi, et al.
Published: (2026)
Nimbus: Secure and Efficient Two-Party Inference for Transformers
by: Li, Zhengyi, et al.
Published: (2024)
by: Li, Zhengyi, et al.
Published: (2024)
Privacy Profiles for Private Selection
by: Koskela, Antti, et al.
Published: (2024)
by: Koskela, Antti, et al.
Published: (2024)
DictPFL: Efficient and Private Federated Learning on Encrypted Gradients
by: Xue, Jiaqi, et al.
Published: (2025)
by: Xue, Jiaqi, et al.
Published: (2025)
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners
by: Redberg, Rachel, et al.
Published: (2023)
by: Redberg, Rachel, et al.
Published: (2023)
Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols
by: He, Longzhu, et al.
Published: (2025)
by: He, Longzhu, et al.
Published: (2025)
Private and Communication-Efficient Federated Learning based on Differentially Private Sketches
by: Zhang, Meifan, et al.
Published: (2024)
by: Zhang, Meifan, et al.
Published: (2024)
Efficient Differentially Private Fine-Tuning of Diffusion Models
by: Liu, Jing, et al.
Published: (2024)
by: Liu, Jing, et al.
Published: (2024)
Data Poisoning Attacks to Locally Differentially Private Range Query Protocols
by: Liao, Ting-Wei, et al.
Published: (2025)
by: Liao, Ting-Wei, et al.
Published: (2025)
IstGPT: LLM-based Anomaly Detection for Spatial-Temporal Graph in Industrial Systems
by: Zhang, Yuchen, et al.
Published: (2026)
by: Zhang, Yuchen, et al.
Published: (2026)
Fuzz-Testing Meets LLM-Based Agents: An Automated and Efficient Framework for Jailbreaking Text-To-Image Generation Models
by: Dong, Yingkai, et al.
Published: (2024)
by: Dong, Yingkai, et al.
Published: (2024)
Selective Pre-training for Private Fine-tuning
by: Yu, Da, et al.
Published: (2023)
by: Yu, Da, et al.
Published: (2023)
Scaling Laws for Differentially Private Language Models
by: McKenna, Ryan, et al.
Published: (2025)
by: McKenna, Ryan, et al.
Published: (2025)
CipherPrune: Efficient and Scalable Private Transformer Inference
by: Zhang, Yancheng, et al.
Published: (2025)
by: Zhang, Yancheng, et al.
Published: (2025)
Efficient Public Verification of Private ML via Regularization
by: Bell, Zoë Ruha, et al.
Published: (2025)
by: Bell, Zoë Ruha, et al.
Published: (2025)
Oracle-Efficient Differentially Private Learning with Public Data
by: Block, Adam, et al.
Published: (2024)
by: Block, Adam, et al.
Published: (2024)
ACTG-ARL: Differentially Private Conditional Text Generation with RL-Boosted Control
by: Hu, Yuzheng, et al.
Published: (2025)
by: Hu, Yuzheng, et al.
Published: (2025)
N-GLARE: An Non-Generative Latent Representation-Efficient LLM Safety Evaluator
by: Lin, Zheyu, et al.
Published: (2025)
by: Lin, Zheyu, et al.
Published: (2025)
Private-RAG: Answering Multiple Queries with LLMs while Keeping Your Data Private
by: Wu, Ruihan, et al.
Published: (2025)
by: Wu, Ruihan, et al.
Published: (2025)
Universal Exact Compression of Differentially Private Mechanisms
by: Liu, Yanxiao, et al.
Published: (2024)
by: Liu, Yanxiao, et al.
Published: (2024)
Private, Efficient and Scalable Kernel Learning for Medical Image Analysis
by: Hannemann, Anika, et al.
Published: (2024)
by: Hannemann, Anika, et al.
Published: (2024)
All Rivers Run to the Sea: Private Learning with Asymmetric Flows
by: Niu, Yue, et al.
Published: (2023)
by: Niu, Yue, et al.
Published: (2023)
DPDR: Gradient Decomposition and Reconstruction for Differentially Private Deep Learning
by: Liu, Yixuan, et al.
Published: (2024)
by: Liu, Yixuan, et al.
Published: (2024)
Differentially Private Attention Computation
by: Gao, Yeqi, et al.
Published: (2023)
by: Gao, Yeqi, et al.
Published: (2023)
TAPAS: Efficient Two-Server Asymmetric Private Aggregation Beyond Prio(+)
by: Karthikeyan, Harish, et al.
Published: (2026)
by: Karthikeyan, Harish, et al.
Published: (2026)
Differentially-Private Data Synthetisation for Efficient Re-Identification Risk Control
by: Carvalho, Tânia, et al.
Published: (2022)
by: Carvalho, Tânia, et al.
Published: (2022)
PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning
by: Feng, Yu, et al.
Published: (2025)
by: Feng, Yu, et al.
Published: (2025)
Optimal Survey Design for Private Mean Estimation
by: Chen, Yu-Wei, et al.
Published: (2025)
by: Chen, Yu-Wei, et al.
Published: (2025)
Locally Private Estimation with Public Features
by: Ma, Yuheng, et al.
Published: (2024)
by: Ma, Yuheng, et al.
Published: (2024)
Pencil: Private and Extensible Collaborative Learning without the Non-Colluding Assumption
by: Liu, Xuanqi, et al.
Published: (2024)
by: Liu, Xuanqi, et al.
Published: (2024)
FlashDP: Private Training Large Language Models with Efficient DP-SGD
by: Wang, Liangyu, et al.
Published: (2025)
by: Wang, Liangyu, et al.
Published: (2025)
PrivORL: Differentially Private Synthetic Dataset for Offline Reinforcement Learning
by: Gong, Chen, et al.
Published: (2025)
by: Gong, Chen, et al.
Published: (2025)
Private Linear Regression with Differential Privacy and PAC Privacy
by: Yang, Hillary, et al.
Published: (2024)
by: Yang, Hillary, et al.
Published: (2024)
Optimal Locally Private Nonparametric Classification with Public Data
by: Ma, Yuheng, et al.
Published: (2023)
by: Ma, Yuheng, et al.
Published: (2023)
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM
by: Xie, Chulin, et al.
Published: (2022)
by: Xie, Chulin, et al.
Published: (2022)
Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning
by: Jiang, Yu, et al.
Published: (2024)
by: Jiang, Yu, et al.
Published: (2024)
Agentic Privacy-Preserving Machine Learning
by: Zhang, Mengyu, et al.
Published: (2025)
by: Zhang, Mengyu, et al.
Published: (2025)
DeepShare: Sharing ReLU Across Channels and Layers for Efficient Private Inference
by: Bornfeld, Yonathan, et al.
Published: (2025)
by: Bornfeld, Yonathan, et al.
Published: (2025)
AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing
by: Zhou, Tong, et al.
Published: (2024)
by: Zhou, Tong, et al.
Published: (2024)
PGD-Imp: Rethinking and Unleashing Potential of Classic PGD with Dual Strategies for Imperceptible Adversarial Attacks
by: Li, Jin, et al.
Published: (2024)
by: Li, Jin, et al.
Published: (2024)
Similar Items
-
On the (In-)Security of the Shuffling Defense in the Transformer Secure Inference
by: Li, Zhengyi, et al.
Published: (2026) -
Nimbus: Secure and Efficient Two-Party Inference for Transformers
by: Li, Zhengyi, et al.
Published: (2024) -
Privacy Profiles for Private Selection
by: Koskela, Antti, et al.
Published: (2024) -
DictPFL: Efficient and Private Federated Learning on Encrypted Gradients
by: Xue, Jiaqi, et al.
Published: (2025) -
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners
by: Redberg, Rachel, et al.
Published: (2023)