3 papers
cs.CR2026
Rethinking the Security of DP-SGD: A Corrected Analysis of Differentially Private Machine Learning
Wenhao Wang, Shujie Cui, Hui Cui +1
Differentially Private Stochastic Gradient Descent (DP-SGD) is widely used to protect training data in machine learning. Its privacy guarantee is commonly analyzed through a securi…
cs.CR2025
CryptPEFT: Efficient and Private Neural Network Inference via Parameter-Efficient Fine-Tuning
Saisai Xia, Wenhao Wang, Zihao Wang +4
Publicly available large pretrained models (i.e., backbones) and lightweight adapters for parameter-efficient fine-tuning (PEFT) have become standard components in modern machine l…
cs.CR2025
Comet: Accelerating Private Inference for Large Language Model by Predicting Activation Sparsity
Guang Yan, Yuhui Zhang, Zimu Guo +6
With the growing use of large language models (LLMs) hosted on cloud platforms to offer inference services, privacy concerns about the potential leakage of sensitive information ar…