15 citations · 20 across the 4 of their papers we have counts for
4 papers
SecFormer: Fast and Accurate Privacy-Preserving Inference for Transformer Models via SMPC
Jinglong Luo, Yehong Zhang, Zhuo Zhang +5
With the growing use of Transformer models hosted on cloud platforms to offer inference services, privacy concerns are escalating, especially concerning sensitive data like investm…
Model Provenance via Model DNA
Xin Mu, Yu Wang, Yehong Zhang +4
Understanding the life cycle of the machine learning (ML) model is an intriguing area of research (e.g., understanding where the model comes from, how it is trained, and how it is…
Practical Privacy-Preserving Gaussian Process Regression via Secret Sharing
Jinglong Luo, Yehong Zhang, Jiaqi Zhang +4
Gaussian process regression (GPR) is a non-parametric model that has been used in many real-world applications that involve sensitive personal data (e.g., healthcare, finance, etc.…
Efficient Private ERM for Smooth Objectives
Jiaqi Zhang, Kai Zheng, Wenlong Mou +1
In this paper, we consider efficient differentially private empirical risk minimization from the viewpoint of optimization algorithms. For strongly convex and smooth objectives, we…