Zhenxing Qian

Invertible Image Dataset Protection

2021 · arXiv (Cornell University)

Deep learning has achieved enormous success in various industrial applications. Companies do not want their valuable data to be stolen by malicious employees to train pirated models. Nor do they wish the data analyzed by …

Model Copyright Protection in Buyer-seller Environment

2023 · arXiv (Cornell University)

Training a deep neural network (DNN) requires a high computational cost. Buying models from sellers with a large number of computing resources has become prevailing. However, the buyer-seller environment is not always trusted. To protect …

Privacy-Preserving Student Learning with Differentially Private Data-Free Distillation

2024 · arXiv (Cornell University)

Deep learning models can achieve high inference accuracy by extracting rich knowledge from massive well-annotated data, but may pose the risk of data privacy leakage in practical deployment. In this paper, we present an effective …