1 citations · 1 across the 4 of their papers we have counts for
7 papers
PrivCode: When Code Generation Meets Differential Privacy
Zheng Liu, Chen Gong, Terry Yue Zhuo +4
Large language models (LLMs) have presented outstanding performance in code generation and completion. However, fine-tuning these models on private datasets can raise privacy and p…
From Easy to Hard++: Promoting Differentially Private Image Synthesis Through Spatial-Frequency Curriculum
Chen Gong, Kecen Li, Zinan Lin +1
To improve the quality of Differentially private (DP) synthetic images, most studies have focused on improving the core optimization techniques (e.g., DP-SGD). Recently, we have wi…
PrivORL: Differentially Private Synthetic Dataset for Offline Reinforcement Learning
Chen Gong, Zheng Liu, Kecen Li +1
Recently, offline reinforcement learning (RL) has become a popular RL paradigm. In offline RL, data providers share pre-collected datasets -- either as individual transitions or se…
Benchmarking Differentially Private Tabular Data Synthesis
Kai Chen, Xiaochen Li, Chen Gong +2
Differentially private (DP) tabular data synthesis generates artificial data that preserves the statistical properties of private data while safeguarding individual privacy. The em…
From Easy to Hard: Building a Shortcut for Differentially Private Image Synthesis
Kecen Li, Chen Gong, Xiaochen Li +3
Differentially private (DP) image synthesis aims to generate synthetic images from a sensitive dataset, alleviating the privacy leakage concerns of organizations sharing and utiliz…
DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis
Chen Gong, Kecen Li, Zinan Lin +1
Differentially private (DP) image synthesis aims to generate artificial images that retain the properties of sensitive images while protecting the privacy of individual images with…