4 citations · 8 across the 13 of their papers we have counts for
14 papers
Differentially Private Paired Table-Image Multimodal Synthesis
Kai Chen, Josephine Lamp, Somesh Jha +1
Differentially private (DP) synthesis has been extensively studied for tabular and image data separately, yet many real-world datasets contain images paired with multivariate tabul…
DP-SAPF: Saliency-Aware Parameter Fine-tuning of Public Models for Differentially Private Image Synthesis
Chen Gong, Kecen Li, Zinan Lin +1
Differentially private (DP) image synthesis generates images that preserve the statistical characteristics of a sensitive dataset, enabling sensitive data analysis and usage while…
Differentially Private Contrastive Learning via Bounding Group-level Contribution
Kecen Li, Chen Gong, Zinan Lin +2
Differentially private (DP) contrastive learning aims to learn general-purpose representations from sensitive data, alleviating the privacy leakage concerns of organizations deploy…
HeteroFedSyn: Differentially Private Tabular Data Synthesis for Heterogeneous Federated Settings
Xiaochen Li, Fengyu Gao, Xizixiang Wei +3
Traditional Differential Privacy (DP) mechanisms are typically tailored to specific analysis tasks, which limits the reusability of protected data. DP tabular data synthesis overco…
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…
Beyond One-Size-Fits-All: Neural Networks for Differentially Private Tabular Data Synthesis
Kai Chen, Chen Gong, Tianhao Wang
In differentially private (DP) tabular data synthesis, the consensus is that statistical models are better than neural network (NN)-based methods. However, we argue that this concl…
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…
NetDPSyn: Synthesizing Network Traces under Differential Privacy
Danyu Sun, Joann Qiongna Chen, Chen Gong +2
As the utilization of network traces for the network measurement research becomes increasingly prevalent, concerns regarding privacy leakage from network traces have garnered the p…
TrajDeleter: Enabling Trajectory Forgetting in Offline Reinforcement Learning Agents
Chen Gong, Kecen Li, Jin Yao +1
Reinforcement learning (RL) trains an agent from experiences interacting with the environment. In scenarios where online interactions are impractical, offline RL, which trains the…
PrivImage: Differentially Private Synthetic Image Generation using Diffusion Models with Semantic-Aware Pretraining
Kecen Li, Chen Gong, Zhixiang Li +3
Differential Privacy (DP) image data synthesis, which leverages the DP technique to generate synthetic data to replace the sensitive data, allowing organizations to share and utili…