3 citations · 7 across the 14 of their papers we have counts for
6 papers · 1 filter
VI-Bench: Benchmarking Prompt Inversion from AIGC Videos
Wulin Xie, Rui Zhao, Kecen Li +5
Recent advances in video generation have made prompt-based control increasingly central to AIGC video generation. Prompts specify what a video should depict and how it should be re…
PrivCode++: Latent-Conditioned Differentially Private Code Generation for Comprehensive Guarantees
Zheng Liu, Chen Gong, Terry Yue Zhuo +6
Large language models fine-tuned on instruction-code pairs may memorize and subsequently leak sensitive training data. Existing differentially private (DP) code generation methods…
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…
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…