300 citations · 581 across the 15 of their papers we have counts for
15 papers
Inside the Black Box: Detecting Data Leakage in Pre-trained Language Encoders
Yuan Xin, Zheng Li, Ning Yu +4
Despite being prevalent in the general field of Natural Language Processing (NLP), pre-trained language models inherently carry privacy and copyright concerns due to their nature o…
PoLLMgraph: Unraveling Hallucinations in Large Language Models via State Transition Dynamics
Derui Zhu, Dingfan Chen, Qing Li +4
Despite tremendous advancements in large language models (LLMs) over recent years, a notably urgent challenge for their practical deployment is the phenomenon of hallucination, whe…
Towards Biologically Plausible and Private Gene Expression Data Generation
Dingfan Chen, Marie Oestreich, Tejumade Afonja +3
Generative models trained with Differential Privacy (DP) are becoming increasingly prominent in the creation of synthetic data for downstream applications. Existing literature, how…
A Unified View of Differentially Private Deep Generative Modeling
Dingfan Chen, Raouf Kerkouche, Mario Fritz
The availability of rich and vast data sources has greatly advanced machine learning applications in various domains. However, data with privacy concerns comes with stringent regul…
MargCTGAN: A "Marginally'' Better CTGAN for the Low Sample Regime
Tejumade Afonja, Dingfan Chen, Mario Fritz
The potential of realistic and useful synthetic data is significant. However, current evaluation methods for synthetic tabular data generation predominantly focus on downstream tas…
Data Forensics in Diffusion Models: A Systematic Analysis of Membership Privacy
Derui Zhu, Dingfan Chen, Jens Grossklags +1
In recent years, diffusion models have achieved tremendous success in the field of image generation, becoming the stateof-the-art technology for AI-based image processing applicati…
FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations
Hui-Po Wang, Dingfan Chen, Raouf Kerkouche +1
Conventional gradient-sharing approaches for federated learning (FL), such as FedAvg, rely on aggregation of local models and often face performance degradation under differential…
Private Set Generation with Discriminative Information
Dingfan Chen, Raouf Kerkouche, Mario Fritz
Differentially private data generation techniques have become a promising solution to the data privacy challenge -- it enables sharing of data while complying with rigorous privacy…
Strongly Augmented Contrastive Clustering
Xiaozhi Deng, Dong Huang, Ding-Hua Chen +2
Deep clustering has attracted increasing attention in recent years due to its capability of joint representation learning and clustering via deep neural networks. In its latest dev…
RelaxLoss: Defending Membership Inference Attacks without Losing Utility
Dingfan Chen, Ning Yu, Mario Fritz
As a long-term threat to the privacy of training data, membership inference attacks (MIAs) emerge ubiquitously in machine learning models. Existing works evidence strong connection…
Vision Transformer for Contrastive Clustering
Hua-Bao Ling, Bowen Zhu, Dong Huang +3
Vision Transformer (ViT) has shown its advantages over the convolutional neural network (CNN) with its ability to capture global long-range dependencies for visual representation l…
Responsible Disclosure of Generative Models Using Scalable Fingerprinting
Ning Yu, Vladislav Skripniuk, Dingfan Chen +2
Over the past years, deep generative models have achieved a new level of performance. Generated data has become difficult, if not impossible, to be distinguished from real data. Wh…
GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators
Dingfan Chen, Tribhuvanesh Orekondy, Mario Fritz
The wide-spread availability of rich data has fueled the growth of machine learning applications in numerous domains. However, growth in domains with highly-sensitive data (e.g., m…
BadNL: Backdoor Attacks against NLP Models with Semantic-preserving Improvements
Xiaoyi Chen, Ahmed Salem, Dingfan Chen +5
Deep neural networks (DNNs) have progressed rapidly during the past decade and have been deployed in various real-world applications. Meanwhile, DNN models have been shown to be vu…
GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models
Dingfan Chen, Ning Yu, Yang Zhang +1
Deep learning has achieved overwhelming success, spanning from discriminative models to generative models. In particular, deep generative models have facilitated a new level of per…