Dingfan Chen
Impact in
- Health Informatics top 10%
- Artificial Intelligence top 5%
- Privacy-Preserving Technologies in Data
- Adversarial Robustness in Machine Learning
- Cryptography and Data Security
- Anomaly Detection Techniques and Applications
- Topic Modeling
Papers in
-
- Privacy-Preserving Technologies in Data 4
- Adversarial Robustness in Machine Learning 3
- Cryptography and Data Security 1
- Machine Learning in Healthcare 1
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- Generative Adversarial Networks and Image Synthesis 1
- Co-authors
- Mario Fritz (7 shared papers)Ning Yu (2 shared papers)Yang Zhang (1 shared paper)Tribhuvanesh Orekondy (1 shared paper)Qingni Shen (1 shared paper)Zhonghai Wu (1 shared paper)Xiaoyi Chen (1 shared paper)Ahmed Salem (1 shared paper)
- Journals
- Proceedings on Privacy Enhancing Technologies (2 papers)PubMed (1 paper)Fraunhofer-Publica (Fraunhofer-Gesellschaft) (1 paper)Annual Computer Security Applications Conference (1 paper)Figshare (1 paper)
- Partner nations
- GermanyUnited StatesChina
In The Last Decade
Dingfan Chen
7 papers receiving 316 citations
Peers
Comparison fields: 5 of 46
- Health Informatics 13
- Artificial Intelligence 274
- Signal Processing 39
- Computer Vision and Pattern Recognition 72
- Computational Mathematics 1
Countries citing papers authored by Dingfan Chen
This map shows the geographic impact of Dingfan Chen's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dingfan Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dingfan Chen more than expected).
Fields of papers citing papers by Dingfan Chen
This network shows the impact of papers produced by Dingfan Chen. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dingfan Chen. The network helps show where Dingfan Chen may publish in the future.
Co-authors
The 19 scholars most cited alongside Dingfan Chen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 158 | |
| 2 | 2021 | 85 | |
| 3 | 2020 | 45 | |
| 4 | 2021 | 17 | |
| 5 | GAN-Leaks: A Taxonomy of Membership Inference Attacks against GANs | 2019 | 14 |
| 6 | 2024 | 2 | |
| 7 | 2024 | 1 | |
| 8 | 2024 | 0 | |
| 9 | 2024 | 0 | |
| 10 | 2022 | 0 |
About Dingfan Chen
Dingfan Chen is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Sociology and Political Science and Public Health, Environmental and Occupational Health, having authored 10 papers that have together received 322 indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (4 papers), Adversarial Robustness in Machine Learning (3 papers), Cancer Genomics and Diagnostics (1 paper), Access Control and Trust (1 paper), Cryptography and Data Security (1 paper), Mental Health Research Topics (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper) and Machine Learning in Healthcare (1 paper). The work is most often cited by research in Health Informatics (13 citations), Artificial Intelligence (274 citations), Signal Processing (39 citations), Computer Vision and Pattern Recognition (72 citations) and Computational Mathematics (1 citation). Dingfan Chen has collaborated with scholars based in Germany, United States and China. Frequent co-authors include Mario Fritz, Ning Yu, Yang Zhang, Tribhuvanesh Orekondy, Qingni Shen, Zhonghai Wu, Xiaoyi Chen, Ahmed Salem, Shiqing Ma and Michael Backes. Their work appears in journals such as Proceedings on Privacy Enhancing Technologies, PubMed, Fraunhofer-Publica (Fraunhofer-Gesellschaft), Annual Computer Security Applications Conference and Figshare.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.