Phillip Rieger
Impact in
- Artificial Intelligence top 10%
- Privacy-Preserving Technologies in Data
- Adversarial Robustness in Machine Learning
- Internet Traffic Analysis and Secure E-voting
- Cryptography and Data Security
- Anomaly Detection Techniques and Applications
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- Network Security and Intrusion Detection
Papers in
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- Privacy-Preserving Technologies in Data 6
- Adversarial Robustness in Machine Learning 4
- Cryptography and Data Security 2
- Internet Traffic Analysis and Secure E-voting 2
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- Network Security and Intrusion Detection 2
- Co-authors
- Ahmad‐Reza Sadeghi (9 shared papers)Markus Miettinen (4 shared papers)Thien Duc Nguyen (2 shared papers)Hossein Fereidooni (6 shared papers)Alexandra Dmitrienko (4 shared papers)Murtuza Jadliwala (2 shared papers)Qian Chen (1 shared paper)Azalia Mirhoseini (1 shared paper)
- Journals
- Radboud Repository (Radboud University) (1 paper)Annual Computer Security Applications Conference (1 paper)TUbilio (Technical University of Darmstadt) (5 papers)
- Partner nations
- GermanyUnited StatesNetherlands
In The Last Decade
Phillip Rieger
9 papers receiving 174 citations
Peers
Comparison fields: 5 of 31
- Artificial Intelligence 138
- Computer Networks and Communications 76
- Signal Processing 31
- Health Informatics 2
- Information Systems 27
Countries citing papers authored by Phillip Rieger
This map shows the geographic impact of Phillip Rieger'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 Phillip Rieger with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Phillip Rieger more than expected).
Fields of papers citing papers by Phillip Rieger
This network shows the impact of papers produced by Phillip Rieger. 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 Phillip Rieger. The network helps show where Phillip Rieger may publish in the future.
Co-authors
The 14 scholars most cited alongside Phillip Rieger, 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 | 119 | |
| 2 | 2023 | 13 | |
| 3 | 2022 | 11 | |
| 4 | 2023 | 9 | |
| 5 | 2024 | 8 | |
| 6 | 2023 | 7 | |
| 7 | 2024 | 6 | |
| 8 | 2023 | 2 | |
| 9 | BAFFLE: TOWARDS RESOLVING FEDERATED LEARNING’S DILEMMA - THWARTING BACKDOOR AND INFERENCE ATTACKS | 2021 | 1 |
| 10 | 2025 | 0 |
About Phillip Rieger
Phillip Rieger is a scholar working on Artificial Intelligence, Computer Networks and Communications, Sociology and Political Science, Information Systems and Signal Processing, having authored 10 papers that have together received 176 indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (6 papers), Adversarial Robustness in Machine Learning (4 papers), Cryptography and Data Security (2 papers), Privacy, Security, and Data Protection (2 papers), Network Security and Intrusion Detection (2 papers), Internet Traffic Analysis and Secure E-voting (2 papers), Speech and Audio Processing (1 paper) and User Authentication and Security Systems (1 paper). The work is most often cited by research in Artificial Intelligence (138 citations), Computer Networks and Communications (76 citations), Signal Processing (31 citations), Health Informatics (2 citations) and Information Systems (27 citations). Phillip Rieger has collaborated with scholars based in Germany, United States and Netherlands. Frequent co-authors include Ahmad‐Reza Sadeghi, Markus Miettinen, Thien Duc Nguyen, Hossein Fereidooni, Alexandra Dmitrienko, Murtuza Jadliwala, Qian Chen, Azalia Mirhoseini, Samuel Marchal and Thomas Schneider. Their work appears in journals such as Radboud Repository (Radboud University), Annual Computer Security Applications Conference and TUbilio (Technical University of Darmstadt).
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.