Daniel Rausch
Impact in
- Information Systems top 10%
- Blockchain Technology Applications and Security
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- Cryptography and Data Security
- Privacy-Preserving Technologies in Data
- Security and Verification in Computing
- Cryptographic Implementations and Security
Papers in
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- Cryptography and Data Security 7
- Privacy-Preserving Technologies in Data 1
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- Blockchain Technology Applications and Security 3
- User Authentication and Security Systems 2
- Co-authors
- Ralf Küsters (7 shared papers)Christoph Egger (1 shared paper)Dominique Schröder (1 shared paper)Rainer Lutz (1 shared paper)Fabian Beck (1 shared paper)Stephan Diehl (1 shared paper)Nicolas Huber (1 shared paper)A. Vogt (1 shared paper)
- Journals
- Journal of Cryptology (2 papers)Critical Care Medicine (1 paper)Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (1 paper)
- Partner nations
- GermanySwitzerlandEstonia
In The Last Decade
Daniel Rausch
9 papers receiving 98 citations
Peers
Comparison fields: 5 of 27
- Information Systems 58
- Artificial Intelligence 60
- Computer Networks and Communications 32
- Signal Processing 12
- Computer Vision and Pattern Recognition 12
Countries citing papers authored by Daniel Rausch
This map shows the geographic impact of Daniel Rausch'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 Daniel Rausch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Rausch more than expected).
Fields of papers citing papers by Daniel Rausch
This network shows the impact of papers produced by Daniel Rausch. 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 Daniel Rausch. The network helps show where Daniel Rausch may publish in the future.
Co-authors
The 9 scholars most cited alongside Daniel Rausch, 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 | 32 | |
| 2 | 2020 | 15 | |
| 3 | 2021 | 12 | |
| 4 | 2022 | 12 | |
| 5 | 2017 | 11 | |
| 6 | 2022 | 9 | |
| 7 | 2014 | 7 | |
| 8 | 2023 | 3 | |
| 9 | 2019 | 1 | |
| 10 | 2024 | 0 | |
| 11 | 2020 | 0 |
About Daniel Rausch
Daniel Rausch is a scholar working on Artificial Intelligence, Information Systems, Computer Networks and Communications, Signal Processing and Pulmonary and Respiratory Medicine, having authored 11 papers that have together received 102 indexed citations. Recurring topics across this work include Cryptography and Data Security (7 papers), Advanced Authentication Protocols Security (3 papers), Blockchain Technology Applications and Security (3 papers), Advanced Malware Detection Techniques (2 papers), User Authentication and Security Systems (2 papers), Vehicular Ad Hoc Networks (VANETs) (1 paper), Privacy-Preserving Technologies in Data (1 paper) and Complexity and Algorithms in Graphs (1 paper). The work is most often cited by research in Information Systems (58 citations), Artificial Intelligence (60 citations), Computer Networks and Communications (32 citations), Signal Processing (12 citations) and Computer Vision and Pattern Recognition (12 citations). Daniel Rausch has collaborated with scholars based in Germany, Switzerland and Estonia. Frequent co-authors include Ralf Küsters, Christoph Egger, Dominique Schröder, Rainer Lutz, Fabian Beck, Stephan Diehl, Nicolas Huber, A. Vogt and Candace K. Chan. Their work appears in journals such as Journal of Cryptology, Critical Care Medicine and Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security.
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.