Christoph Bösch

1.3k citations
19 papers · 513 indexed · h-index 7

Christoph Bösch

18 papers receiving 492 citations

Peers

Christoph Bösch
Comparison fields: 5 of 64
  • Information Systems 216
  • Artificial Intelligence 302
  • Human-Computer Interaction 31
  • Computer Vision and Pattern Recognition 74
  • Signal Processing 36
Replace Maunendra Sankar Desarkar with:
Maunendra Sankar Desarkar India
Jorge García‐Duque Spain
Loredana Parasiliti Provenza Italy
Jack Muramatsu United States
Mohammad Alia Jordan
Maya Haridasan United States
Fabio Gasparetti Italy
Max Goldman United States
Shuguang Han United States
Manuel Ramos‐Cabrer Spain
Christoph Bösch relative to Maunendra Sankar Desarkar India Maunendra Sankar Desarkar's profile →
Citations per field
00.5×3.8×
Maunendra Sankar Desarkar · 1×
Citations per year

Countries citing papers authored by Christoph Bösch

Since Specialization
Citations

This map shows the geographic impact of Christoph Bösch'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 Christoph Bösch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christoph Bösch more than expected).

Fields of papers citing papers by Christoph Bösch

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Christoph Bösch. 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 Christoph Bösch. The network helps show where Christoph Bösch may publish in the future.

Co-authorship network

The 24 scholars most cited alongside Christoph Bösch, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Christoph Bösch Line = papers co-authored together Christoph Bösch links everyone, so they are left out of the graph.

All Works

19 of 19 papers shown
#Work
1 20240
2 20221
3 20221
4 20214
5 20214
6 20212
7 20211
8 20191
9 201912
10 201924
11 20191
12 20184
13 20183
14 201736
15 2016177
16 2014204
17 20145
18 201421
19 201212

About Christoph Bösch

Christoph Bösch is a scholar working on Artificial Intelligence, Signal Processing and Information Systems, having authored 19 papers that have together received 513 indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (7 papers), Privacy, Security, and Data Protection (6 papers), Cryptography and Data Security (5 papers), Blockchain Technology Applications and Security (3 papers), Advanced Malware Detection Techniques (3 papers), Cryptographic Implementations and Security (2 papers), Internet Traffic Analysis and Secure E-voting (2 papers) and Advanced Steganography and Watermarking Techniques (2 papers). The work is most often cited by research in Information Systems (216 citations), Artificial Intelligence (302 citations) and Human-Computer Interaction (31 citations). Christoph Bösch has collaborated with scholars based in Germany, Netherlands and Belgium. Frequent co-authors include Peter Andreas, Pieter Hartel, Willem Jonker, Frank Kargl, Henning Kopp, Benjamin Erb, Stefan Pfattheicher, Ala’a M. Al-Momani, Franz J. Hauck and Michael Feiri. Their work appears in journals such as SHILAP Revista de lepidopterología, ACM Computing Surveys and Frontiers in Big Data.

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.

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