Raphaël Bost

1.5k citations
4 papers · 813 · 1 hit paper · h-index 4

Impact in

    • Cryptography and Data Security
    • Privacy-Preserving Technologies in Data
    • Internet Traffic Analysis and Secure E-voting
    • Adversarial Robustness in Machine Learning
    • Cloud Data Security Solutions
    • Blockchain Technology Applications and Security

Papers in

Raphaël Bost

4 papers receiving 801 citations

Raphaël Bost's Hit Papers

Machine Learning Classification over Encrypted Data 2015 · 400 citations
4000+3+7Years since publication100200300400

Peers

Raphaël Bost
Comparison fields: 5 of 51
  • Artificial Intelligence 754
  • Information Systems 267
  • Computational Theory and Mathematics 170
  • Computer Vision and Pattern Recognition 165
  • Health Informatics 5
Replace Daniel Demmler with:
Daniel Demmler Germany
Joppe W. Bos Belgium
M. Sadegh Riazi United States
Minxin Du China
Carmit Hazay Israel
Valeria Nikolaenko United States
Oriol Farràs Spain
Lior Malka United States
Hongbo Li China
Raphaël Bost relative to Daniel Demmler Germany Daniel Demmler's profile →
Citations per field
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Daniel Demmler · 1×
Citations per year

Countries citing papers authored by Raphaël Bost

Since Specialization
Citations

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

Fields of papers citing papers by Raphaël Bost

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 6 scholars most cited alongside Raphaël Bost, 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 Raphaël Bost Line = papers co-authored together Raphaël Bost links everyone, so they are left out of the graph.

All Works

4 of 4 papers shown

About Raphaël Bost

Raphaël Bost is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Computer Networks and Communications, Computer Vision and Pattern Recognition and Infectious Diseases, having authored 4 papers that have together received 813 indexed citations. Recurring topics across this work include Complexity and Algorithms in Graphs (3 papers), Cryptography and Data Security (3 papers), Privacy-Preserving Technologies in Data (2 papers), Network Security and Intrusion Detection (1 paper) and Chaos-based Image/Signal Encryption (1 paper). The work is most often cited by research in Artificial Intelligence (754 citations), Information Systems (267 citations), Computational Theory and Mathematics (170 citations), Computer Vision and Pattern Recognition (165 citations) and Health Informatics (5 citations). Raphaël Bost has collaborated with scholars based in France, United Kingdom and United States. Frequent co-authors include Raluca Ada Popa, Stephen Tu, Shafi Goldwasser, Brice Minaud, Olga Ohrimenko and Pierre-Alain Fouque. Their work appears in journals such as SHILAP Revista de lepidopterología.

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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