Kim Laine

60 total papers · 3.8k total citations
21 papers, 1.7k citations indexed

About

Kim Laine is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Kim Laine has authored 21 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 6 papers in Computational Theory and Mathematics and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Kim Laine's work include Cryptography and Data Security (17 papers), Privacy-Preserving Technologies in Data (11 papers) and Cryptographic Implementations and Security (5 papers). Kim Laine is often cited by papers focused on Cryptography and Data Security (17 papers), Privacy-Preserving Technologies in Data (11 papers) and Cryptographic Implementations and Security (5 papers). Kim Laine collaborates with scholars based in United States, United Kingdom and Switzerland. Kim Laine's co-authors include Kristin Lauter, Ran Gilad-Bachrach, John Wernsing, Nathan Dowlin, Michael Naehrig, Hao Chen, Peter Rindal, Wei Dai, M. Sadegh Riazi and Zhicong Huang and has published in prestigious journals such as Proceedings of the IEEE, IEEE Access and IEEE Security & Privacy.

In The Last Decade

Kim Laine

21 papers receiving 1.6k citations

Hit Papers

CryptoNets: applying neur... 2016 2026 2019 2022 2016 250 500 750

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Kim Laine 1.5k 361 261 228 177 21 1.7k
Michael Naehrig 1.9k 1.3× 630 1.7× 385 1.5× 276 1.2× 223 1.3× 22 2.1k
Yupeng Zhang 1.4k 0.9× 381 1.1× 251 1.0× 168 0.7× 239 1.4× 41 1.7k
Ronald Cramer 1.7k 1.1× 558 1.5× 277 1.1× 380 1.7× 391 2.2× 46 1.9k
Payman Mohassel 1.5k 1.0× 294 0.8× 147 0.6× 167 0.7× 153 0.9× 19 1.6k
Matt J. Kusner 1.1k 0.7× 246 0.7× 314 1.2× 126 0.6× 73 0.4× 26 1.7k
Tanja Lange 1.2k 0.8× 627 1.7× 290 1.1× 182 0.8× 269 1.5× 44 1.4k
Alessandro Chiesa 1.3k 0.9× 1.3k 3.6× 194 0.7× 219 1.0× 348 2.0× 27 1.8k
Galileo Namata 2.0k 1.3× 390 1.1× 458 1.8× 77 0.3× 199 1.1× 18 2.3k
Emil Stefanov 1.9k 1.3× 952 2.6× 200 0.8× 354 1.6× 404 2.3× 26 2.2k
Xavier Boyen 942 0.6× 552 1.5× 204 0.8× 281 1.2× 294 1.7× 41 1.3k

Countries citing papers authored by Kim Laine

Since Specialization
Citations

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

Fields of papers citing papers by Kim Laine

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kim Laine

This figure shows the co-authorship network connecting the top 25 collaborators of Kim Laine. A scholar is included among the top collaborators of Kim Laine based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Kim Laine. Kim Laine is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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