Jun Feng

2.2k citations
89 papers · 1.6k indexed · 1 hit paper · h-index 25
Topics
Cryptography and Data Security (20 papers)Privacy-Preserving Technologies in Data (17 papers)Cloud Data Security Solutions (8 papers)
Partner nations
ChinaCanadaUnited States

In The Last Decade

Jun Feng

84 papers receiving 1.6k citations

Hit Papers

Panther: Practical Secure Two-Party Neural Network Inference20252026202551015

Peers

Jun Feng
Comparison fields: 5 of 154
  • Artificial Intelligence 565
  • Computer Networks and Communications 320
  • Information Systems 312
  • Molecular Biology 268
  • Computer Vision and Pattern Recognition 235
Replace Zhigang Luo with:
Zhigang Luo China
Jia Zhang China
Martin Scholz Germany
Lin Wu China
Panpan Xu China
Felix Agakov United Kingdom
Xiujuan Wang China
Tingting He China
David W. Cheung Hong Kong
Xiaotong Lin China
Jun Feng relative to Zhigang Luo China Zhigang Luo's profile →
Citations per field
00.5×4.8×
Zhigang Luo · 1×
Citations per year

Countries citing papers authored by Jun Feng

Since Specialization
Citations

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

Fields of papers citing papers by Jun Feng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jun Feng

This figure shows the co-authorship network connecting the top 25 collaborators of Jun Feng. A scholar is included among the top collaborators of Jun Feng 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 Jun Feng. Jun Feng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 3
2 0
3 0
4 7
5 7
6 52
7 14
8 2
9 12
10 44
11 102
12 5
13 9
14 38
15 57
16 16
17 6
18
Authenticated tripartite key agreement protocol based on certificateless cryptography
1
19 33
20 4

About Jun Feng

Jun Feng is a scholar working on Computational Mathematics, Artificial Intelligence and Signal Processing, having authored 89 papers that have together received 1.6k indexed citations. Recurring topics across this work include Cryptography and Data Security (20 papers), Privacy-Preserving Technologies in Data (17 papers) and Cloud Data Security Solutions (8 papers). The work is most often cited by research in Computational Mathematics (73 citations), Biological Psychiatry (77 citations) and Artificial Intelligence (565 citations). Jun Feng has collaborated with scholars based in China, Canada and United States. Frequent co-authors include Laurence T. Yang, Xiaokang Wang, Daixing Zhou, Wei Zhu, Shusheng Li, Junshuai Wang, Kim‐Kwang Raymond Choo, Qing Zhu, Shunli Zhang and Qing Lü. Their work appears in journals such as International Journal of Molecular Sciences, Neuroscience and European Journal of Pharmacology.

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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