Chan Fook Mun

487 total citations
6 papers, 268 citations indexed

About

Chan Fook Mun is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Chan Fook Mun has authored 6 papers receiving a total of 268 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 2 papers in Information Systems and 1 paper in Computer Vision and Pattern Recognition. Recurrent topics in Chan Fook Mun's work include Cryptography and Data Security (5 papers), Cryptographic Implementations and Security (3 papers) and Privacy-Preserving Technologies in Data (3 papers). Chan Fook Mun is often cited by papers focused on Cryptography and Data Security (5 papers), Cryptographic Implementations and Security (3 papers) and Privacy-Preserving Technologies in Data (3 papers). Chan Fook Mun collaborates with scholars based in Singapore and United States. Chan Fook Mun's co-authors include Khin Mi Mi Aung, Ahmad Al Badawi, Bharadwaj Veeravalli, Benjamin Hong Meng Tan, Vijay Chandrasekhar, Chao Jin, Kim Laine, Nan Xiao, Jie Lin and Jayashree Kalpathy–Cramer and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and IEEE Transactions on Information Forensics and Security.

In The Last Decade

Chan Fook Mun

6 papers receiving 261 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Chan Fook Mun Singapore	5	236	78	49	41	33	6	268
Benjamin Hong Meng Tan Singapore	6	200 0.8×	74 0.9×	36 0.7×	49 1.2×	37 1.1×	18	237
Yarkın Doröz United States	10	317 1.3×	176 2.3×	65 1.3×	38 0.9×	36 1.1×	11	353
Samee Zahur United States	4	240 1.0×	69 0.9×	46 0.9×	17 0.4×	38 1.2×	4	262
Fabian Boemer United States	7	174 0.7×	35 0.4×	21 0.4×	43 1.0×	26 0.8×	8	218
Paulo Martins Portugal	8	197 0.8×	135 1.7×	50 1.0×	47 1.1×	55 1.7×	18	280
Jack Doerner United States	7	341 1.4×	122 1.6×	62 1.3×	25 0.6×	63 1.9×	9	377
Jieun Eom South Korea	4	201 0.9×	65 0.8×	19 0.4×	48 1.2×	27 0.8×	5	235
Damien Vergnaud France	8	243 1.0×	124 1.6×	46 0.9×	35 0.9×	65 2.0×	26	280
Wilko Henecka Australia	5	208 0.9×	59 0.8×	50 1.0×	26 0.6×	28 0.8×	7	225
Eiichiro Fujisaki Japan	6	204 0.9×	79 1.0×	57 1.2×	43 1.0×	39 1.2×	19	225

Countries citing papers authored by Chan Fook Mun

Since Specialization

Citations

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

Fields of papers citing papers by Chan Fook Mun

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chan Fook Mun. 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 Chan Fook Mun. The network helps show where Chan Fook Mun may publish in the future.

Co-authorship network of co-authors of Chan Fook Mun

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

All Works

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6 of 6 papers shown

1.

Tan, Benjamin Hong Meng, et al.. (2021). DOReN: Toward Efficient Deep Convolutional Neural Networks with Fully Homomorphic Encryption. IEEE Transactions on Information Forensics and Security. 16. 3740–3752. 34 indexed citations

2.

Badawi, Ahmad Al, et al.. (2020). PrivFT: Private and Fast Text Classification With Homomorphic Encryption. IEEE Access. 8. 226544–226556. 57 indexed citations

3.

Badawi, Ahmad Al, Chao Jin, Jie Lin, et al.. (2020). Towards the AlexNet Moment for Homomorphic Encryption: HCNN, the First Homomorphic CNN on Encrypted Data With GPUs. IEEE Transactions on Emerging Topics in Computing. 9(3). 1330–1343. 100 indexed citations

4.

Badawi, Ahmad Al, Jie Lin, Chan Fook Mun, et al.. (2019). CareNets: Efficient Homomorphic CNN for High Resolution Images. 3 indexed citations

5.

Badawi, Ahmad Al, Bharadwaj Veeravalli, Chan Fook Mun, & Khin Mi Mi Aung. (2018). High-Performance FV Somewhat Homomorphic Encryption on GPUs: An Implementation using CUDA. SHILAP Revista de lepidopterología. 21 indexed citations

6.

Badawi, Ahmad Al, Bharadwaj Veeravalli, Chan Fook Mun, & Khin Mi Mi Aung. (2018). High-Performance FV Somewhat Homomorphic Encryption on GPUs: An Implementation using CUDA. IACR Transactions on Cryptographic Hardware and Embedded Systems. 70–95. 53 indexed citations

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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