Jieun Eom
Impact in
- Artificial Intelligence top 10%
- Cryptography and Data Security
- Privacy-Preserving Technologies in Data
- Cryptographic Implementations and Security
- Adversarial Robustness in Machine Learning
- Stochastic Gradient Optimization Techniques
Papers in
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- Cryptography and Data Security 5
- Privacy-Preserving Technologies in Data 2
- Coding theory and cryptography 1
- Cryptographic Implementations and Security 1
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- Complexity and Algorithms in Graphs 3
- Co-authors
- Yongwoo Lee (2 shared papers)Maxim Deryabin (2 shared papers)Joon-Woo Lee (1 shared paper)HyungChul Kang (1 shared paper)Donghoon Yoo (1 shared paper)Jong‐Seon No (1 shared paper)Young Sik Kim (1 shared paper)Eunsang Lee (1 shared paper)
- Journals
- IEEE Access (2 papers)IEEE Transactions on Computers (1 paper)Journal of Medical Systems (1 paper)Information Security and Cryptology (1 paper)
- Partner nations
- South Korea
In The Last Decade
Jieun Eom
5 papers receiving 224 citations
Jieun Eom's Hit Papers
Peers
Comparison fields: 5 of 39
- Artificial Intelligence 201
- Health Informatics 4
- Information Systems 65
- Computer Vision and Pattern Recognition 48
- Computational Theory and Mathematics 19
Countries citing papers authored by Jieun Eom
This map shows the geographic impact of Jieun Eom'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 Jieun Eom with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jieun Eom more than expected).
Fields of papers citing papers by Jieun Eom
This network shows the impact of papers produced by Jieun Eom. 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 Jieun Eom. The network helps show where Jieun Eom may publish in the future.
Co-authors
The 14 scholars most cited alongside Jieun Eom, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network Hit paper breakdown → | 2022 | 188 |
| 2 | 2016 | 19 | |
| 3 | 2023 | 17 | |
| 4 | 2018 | 10 | |
| 5 | Public Key Encryption with Keyword Search for Restricted Testability | 2011 | 1 |
About Jieun Eom
Jieun Eom is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Computer Networks and Communications, Information Systems and Computer Vision and Pattern Recognition, having authored 5 papers that have together received 235 indexed citations. Recurring topics across this work include Cryptography and Data Security (5 papers), Complexity and Algorithms in Graphs (3 papers), Privacy-Preserving Technologies in Data (2 papers), Advanced Authentication Protocols Security (1 paper), Cloud Data Security Solutions (1 paper), Coding theory and cryptography (1 paper), Cryptographic Implementations and Security (1 paper) and Chaos-based Image/Signal Encryption (1 paper). The work is most often cited by research in Artificial Intelligence (201 citations), Health Informatics (4 citations), Information Systems (65 citations), Computer Vision and Pattern Recognition (48 citations) and Computational Theory and Mathematics (19 citations). Jieun Eom has collaborated with scholars based in South Korea. Frequent co-authors include Yongwoo Lee, Maxim Deryabin, Joon-Woo Lee, HyungChul Kang, Donghoon Yoo, Jong‐Seon No, Young Sik Kim, Eunsang Lee, Junghyun Lee and Kwangsu Lee. Their work appears in journals such as IEEE Access, IEEE Transactions on Computers, Journal of Medical Systems and Information Security and Cryptology.
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.