Maxim Deryabin

813 total citations · 1 hit paper
31 papers, 406 citations indexed

About

Maxim Deryabin is a scholar working on Artificial Intelligence, Information Systems and Computer Networks and Communications. According to data from OpenAlex, Maxim Deryabin has authored 31 papers receiving a total of 406 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Artificial Intelligence, 19 papers in Information Systems and 10 papers in Computer Networks and Communications. Recurrent topics in Maxim Deryabin's work include Cryptography and Residue Arithmetic (14 papers), Cryptography and Data Security (14 papers) and Chaos-based Image/Signal Encryption (8 papers). Maxim Deryabin is often cited by papers focused on Cryptography and Residue Arithmetic (14 papers), Cryptography and Data Security (14 papers) and Chaos-based Image/Signal Encryption (8 papers). Maxim Deryabin collaborates with scholars based in Russia, Mexico and South Korea. Maxim Deryabin's co-authors include Mikhail Babenko, N.I. Chervyakov, Yongwoo Lee, Jieun Eom, Young Sik Kim, Joon-Woo Lee, Junghyun Lee, Jong‐Seon No, Eunsang Lee and HyungChul Kang and has published in prestigious journals such as IEEE Access, Sensors and Neurocomputing.

In The Last Decade

Maxim Deryabin

30 papers receiving 382 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network

2022 188 citations Yongwoo Lee, Maxim Deryabin et al. profile →

Peers

Maxim Deryabin

AI

IS

CVPR

CNC

EEE

Ahmad Al Badawi Singapore

Shahzaib Tahir Pakistan

Kim Thuat Nguyen France

Mohammed Abdulridha Hussain Iraq

Zaid Alaa Hussien Iraq

Zhuoran Ma China

Slobodan Petrović Norway

Nouha Oualha France

Ahmed T. Sadiq Iraq

Shamsher Ullah China

Ahmad Al Badawi Singapore

Maxim Deryabin

464 ×1.6

AI

200 ×1.2

IS

77 ×0.8

CVPR

216 ×2.3

CNC

47 ×1.1

EEE

Citations per year, relative to Maxim Deryabin Maxim Deryabin (= 1×) peers Ahmad Al Badawi

Countries citing papers authored by Maxim Deryabin

Since Specialization

Citations

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

Fields of papers citing papers by Maxim Deryabin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maxim Deryabin

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

All Works

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20 of 20 papers shown

1.

Mert, Ahmet Can, et al.. (2025). REED: Chiplet-based Accelerator for Fully Homomorphic Encryption. IACR Transactions on Cryptographic Hardware and Embedded Systems. 2025(2). 163–208. 1 indexed citations

2.

Mert, Ahmet Can, et al.. (2023). ModHE: Modular Homomorphic Encryption Using Module Lattices. IACR Transactions on Cryptographic Hardware and Embedded Systems. 2024(1). 527–562. 3 indexed citations

3.

Tchernykh, Andrei, et al.. (2023). DT-RRNS: Routing Protocol Design for Secure and Reliable Distributed Smart Sensors Communication Systems. Sensors. 23(7). 3738–3738. 4 indexed citations

4.

Babenko, Mikhail, Maxim Deryabin, Andrei Tchernykh, et al.. (2022). Multiple Error Correction in Redundant Residue Number Systems: A Modified Modular Projection Method with Maximum Likelihood Decoding. Applied Sciences. 12(1). 463–463. 2 indexed citations

5.

Babenko, Mikhail, S.J. Piestrak, N.I. Chervyakov, & Maxim Deryabin. (2021). The Study of Monotonic Core Functions and Their Use to Build RNS Number Comparators. Electronics. 10(9). 1041–1041. 1 indexed citations

6.

Chervyakov, N.I., Pavel Lyakhov, Mikhail Babenko, et al.. (2020). A Division Algorithm in a Redundant Residue Number System Using Fractions. Applied Sciences. 10(2). 695–695. 2 indexed citations

7.

Chervyakov, N.I., Pavel Lyakhov, Maxim Deryabin, et al.. (2020). Residue Number System-Based Solution for Reducing the Hardware Cost of a Convolutional Neural Network. Neurocomputing. 407. 439–453. 15 indexed citations

8.

Babenko, Mikhail, Maxim Deryabin, S.J. Piestrak, et al.. (2020). RNS Number Comparator Based on a Modified Diagonal Function. Electronics. 9(11). 1784–1784. 10 indexed citations

9.

Deryabin, Maxim, et al.. (2019). Protocol for Secure and Reliable Data Transmission in MANET based on Modular Arithmetic. 93. 1–5. 3 indexed citations

10.

Chervyakov, N.I., et al.. (2019). A High-Speed Division Algorithm for Modular Numbers Based on the Chinese Remainder Theorem with Fractions and Its Hardware Implementation. Electronics. 8(3). 261–261. 9 indexed citations

11.

Miranda-López, Vanessa, Andrei Tchernykh, Mikhail Babenko, et al.. (2019). Weighted Two-Levels Secret Sharing Scheme for Multi-Clouds Data Storage with Increased Reliability. 23. 915–922. 2 indexed citations

12.

Babenko, Mikhail, et al.. (2019). Efficient Implementation of Cryptography on Points of an Elliptic Curve in Residue Number System. 1–5. 1 indexed citations

13.

Deryabin, Maxim, et al.. (2018). High Performance Parallel Computing in Residue Number System. 9(1). 62–67. 5 indexed citations

14.

Tchernykh, Andrei, Mikhail Babenko, N.I. Chervyakov, et al.. (2018). AC-RRNS: Anti-collusion secured data sharing scheme for cloud storage. International Journal of Approximate Reasoning. 102. 60–73. 34 indexed citations

15.

Chervyakov, N.I., et al.. (2018). A new model to optimize the architecture of a fault-tolerant modular neurocomputer. Neurocomputing. 303. 37–46. 3 indexed citations

16.

Babenko, Mikhail, N.I. Chervyakov, Andrei Tchernykh, et al.. (2018). Security analysis of homomorphic encryption scheme for cloud computing: Known-plaintext attack. 270–274. 4 indexed citations

17.

Tchernykh, Andrei, Mikhail Babenko, N.I. Chervyakov, et al.. (2017). Towards Mitigating Uncertainty of Data Security Breaches and Collusion in Cloud Computing. 51. 137–141. 10 indexed citations

18.

Chervyakov, N.I., Amir Sabbagh Molahosseini, Pavel Lyakhov, Mikhail Babenko, & Maxim Deryabin. (2016). Residue-to-binary conversion for general moduli sets based on approximate Chinese remainder theorem. International Journal of Computer Mathematics. 94(9). 1833–1849. 37 indexed citations

19.

Chervyakov, N.I., et al.. (2016). Computation of positional characteristics of numbers in RNS based on approximate method. 34. 177–179. 5 indexed citations

20.

Chervyakov, N.I., et al.. (2016). An efficient method of error correction in fault-tolerant modular neurocomputers. Neurocomputing. 205. 32–44. 18 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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