VLDB 2026 Research / reviewers in the wild / expert
Maxim Anatolievich Deryabin
dblp:181/1804 · also Maxim Deryabin
· DBLP profile ↗
7ranked-venue papers
0as first author
3since 2021 · last 2024
0000-0002-6761-3667ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 4Security and privacy · 2 · 2 since 2021Systems, architecture and hardware · 1 · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Exploring the Advantages and Challenges of Fermat NTT in FHE Acceleration
Andrey Kim, Ahmet Can Mert, Anisha Mukherjee, Aikata, Maxim Anatolievich Deryabin, Sunmin Kwon, HyungChul Kang, Sujoy Sinha Roy |
CRYPTO (3) | 5 |
| 2024 | General Bootstrapping Approach for RLWE-Based Homomorphic EncryptionabstractHomomorphic Encryption (HE) makes it possible to compute on encrypted data without decryption. In lattice-based HE, a ciphertext contains noise, which accumulates along with homomorphic computations. Bootstrapping refreshes the noise and it is possible to perform arbitrary-depth computations on HE with bootstrapping, which we call Fully Homomorphic Encryption (FHE). In this article, we propose a new general bootstrapping technique for RLWE-based schemes and its practical instantiation for FHE. It can be applied to all three RLWE-based leveled FHE schemes: Brakerski-Gentry-Vaikuntanathan (BGV), Brakerski/Fan-Vercauteren (BFV), and Cheon-Kim-Kim-Song (CKKS) with minor deviations in the algorithms. Our new construction of bootstrapping extracts a noiseless ciphertext for a part of the input, scales it, and finally removes it. In contrast with previous bootstrapping algorithms, the proposed method consumes only 1–2 levels and uses smaller parameters. For BGV and BFV, our new bootstrapping does not have any restrictions on a plaintext modulus unlike typical cases of the previous methods. The error introduced by our approach for CKKS is comparable to a rescaling error, allowing us to preserve a large amount of precision after bootstrapping. Andrey Kim, Maxim Anatolievich Deryabin, Jieun Eom, Rakyong Choi, Yongwoo Lee 0002, Whan Ghang, Donghoon Yoo |
IEEE Trans. Computers | 2 |
| 2023 | Efficient FHEW Bootstrapping with Small Evaluation Keys, and Applications to Threshold Homomorphic Encryption
Yongwoo Lee 0002, Daniele Micciancio, Andrey Kim, Rakyong Choi, Maxim Anatolievich Deryabin, Jieun Eom, Donghoon Yoo |
EUROCRYPT (3) | 5 |
| 2020 | Residue Number System-Based Solution for Reducing the Hardware Cost of a Convolutional Neural Network
Nikolay I. Chervyakov, Pavel A. Lyakhov, Maxim Anatolievich Deryabin, Nikolay N. Nagornov, Maria V. Valueva, Georgii V. Valuev |
Neurocomputing | 3 |
| 2018 | AC-RRNS: Anti-collusion secured data sharing scheme for cloud storage
Andrei Tchernykh, Mikhail G. Babenko, Nikolay I. Chervyakov, Vanessa Miranda-López, Viktor Andreevich Kuchukov, Jorge M. Cortés-Mendoza, Maxim Anatolievich Deryabin, Nikolay Nikolaevich Kucherov, Gleb I. Radchenko, Arutyun Avetisyan |
Int. J. Approx. Reason. | 7 |
| 2018 | A new model to optimize the architecture of a fault-tolerant modular neurocomputer
Nikolay I. Chervyakov, Pavel A. Lyakhov, Mikhail G. Babenko, Irina N. Lavrinenko, Anton V. Lavrinenko, Maxim Anatolievich Deryabin, Anton S. Nazarov |
Neurocomputing | 6 |
| 2016 | An efficient method of error correction in fault-tolerant modular neurocomputers
Nikolay I. Chervyakov, Pavel A. Lyakhov, Mikhail G. Babenko, A. I. Garyanina, Irina N. Lavrinenko, Anton V. Lavrinenko, Maxim Anatolievich Deryabin |
Neurocomputing | 7 |