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Privacy-preserving neural networks with Homomorphic encryption: Challenges and opportunities
Open accessAbstract
Classical machine learning modeling demands considerable computing power for internal calculations and training with big data in a reasonable amount of time. In recent years, clouds provide services to facilitate this process, but it introduces new security threats of data breaches. Modern encryption techniques ensure security and are considered as the best option to protect stored data and data in transit from an unauthorized third-party. However, a decryption process is necessary when the data must be processed or analyzed, falling into the initial problem of data vulnerability. Fully Homomorphic Encryption (FHE) is considered the holy grail of cryptography. It allows a non-trustworthy third-party resource to process encrypted information without disclosing confidential data. In this paper, we analyze the fundamental concepts of FHE, practical implementations, state-of-the-art approaches, limitations, advantages, disadvantages, potential applications, and development tools focusing on neural networks. In recent years, FHE development demonstrates remarkable progress. However, current literature in the homomorphic neural networks is almost exclusively addressed by practitioners looking for suitable implementations. It still lacks comprehensive and more thorough reviews. We focus on the privacy-preserving homomorphic encryption cryptosystems targeted at neural networks identifying current solutions, open issues, challenges, opportunities, and potential research directions.
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“To address these challenges, hybrid PPML frameworks could leverage lightweight HE variants (like CKKS for approximate arithmetic) with hierarchical FL architectures [17, 109, 149], distributing computation across edge devices, RSUs, and cloud servers to minimize latency.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [109]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“HE, though theoretically robust, introduces prohibitive latency in large-scale traffic simulations due to polynomial-degree ciphertext operations, conflicting with sub-second response requirements for dynamic traffic management [2, 109, 132].”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [109]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Moreover, HE's reliance on lattice -based cryptography introduces latency from ciphertext expansion, especially in inference [109].”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [109]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“GPU-accelerated HE libraries tailored for CKKS/BFV schemes could expedite encrypted inference, while Chebyshev polynomial approximations may enable HE-compatible ReLU activations [109].”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [109]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“HE allows arithmetic operations on ciphertexts but struggles with non-linear functions (like ReLU) due to polynomial approximations [109].”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [109]
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In the citing paper
“GPU-accelerated HE libraries tailored for CKKS/BFV schemes could expedite encrypted inference, while Chebyshev polynomial approximations may enable HE-compatible ReLU activations [109].”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [109]
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Flagged first, then by citations
4 of the 118 references couldn’t be matched to a known paper.
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Metadata
- Authors
- Pulido-Gaytan, B., Tchernykh, A., Cortés-Mendoza, J. M., Babenko, M., Radchenko, G., Avetisyan, A., & Drozdov, A. Y.
- Affiliations
- South Ural State University (RU)
- North-Caucasus Federal University (RU)
- Institute for System Programming (RU)
- Moscow Institute of Physics and Technology (RU)
- Corresponding author
- Tchernykh, Andrei
- ORCID
- Venue
- Peer-to-Peer Networking and Applications
- Published
- 2021-03-08
- Volume
- 14
- Issue
- 3
- Pages
- 1666-1691
- ISSN
- 1936-6442 (ISSN-L)
- 1936-6450
- OpenAlex
- W3134210339
- Cited by
- 126
- References
- 118
- Citations by year
- 2026: 18 · 2025: 33 · 2024: 27 · 2023: 29 · 2022: 13 · 2021: 6
- FWCI
- 6.63
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Peer-to-Peer Networking and Applications (publishedVersion, cc-by)
- Funders
- Ministry of Education and Science of the Russian Federation (075-15-2020-788)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- fully homomorphic encryption, privacy-preserving machine learning, encrypted data processing, cloud security, data breaches, ciphers
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