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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.
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“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]
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“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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“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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Cites
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473
New directions in cryptography
Diffie, W., & Hellman, M. (1976) · IEEE Transactions on Information Theory · doi:10.1109/tit.1976.1055638 · Cited by 14,713
A method for obtaining digital signatures and public-key cryptosystems
Rivest, R. L., Shamir, A., & Adleman, L. (1978) · Communications of the ACM · doi:10.1145/359340.359342 · Cited by 13,462
A method for obtaining digital signatures and public-key cryptosystems
Rivest, R. L., Shamir, A., & Adleman, L. (1983) · Communications of the ACM · doi:10.1145/357980.358017 · Cited by 13,012
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
A public key cryptosystem and a signature scheme based on discrete logarithms
Elgamal, T. (1985) · IEEE Transactions on Information Theory · doi:10.1109/tit.1985.1057074 · Cited by 8,059
Public-Key Cryptosystems Based on Composite Degree Residuosity Classes
Paillier, P. (2007) · Lecture Notes in Computer Science · doi:10.1007/3-540-48910-x_16 · Cited by 7,250
Proceedings of the 25th international conference on Machine learning - ICML '08
(2008) · doi:10.1145/1390156 · Cited by 6,865
Protocols for secure computations
Yao, A. C. (1982) · 23rd Annual Symposium on Foundations of Computer Science (sfcs 1982) · doi:10.1109/sfcs.1982.38 · Cited by 3,306
Dlib-ml: A Machine Learning Toolkit
King, D. E. (2009) · Journal of machine learning research · doi:10.5555/1577069.1755843 · Cited by 2,945
Protocols for secure computations
Yao, A. C.-C. (1982) · 23rd Annual Symposium on Foundations of Computer Science (sfcs 1982) · doi:10.1109/sfcs.1982.88 · Cited by 2,702
Homomorphic Encryption for Arithmetic of Approximate Numbers
Cheon, J. H., Kim, A., Kim, M., & Song, Y. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-70694-8_15 · Cited by 2,522
Privacy-preserving deep learning
Shokri, R., & Shmatikov, V. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813687 · Cited by 2,356
A fully homomorphic encryption scheme
Boneh, D., & Gentry, C. (2009) · Cited by 2,341
(1978) · Cited by 1,875
Fully Homomorphic Encryption over the Integers
van Dijk, M., Gentry, C., Halevi, S., & Vaikuntanathan, V. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13190-5_2 · Cited by 1,861
(Leveled) fully homomorphic encryption without bootstrapping
Brakerski, Z., Gentry, C., & Vaikuntanathan, V. (2012) · Conference on Innovations in Theoretical Computer Science · doi:10.1145/2090236.2090262 · Cited by 1,825
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., Xiao, T., Xu, B., Zhang, C., & Zhang, Z. (2015) · arXiv · doi:10.48550/arxiv.1512.01274 · Cited by 1,819
Neural networks for classification: a survey
Zhang, G. (2000) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/5326.897072 · Cited by 1,707
Evaluating 2-DNF Formulas on Ciphertexts
Boneh, D., Goh, E.-J., & Kobbi Nissim, B. (2005) · Lecture notes in computer science · doi:10.1007/978-3-540-30576-7_18 · Cited by 1,679
Privacy-Preserving Deep Learning via Additively Homomorphic Encryption
Phong, L. T., Aono, Y., Hayashi, T., Wang, L., & Moriai, S. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2787987 · Cited by 1,662
Gentry, C., Sahai, A., & Waters, B. R. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40041-4_5 · Cited by 1,571
Efficient Fully Homomorphic Encryption from (Standard) LWE
Brakerski, Z., & Vaikuntanathan, V. (2011) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2011.12 · Cited by 1,466
A Survey on Homomorphic Encryption Schemes: Theory and Implementation
Acar, A., Aksu, H., Uluagac, A. S., & Conti, M. (2019) · ACM Computing Surveys · doi:10.1145/3214303 · Cited by 1,397
Fully Homomorphic Encryption without Modulus Switching from Classical GapSVP
Brakerski, Z. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-32009-5_50 · Cited by 1,282
(2016) · International Conference on Machine Learning · Cited by 1,270
Somewhat Practical Fully Homomorphic Encryption.
Fan, J., & Vercauteren, F. (2012) · Cited by 1,189
Mapping and sequencing of structural variation from eight human genomes
Kidd, J. M., Cooper, G. M., Donahue, W. F., Hayden, H. S., Sampas, N., Graves, T., Hansen, N., Teague, B., Alkan, C., Antonacci, F., et al. (2008) · Nature · doi:10.1038/nature06862 · Cited by 1,119
Damgård, I., & Jurik, M. J. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44586-2_9 · Cited by 1,058
Can homomorphic encryption be practical?
Naehrig, M., Lauter, K., & Vaikuntanathan, V. (2011) · Cloud Computing Security Workshop · doi:10.1145/2046660.2046682 · Cited by 1,027
Implementing Gentry’s Fully-Homomorphic Encryption Scheme
Gentry, C., & Halevi, S. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-20465-4_9 · Cited by 921
Probabilistic encryption & how to play mental poker keeping secret all partial information
Goldwasser, S., & Micali, S. M. (1982) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/800070.802212 · Cited by 884
A Taxonomy of Obfuscating Transformations
Collberg, C. S., Thomborson, C. D., & Low, D. (1997) · Cited by 849
Faster Fully Homomorphic Encryption: Bootstrapping in Less Than 0.1 Seconds
Chillotti, I., Gama, N., Georgieva, M., & Izabachène, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-662-53887-6_1 · Cited by 692
A new public-key cryptosystem as secure as factoring
Okamoto, T., & Uchiyama, S. (1998) · Lecture notes in computer science · doi:10.1007/bfb0054135 · Cited by 674
Computing arbitrary functions of encrypted data
Gentry, C. (2010) · Communications of the ACM · doi:10.1145/1666420.1666444 · Cited by 650
Torch: a modular machine learning software library
Collobert, R., Bengio, S., & Mariéthoz, J. (2002) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 443
Improved Security for a Ring-Based Fully Homomorphic Encryption Scheme
Bos, J. W., Lauter, K., Loftus, J., & Naehrig, M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-45239-0_4 · Cited by 428
SecureNN: 3-Party Secure Computation for Neural Network Training
Wagh, S., Gupta, D., & Chandran, N. (2019) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2019-0035 · Cited by 418
Kim, P. (2017) · Apress eBooks · doi:10.1007/978-1-4842-2845-6 · Cited by 400
Flux: Elegant machine learning with Julia
Innes, M. J. (2018) · The Journal of Open Source Software · doi:10.21105/joss.00602 · Cited by 393
The Generalized Weierstrass Approximation Theorem, Marshall H. Stone (An)
Alexanderson, G. L., & Ross, P. (2007) · MAA spectrum · doi:10.5948/upo9780883859667.010 · Cited by 383
CNTK: Microsoft's Open-Source Deep-Learning Toolkit
Seide, F., & Agarwal, A. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2945397 · Cited by 382
A new public key cryptosystem based on higher residues
Naccache, D., & Stern, J. (1998) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/288090.288106 · Cited by 350
Norwegian University of Science and Technology
(2025) · doi:10.22501/ntnu · Cited by 339
Fast Homomorphic Evaluation of Deep Discretized Neural Networks
Bourse, F., Minelli, M., Minihold, M., & Paillier, P. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-96878-0_17 · Cited by 321
The Generalized Weierstrass Approximation Theorem
Stone, M. H. (1948) · Mathematics Magazine · doi:10.2307/3029750 · Cited by 313
CryptoDL: Deep Neural Networks over Encrypted Data
Hesamifard, E., Takabi, H., & Ghasemi, M. (2017) · arXiv · doi:10.48550/arxiv.1711.05189 · Cited by 306
Faster Bootstrapping with Polynomial Error
Alperin-Sheriff, J., & Peikert, C. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-44371-2_17 · Cited by 304
Simple Encrypted Arithmetic Library - SEAL v2.1
Chen, H., Laine, K., & Player, R. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70278-0_1 · Cited by 287
Private predictive analysis on encrypted medical data
Bos, J. W., Lauter, K., & Naehrig, M. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.04.003 · Cited by 283
Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning
Zhang, Q., Yang, L. T., & Chen, Z. (2015) · IEEE Transactions on Computers · doi:10.1109/tc.2015.2470255 · Cited by 282
Non-interactive cryptocomputing for NC/sup 1/
Sander, T., Young, A. L., & Yung, M. M. (2003) · Annual Symposium on Foundations of Computer Science · doi:10.1109/sffcs.1999.814630 · Cited by 276
Dense Probabilistic Encryption
Benaloh, J. (1999) · Selected Areas in Cryptography · Cited by 252
Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted InferencePreprint
Chou, E., Beal, J., Levy, D., Yeung, S. Y., Haque, A., & Fei-Fei, L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.09953 · Cited by 235
A Full RNS Variant of FV Like Somewhat Homomorphic Encryption Schemes
Bajard, J.-C., Eynard, J., Hasan, M. A., & Zucca, V. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-69453-5_23 · Cited by 234
Chiron: Privacy-preserving Machine Learning as a ServicePreprint
Hunt, T., Song, C., Shokri, R., Shmatikov, V., & Witchel, E. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.05961 · Cited by 234
Evaluating Branching Programs on Encrypted Data
Ishai, Y., & Paskin-Cherniavsky, A. (2007) · Springer eBooks · doi:10.1007/978-3-540-70936-7_31 · Cited by 231
Privacy-Preserving Classification on Deep Neural Network.Preprint
Chabanne, H., de Wargny, A., Milgram, J., Morel, C., & Prouff, E. (2017) · IACR Cryptology ePrint Archive · Cited by 224
An Improved RNS Variant of the BFV Homomorphic Encryption Scheme
Halevi, S., Polyakov, Y., & Shoup, V. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-12612-4_5 · Cited by 217
A Guide to Fully Homomorphic Encryption.
Armknecht, F., Boyd, C., Carr, C., Gjøsteen, K., Jäschke, A., Reuter, C. A., & Strand, M. (2015) · MADOC (University of Mannheim) · Cited by 210
Logistic regression model training based on the approximate homomorphic encryption
Kim, A., Song, Y., Kim, M., Lee, K., & Cheon, J. H. (2018) · BMC Medical Genomics · doi:10.1186/s12920-018-0401-7 · Cited by 210
Badawi, A. A., Jin, C., Lin, J., Mun, C. F., Jie, S. J., Tan, B. H. M., Xiao, N., Aung, K. M. M., & Chandrasekhar, V. (2020) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2020.3014636 · Cited by 191
Scale-Invariant Fully Homomorphic Encryption over the Integers
Coron, J.-S., Lepoint, T., & Tibouchi, M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-642-54631-0_18 · Cited by 186
Scalable and Secure Logistic Regression via Homomorphic Encryption
Aono, Y., Hayashi, T., Phong, L. T., & Wang, L. (2016) · ACM Conference on Data and Application Security and Privacy (CODASPY) · doi:10.1145/2857705.2857731 · Cited by 183
Boemer, F., Costache, A., Cammarota, R., & Wierzynski, C. (2019) · doi:10.1145/3338469.3358944 · Cited by 182
A Survey on Fully Homomorphic Encryption
Martins, P., Sousa, L. A., & Mariano, A. (2017) · ACM Computing Surveys · doi:10.1145/3124441 · Cited by 174
Tchernykh, A. N., Uwe Schwiegelshohn, A., Talbi, E., & Babenko, M. G. (2016) · Journal of Computational Science · doi:10.1016/j.jocs.2016.11.011 · Cited by 167
Self-citationBoemer, F., Lao, Y., Cammarota, R., & Wierzynski, C. (2019) · ACM International Conference on Computing Frontiers (CF) · doi:10.1145/3310273.3323047 · Cited by 162
Low Latency Privacy Preserving InferencePreprint
Brutzkus, A., Elisha, O., & Gilad-Bachrach, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.10659 · Cited by 159
Efficient Homomorphic Comparison Methods with Optimal Complexity
Cheon, J. H., Kim, D., & Kim, D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-64834-3_8 · Cited by 153
cuHE: A Homomorphic Encryption Accelerator Library
Dai, W., & Sunar, B. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-29172-7_11 · Cited by 149
Melchor, C. A., Fau, S., Fontaine, C., Gogniat, G., & Sirdey, R. (2013) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2230219 · Cited by 149
Computing Blindfolded: New Developments in Fully Homomorphic Encryption
Vaikuntanathan, V. (2011) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2011.98 · Cited by 143
Methods and Applications of Error-Free Computation
Gregory, R. T., & Krishnamurthy, E. V. (1984) · Springer eBooks · doi:10.1007/978-1-4612-5242-9 · Cited by 139
Survey of Various Homomorphic Encryption algorithms and Schemes
V.Parmar, P., Padhar, S. B., Patel, S. N., Bhatt, N. I., & Jhaveri, R. H. (2014) · International Journal of Computer Applications · doi:10.5120/15902-5081 · Cited by 132
Implementation and Performance Evaluation of RNS Variants of the BFV Homomorphic Encryption Scheme
Badawi, A. A., Polyakov, Y., Aung, K. M. M., Veeravalli, B., & Rohloff, K. R. (2019) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2019.2902799 · Cited by 126
SHIELD: Scalable Homomorphic Implementation of Encrypted Data-Classifiers
Khedr, A. F., Gulak, P. G., & Vaikuntanathan, V. (2015) · IEEE Transactions on Computers · doi:10.1109/tc.2015.2500576 · Cited by 111
Multi-bit Cryptosystems Based on Lattice Problems
Kawachi, A., Tanaka, K., & Xagawa, K. (2007) · Springer eBooks · doi:10.1007/978-3-540-71677-8_21 · Cited by 99
A Lightweight Authenticated Encryption Scheme Based on Chaotic SCML for Railway Cloud Service
Zheng, Q., Wang, X., Khan, M. K., Zhang, W., Gupta, B. B., & Guo, W. M. (2017) · IEEE Access · doi:10.1109/access.2017.2775038 · Cited by 95
Elliptic Curve Paillier Schemes
Galbraith⋆, S. D. (2002) · Journal of Cryptology · doi:10.1007/s00145-001-0015-6 · Cited by 91
A review of homomorphic encryption and software tools for encrypted statistical machine learningPreprint
Aslett, L. J. M., Esperança, P. M., & Holmes, C. C. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1508.06574 · Cited by 69
A Survey on Agent-based Simulation Using Hardware Accelerators
Xiao, J., Andelfinger, P., Eckhoff, D., Cai, W., & Knoll, A. (2019) · ACM Computing Surveys · doi:10.1145/3291048 · Cited by 68
A Secure Face-Verification Scheme Based on Homomorphic Encryption and Deep Neural Networks
Ma, Y., Wu, L., Gu, X., He, J., & Yang, Z. (2017) · IEEE Access · doi:10.1109/access.2017.2737544 · Cited by 65
Packing Messages and Optimizing Bootstrapping in GSW-FHE
Hiromasa, R., Abe, M., & Okamoto, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-662-46447-2_31 · Cited by 63
Private naive bayes classification of personal biomedical data: Application in cancer data analysis
Wood, A., Shpilrain, V., Najarian, K., & Kahrobaei, D. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.11.018 · Cited by 59
A Scalable Implementation of Fully Homomorphic Encryption Built on NTRU
Rohloff, K. R., & Cousins, D. B. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-44774-1_18 · Cited by 55
Salem, M., Taheri, S., & Yuan, J.-S. (2018) · Computers · doi:10.3390/computers8010003 · Cited by 47
Arabic Online Handwriting Recognition (AOHR)
Al-Helali, B. M., & Mahmoud, S. A. (2017) · ACM Computing Surveys · doi:10.1145/3060620 · Cited by 42
Simulating Homomorphic Evaluation of Deep Learning Predictions
Boura, C., Gama, N., Georgieva, M., & Jetchev, D. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20951-3_20 · Cited by 41
Applications of Homomorphic Encryption
Ekblom, M. (2015) · Aaltodoc (Aalto University) · Cited by 39
Reparaz, O., Gierlichs, B., & Verbauwhede, I. M. R. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66787-4_19 · Cited by 31
Machine Learning Classification over Encrypted Data.
Bost, R., Popa, R. A., Tu, S., & Goldwasser, S. (2014) · Cited by 31
Premkamal, P. K., Pasupuleti, S. K., & Alphonse, P. J. A. (2019) · International Journal of Cloud Applications and Computing · doi:10.4018/ijcac.2020010103 · Cited by 29
Homomorphic encryption in the cloud
Hrestak, D., & Picek, S. (2014) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro.2014.6859786 · Cited by 28
Kaushik, S., & Gandhi, C. (2019) · International Journal of Cloud Applications and Computing · doi:10.4018/ijcac.2020010105 · Cited by 28
Positional Characteristics for Efficient Number Comparison over the Homomorphic Encryption
Babenko, M. G., Tchernykh, A. N., Chervyakov, N. I., Kuchukov, V. A., Miranda-López, V., Rivera-Rodríguez, R., Du, Z., & Talbi, E. (2019) · Programming and Computer Software · doi:10.1134/s0361768819080115 · Cited by 26
Self-citationCryptoRNN - Privacy-Preserving Recurrent Neural Networks Using Homomorphic Encryption
Bakshi, M., & Last, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-49785-9_16 · Cited by 24
Personal Model Training under Privacy Constraints.
Servia-Rodríguez, S., Wang, L., Zhao, J., Mortier, R., & Haddadi, H. (2017) · Cited by 23
Homomorphically Encrypted Arithmetic Operations Over the Integer Ring
Chen, X., Chen, J., Wu, W., & Feng, Y. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49151-6_12 · Cited by 23
Accelerating integer-based fully homomorphic encryption using Comba multiplication
Rafferty, C., O'Neill, M. P., Hanley, N., & O’Sullivan, E. (2014) · SiPS ... design and implementation - IEEE Workshop on Signal Processing Systems · doi:10.1109/sips.2014.6986063 · Cited by 21
Subgroup membership problems and public key cryptosystems
Gjøsteen, K. (2004) · NORA - Norwegian Open Research Archives · Cited by 17
Computing on the Edge of Chaos: Structure and Randomness in Encrypted Computation.Preprint
Gentry, C. (2014) · IACR Cryptology ePrint Archive · Cited by 17
2P-DNN : Privacy-Preserving Deep Neural Networks Based on Homomorphic CryptosystemPreprint
Zhu, Q., & Lv, X. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.08459 · Cited by 17
Improving Learning-based Ego-motion Estimation with Homomorphism-based Losses and Drift Correction
Wang, X., Maturana, D., Yang, S., Wang, W., Chen, Q., & Scherer, S. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968515 · Cited by 15
Chiffrement totalement homomorphe pour l'apprentissage automatique
Minelli, M. (2018) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 14
Parameter selection in lattice-based cryptography
Player, R. (2018) · Cited by 14
Faster Bootstrapping of FHE over the Integers
Cheon, J. H., Han, K., & Kim, D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-40921-0_15 · Cited by 13
Privacy-Preserving Logistic Regression as a Cloud Service Based on Residue Number System
Cortés-Mendoza, J. M., Tchernykh, A. N., Babenko, M. G., Pulido-Gaytan, L. B., Radchenko, G., Leprévost, F., Wang, X., & Avetisyan, A. I. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-64616-5_51 · Cited by 12
Self-citationKeyword guessing on multi-user searchable encryption
Zhen, L., Zhao, M., Jiang, H., & Xu, Q. (2019) · International Journal of High Performance Computing and Networking · doi:10.1504/ijhpcn.2019.099744 · Cited by 10
Keyword guessing on multi-user searchable encryption
Zhao, M., Jiang, H., Xu, Q., & Li, Z. (2017) · International Journal of High Performance Computing and Networking · doi:10.1504/ijhpcn.2017.10007944 · Cited by 9
Zhu, X., Li, D.-P., Fan, W.-G., Lin, M. C., Wang, J.-L., Lin, S.-Q., Huang, J.-Q., & Kung, H. (2010) · BMC Medical Genetics · doi:10.1186/1471-2350-11-83 · Cited by 9
Miranda-López, V., Tchernykh, A. N., Babenko, M. G., Avetisyan, A. I., Toporkov, V. V., & Drozdov, A. Y. (2020) · IEEE Access · doi:10.1109/access.2020.3032655 · Cited by 8
Self-citationTchernykh, A. N., Babenko, M. G., Kuchukov, V. A., Miranda-López, V., Avetisyan, A. I., Rivera-Rodríguez, R., & Radchenko, G. (2019) · IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum · doi:10.1109/ipdpsw.2019.00099 · Cited by 8
Self-citation
Flagged first, then by citations
4 of the 118 references couldn’t be matched to a known paper.
Research works printed but not linked: 9
- Minelli M (2018) Fully homomorphic encryption for machine learning. In: PSL Research University. PhD Thesis, Paris
- Sobitha Ahila S, Shunmuganathan KL (2014) State of art in Homomorphic encryption schemes. Int J Eng Res Appl 4:37–43
- Takabi H, Hesamifard E, Ghasemi M (2016) Privacy preserving multi-party machine learning with Homomorphic encryption. In: 29th Annual Conference on Neural Information Processing Systems (NIPS). Barcelona, Spain
- Rondeau T (2020) Data protection in virtual environments (DPRIVE). DARPA/MTO, Technical report
- Halevi S, Shoup V (2013) Design and implementation of a Homomorphic-encryption library. IBM Res 6:12–15
- Tokui S, Oono K, Hido S, Clayton J (2015) Chainer: a next-generation open source framework for deep learning. In: Proceedings of the Workshop on Machine Learning Systems (LearningSys) at the 28th Annual Conference on Neural Information Processing Systems (NIPS), pp 1-6. http://learningsys.org/papers/LearningSys_2015_paper_33.pdf
- Abadi M, Agarwal A, Barham P, Brevdo E (1603) Large-Scale Machine Learning on Heterogeneous Systems. arXiv, Xiaoqiang Z TensorFlow, p 04467
- Bergstra J, Bastien F, Breuleux O, Lamblin P, Pascanu R, Delalleau O, Bengio Y (2011) Theano: deep learning on GPU with Python. J Mach Learn Res 1:1–48
- Ryffel T, Trask A, Dahl M, Wagner B (1811) Mancuso J. Passerat-Palmbach J A generic framework for privacy preserving deep learning. arXiv, Rueckert D, p 04017
Cited by
Sorted by citations
Privacy–Enhancing Face Biometrics: A Comprehensive Survey
Meden, B., Rot, P., Terhörst, P., Damer, N., Kuijper, A., Scheirer, W. J., Ross, A. A., Peer, P., & Štruc, V. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3096024 · Cited by 226
Recent methodological advances in federated learning for healthcare
Zhang, F., Kreuter, D., Chen, Y., Dittmer, S., Tull, S., Shadbahr, T., Schut, M. C., Asselbergs, F. W., Kar, S., Sivapalaratnam, S., et al. (2024) · Patterns · doi:10.1016/j.patter.2024.101006 · Cited by 152
A Review of Homomorphic Encryption for Privacy-Preserving Biometrics
Yang, W., Wang, S., Cui, H., Tang, Z., & Li, Y. (2023) · Sensors · doi:10.3390/s23073566 · Cited by 126
Fouda, M. M., Fadlullah, Z. M., Ibrahem, M. I., & Kato, N. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3486690 · Cited by 103
Podschwadt, R., Takabi, D., Hu, P., Rafiei, M. H., & Cai, Z. (2022) · IEEE Access · doi:10.1109/access.2022.3219049 · Cited by 99
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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