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Analytical Review of Confidential Artificial Intelligence: Methods and Algorithms for Deployment in Cloud Computing

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Abstract

The technologies of artificial intelligence and cloud computing systems have recently been actively developed and implemented. In this regard, the issue of their joint use, which has been topical for several years, has become more acute. The problem of data privacy preservation in cloud computing acquired the status of critical long before the necessity of their joint use with artificial intelligence, which made it even more complicated. This paper presents an overview of both the artificial intelligence and cloud computing techniques themselves, as well as methods to ensure data privacy. The review considers methods that utilize differentiated privacy; secret sharing schemes; homomorphic encryption; and hybrid methods. The conducted research has shown that each considered method has its pros and cons outlined in the paper, but there is no universal solution. It was found that theoretical models of hybrid methods based on secret sharing schemes and fully homomorphic encryption can significantly improve the confidentiality of data processing using artificial intelligence.

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REFERENCES

  1. Brown, T. et al., Language models are few-shot learners, Adv. Neural Inf. Process. Syst., 2020, vol. 33, pp. 1877–1901.

    Google Scholar 

  2. OpenAI, GPT-4 Technical Report, March 27, 2023. https://doi.org/10.48550/arXiv.2303.0877

  3. Douligeris, C. and Mitrokotsa, A., DDoS attacks and defense mechanisms: classification and state-of-the-art, Comput. Networks, 2004, vol. 44, no. 5, pp. 643–666.

    Article  Google Scholar 

  4. Beimel, A., Secret-sharing schemes: a survey, in Coding and Cryptology, Chee, Y.M., Guo, Z., Ling, S., Shao, F., Tang, Y., Wang, H., and Xing, C., Eds., Berlin, Heidelberg: Springer, 2011, pp. 11–46. https://doi.org/10.1007/978-3-642-20901-7_2

    Book  Google Scholar 

  5. Mahesh, B., Machine learning algorithms-a review, Int. J. Sci. Res., 2020, vol. 9, no. 1, pp. 381–386.

    Google Scholar 

  6. Kaelbling, L.P., Littman, M.L., and Moore, A.W., Reinforcement learning: a survey, J. Artif. Intellig. Res., 1996, vol. 4, pp. 237–285.

    Article  Google Scholar 

  7. Srinivas, M. and Patnaik, L.M., Genetic algorithms: a survey, Computer, 1994, vol. 27, no. 6, pp. 17–26.

    Article  Google Scholar 

  8. Spragins, J., Learning without a teacher, IEEE Trans. Inf. Theory, 1996, vol. 12, no. 2, pp. 223–230.

    Article  Google Scholar 

  9. Liu, B., Supervised learning, in Web Data Mining, Berlin, Heidelberg: Springer, 2011, pp. 63–132. https://doi.org/10.1007/978-3-642-19460-3_3

    Book  Google Scholar 

  10. Wang, S.-C., Artificial neural network, in Interdisciplinary Computing in Java Programming, Boston: MA: Springer US, 2003, pp. 81–100. https://doi.org/10.1007/978-1-4615-0377-4_5

    Book  Google Scholar 

  11. Park, H. and Kim, S., Chapter three – hardware accelerator systems for artificial intelligence and machine learning, Adv. Comput., 2021, vol. 122, pp. 51–95. https://doi.org/10.1016/bs.adcom.2020.11.005

    Article  Google Scholar 

  12. Hwang, D.H., Han, C.Y., Oh, H.W., and Lee, S.E., ASimOV: a framework for simulation and optimization of an embedded AI accelerator, Micromachines, 2021, vol. 12, no. 7. https://doi.org/10.3390/mi12070838

  13. Mishra, A., Yadav, P., and Kim, S., Artificial intelligence accelerators, in Artificial Intelligence and Hardware Accelerators, Mishra, A., Cha, J., Park, H., and Kim, S., Eds., Cham: Springer Int. Publ., 2023, pp. 1–52. https://doi.org/10.1007/978-3-031-22170-5_1

    Book  Google Scholar 

  14. Carminati, M. and Scandurra, G., Impact and trends in embedding field programmable gate arrays and microcontrollers in scientific instrumentation, Rev. Sci. Instrum., 2021, vol. 92, no. 9. https://pubs.aip.org/aip/rsi/article-abstract/92/9/091501/1030652.

  15. Shawash, J. and Selviah, D.R., Real-time nonlinear parameter estimation using the Levenberg-Marquardt algorithm on field programmable gate arrays, IEEE Trans. Ind. Electron. Control Instrum., 2012, vol. 60, no. 1, pp. 170–176.

    Article  Google Scholar 

  16. Ruiz-Rosero, J., Ramirez-Gonzalez, G., and Khanna, R., Field programmable gate array applications-a scientometric review, Computation, 2019, vol. 7, no. 4, p. 63.

    Article  Google Scholar 

  17. Mellit, A. and Kalogirou, S.A., MPPT-based artificial intelligence techniques for photovoltaic systems and its implementation into field programmable gate array chips: review of current status and future perspectives, Energy, 2014, vol. 70, pp. 1–21.

    Article  Google Scholar 

  18. Goodfellow, I., Bengio, Y., and Courville, A., Deep Learning, MIT Press, 2016. https://books.google.com/books?hl=ru&lr=&id=omivDQAAQBAJ&oi=fnd&pg=PR5&dq=Deep+Learning&ots=MNV5aolzSS&sig=waXAS6C-_v-48H2qbW9rMFkEhFY.

  19. Bouvrie, J., Notes on convolutional neural networks, 2006. http://web.mit.edu/jvb/www/papers/cnn_tutorial.pdf.

  20. Rawat, W. and Wang, Z., Deep convolutional neural networks for image classification: a comprehensive review, Neural Comput., 2017, vol. 29, no. 9, pp. 2352–2449.

    Article  MathSciNet  Google Scholar 

  21. Needham, R.M. and Herbert, A.J., The Cambridge Distributed Computing System, Cambridge, 1983.

    Google Scholar 

  22. Adiga, N.R. et al., An overview of the BlueGene/L supercomputer, Proc. ACM/IEEE Conf. on Supercomputing, SC’02, Baltimore, MD, 2002, p. 60. https://ieeexplore.ieee.org/abstract/document/1592896/.

  23. Jacob, B., Brown, M., Fukui, K., and Trivedi, N., Introduction to grid computing, in IBM Redbooks, 2005, pp. 3–6.

  24. Foster, I., Zhao, Y., Raicu, I., and Lu, S., Cloud computing and grid computing 360-degree compared, Proc. IEEE Grid Computing Environments Workshop, Austin, TX, 2008, pp. 1–10. https://ieeexplore.ieee.org/abstract/document/4738445/?casa_token=TbNOHOEaljQAAAAA:j6MuEJKmrGL8iCvH-HzRnmI2k5UKn5y1w7hC4MNJanJXZPfiBC_XKLoTFsCImP1RYzyKfRKiCE0.

    Book  Google Scholar 

  25. Cusumano, M., Cloud computing and SaaS as new computing platforms, Commun. ACM, 2010, vol. 53, no. 4, pp. 27–29. https://doi.org/10.1145/1721654.1721667

    Article  Google Scholar 

  26. Rodero-Merino, L., Vaquero, L.M., Caron, E., Muresan, A., and Desprez, F., Building safe PaaS clouds: a survey on security in multitenant software platforms, Comput. Secur., 2012, vol. 31, no. 1, pp. 96–108.

    Article  Google Scholar 

  27. Bhardwaj, S., Jain, L., and Jain, S., Cloud computing: a study of infrastructure as a service (IAAS), Int. J. Eng. Inf. Technol., 2010, vol. 2, no. 1, pp. 60–63.

    Google Scholar 

  28. Manvi, S.S. and Shyam, G.K., Resource management for infrastructure as a service (IAAS) in cloud computing: a survey, J. Network Comput. Appl., 2014, vol. 41, pp. 424–440.

    Article  Google Scholar 

  29. Lehner, W. and Sattler, K.-U., Database as a service (DBaaS), Proc. IEEE 26th Int. Conf. on Data Engineering (ICDE 2010), Long Beach, CA, 2010, pp. 1216–1217. https://ieeexplore.ieee.org/abstract/document/5447723/?casa_token=uaXogPZV0C0AAAAA:4Dg_40-GvhUsuHXFKUOgxZ_ZyGlCOqjcZtpRoK6UosB-k-_Wh5wAmJIBtHYRE9OLXZ1xwVKuLAE.

  30. Meng, S. and Liu, L., Enhanced monitoring-as-a-service for effective cloud management, IEEE Trans. Comput., 2012, vol. 62, no. 9, pp. 1705–1720.

    Article  MathSciNet  Google Scholar 

  31. Weng, Q., et al., {MLaaS} in the wild: workload analysis and scheduling in {Large-Scale} heterogeneous {GPU} clusters, Proc. 19th USENIX Symp. on Networked Systems Design and Implementation (NSDI 22), Renton, WA, 2022, pp. 945–960. https://www.usenix.org/conference/nsdi22/presentation/weng.

    Google Scholar 

  32. Bisong, E., Google colaboratory, in Building Machine Learning and Deep Learning Models on Google Cloud Platform, Berkeley: CA: Apress, 2019, pp. 59–64. https://doi.org/10.1007/978-1-4842-4470-8_7

    Book  Google Scholar 

  33. H2O AI Cloud. https://h2o.ai/platform/ai-cloud/.

  34. NVIDIA NGC | NVIDIA. https://www.nvidia.com/en-us/gpu-cloud/.

  35. Tang, J., Artificial intelligence-based e-commerce platform based on SaaS and neural networks, Proc. 4th IEEE Int. Conf. on Inventive Systems and Control (ICISC), Seoul, 2020, pp. 421–424. https://ieeexplore.ieee.org/abstract/document/9171193/?casa_token=TmYwFdLDXq0AAAAA:8P5VVcZS_KWCXEnEm8xk2RPMV5kfWF27K9S9O9Z5fYh273EkseT7j0Jf7jZYAMOnPUX0l-5sCbs.

    Google Scholar 

  36. Yathiraju, N., Investigating the use of an artificial intelligence model in an ERP cloud-based system, Int. J. Electr., Electron. Comput., 2022, vol. 7, no. 2, pp. 1–26.

    Google Scholar 

  37. Mishra, S. and Tripathi, A.R., AI business model: an integrative business approach, J. Innov. Entrepreneur, 2021, vol. 10, no. 1, p. 18. https://doi.org/10.1186/s13731-021-00157-5

    Article  Google Scholar 

  38. Mishra, D. and Shekhar, S., Artificial intelligence candidate recruitment system using software as a service (SaaS) architecture, Int. Res. J. Eng. Technol., 2018, vol. 05, no. 05, pp. 3804–3808.

    Google Scholar 

  39. Cadario, R., Longoni, C., and Morewedge, C.K., Understanding, explaining, and utilizing medical artificial intelligence, Nat. Hum. Behav., 2021, vol. 5, no. 12, pp. 1636–1642.

    Article  Google Scholar 

  40. Kim, M., Song, Y., Wang, S., Xia, Y., and Xiang, X., Secure logistic regression based on homomorphic encryption: design and evaluation, JMIR Med. Inf., 2018, vol. 6, no. 2, p. e8805.

  41. Klonoff, D.C., Fog computing and edge computing architectures for processing data from diabetes devices connected to the medical internet of things, J. Diabetes Sci. Technol., 2017, vol. 11, no. 4, pp. 647–652.

    Article  Google Scholar 

  42. Kocabas, O. and Soyata, T., Utilizing homomorphic encryption to implement secure and private medical cloud computing, Proc. 8th IEEE Int. Conf. on Cloud Computing, New York, 2015, pp. 540–547.

  43. Liu, R., Rong, Y., and Peng, Z., A review of medical artificial intelligence, Global Health J., 2020, vol. 4, no. 2, pp. 42–45.

    Article  Google Scholar 

  44. Sun, X., Zhang, P., Sookhak, M., Yu, J., and Xie, W., Utilizing fully homomorphic encryption to implement secure medical computation in smart cities, Pers. Ubiquitous Comput., 2017, vol. 21, no. 5, pp. 831–839.

    Article  Google Scholar 

  45. Kaya, O., Schildbach, J., Ag, D.B., and Schneider, S., Artificial intelligence in banking, in Artificial Intelligence, 2019. https://www.dbresearch.com/PROD/RPS_ENPROD/PROD0000000000495172/Artificial_intelligence_in_banking%3A_A_lever_for_pr.pdf.

  46. Rahman, M., Ming, T.H., Baigh, T.A., and Sarker, M., Adoption of artificial intelligence in banking services: an empirical analysis, Int. J. Emerging Markets, 2021. https://www.emerald.com/insight/content/doi/10.1108/IJOEM-06-2020-0724/full/html.

  47. Sadok, H., Sakka, F., and El Maknouzi, M.E.H., Artificial intelligence and bank credit analysis: a review, Cogent Econ. Fin., 2022, vol. 10, no. 1, p. 2023262. https://doi.org/10.1080/23322039.2021.2023262

  48. Smith, A. and Nobanee, H., Artificial intelligence: in banking a mini-review. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3539171.

  49. Reis, J., Santo, P.E., and Melão, N., Artificial intelligence in government services: a systematic literature review, in New Knowledge in Information Systems and Technologies, Rocha, Á., Adeli, H., Reis, L.P., and Costanzo, S., Eds., Cham: Springer Int. Publ., 2019, vol. 930, pp. 341–259. https://doi.org/10.1007/978-3-030-16181-1_23

    Book  Google Scholar 

  50. Valle-Cruz, D., Alejandro Ruvalcaba-Gomez, E., Sandoval-Almazan, R., Ignacio Criado, J., A review of artificial intelligence in government and its potential from a public policy perspective, in Proc. 20th Annu. Int. Conf. on Digital Government Research, Dubai: ACM, 2019, pp. 91–99. https://doi.org/10.1145/3325112.3325242

  51. Pitts, W., The linear theory of neuron networks: the dynamic problem, Bull. Math. Biophys., 1943, vol. 5, pp. 23–31.

    Article  MathSciNet  Google Scholar 

  52. Khare, S.S. and Gajbhiye, A.R., Literature review on application of artificial neural network (ANN) in operation of reservoirs, Int. J. Comput. Eng. Res., 2013, vol. 3, no. 6, p. 63.

    Google Scholar 

  53. Seesing, A., Evotest: test case generation using genetic programming and software analysis, Oper. Res., 1954, vol. 2, pp. 393–410.

    Google Scholar 

  54. Samuel, A.L., Machine learning, Technol. Rev., 1959, vol. 62, no. 1, pp. 42–45.

    Google Scholar 

  55. Evreinov, Ė.V. and Kosarev, I., Odnorodnye universal’nye vychislitel’nye sistemy vysokoi proizvoditel’nosti (Uniform High Efficiency Computating Systems), Novosibirsk: Nauka, 1966. https://cir.nii.ac.jp/crid/1130282272859765760.

  56. Gold, E.M., Language identification in the limit, Inf. Control, 1967, vol. 10, no. 5, pp. 447–474.

    Article  MathSciNet  Google Scholar 

  57. Glushkov, V.M., Computating system, 1996. https://elibrary.ru/item.asp?id=41074434.

  58. Huang, X., Deep-learning based climate downscaling using the super-resolution method, Preprint, 1981. https://pdfs.semanticscholar.org/cf5c/3b29559ababba5a889444632e1c91d6b78fc.pdf.

  59. Smarr, L. and Catlett, C.E., Metacomputing, in Grid Computing, Berman, F., Fox, G., and Hey, T., Eds., 1st ed., Wiley, 2003, pp. 825–835. https://doi.org/10.1002/0470867167.ch37

    Book  Google Scholar 

  60. Buske, D. and Keith, S., GIMPS finds another prime!, Math Horizons, 2000, vol. 7, no. 4, pp. 19–21. https://doi.org/10.1080/10724117.2000.11975124

    Article  Google Scholar 

  61. Anderson, D.P., Boinc: a system for public-resource computing and storage, Proc. 5th IEEE/ACM Int. Workshop on Grid Computing, Pittsburgh, PA, 2004, pp. 4–10. https://ieeexplore.ieee.org/abstract/document/1382809/?casa_token=cjAKtADFAKwAAAAA:-WGH_xmovZAUi-kr_PA-h3nXtuizBL829DPFlC0B6pbcCoApRKDCZLwFWxzfYdT0WauFC5c6EQw1

    Google Scholar 

  62. Du, T. and Shanker, V., Deep learning for natural language processing, Brain Nerve, 2019, vol. 71, no. 1, pp. 45–55.

    Google Scholar 

  63. Davies, E.R., Machine Vision: Theory, Algorithms, Practicalities, Elsevier, 2004. https://books.google.com/books?hl=ru&lr=&id=uY-Z3vORugwC&oi=fnd&pg=PP1&dq=Machine+Vision+:+Theory,+Algorithms,+Practicalities&ots=QOl9U9_MBf&sig=w0poN6d3IGeXs4oacagO4MlnxYs.

  64. Mell, P. and Grance, T., The NIST definition of cloud computing, Natl. Inst. Stand. Technol. Spec. Publ., 2011, vol. 53, pp. 1–7.

    Google Scholar 

  65. Finkelstein, R., Analyzing trend of cloud computing and it’s enablers using Gartner strategic technology, 2004. https://www.researchgate.net/profile/Amol-Adamuthe/publication/308747055_Analyzing_Trend_of_Cloud_Computing_and_it's_Enablers_using_Gartner_Strategic_Technology/links/59a929d3a6fdcc2398414d6f/Analyzing-Trend-of-Cloud-Computing-and-its-Enablers-using-Gartner-Strategic-Technology.pdf.

  66. A history of cloud computing, Computer Weekly. https://www.computerweekly.com/feature/A-history-of-cloud-computing.

  67. Dolui, K. and Datta, S.K., Comparison of edge computing implementations: fog computing, cloudlet and mobile edge computing, Proc. IEEE Global Internet of Things Summit (GIoTS), Geneva, 2017, pp. 1–6.

  68. OpenFog, OPC Foundation. https://opcfoundation.org/markets-collaboration/openfog/.

  69. Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I., Improving language understanding by generative pre-training, 2018. https://www.mikecaptain.com/resources/pdf/GPT-1.pdf.

  70. Beaulieu-Jones, B.K. et al., Privacy-preserving generative deep neural networks support clinical data sharing, Circ: Cardiovasc. Qual. Outcomes, 2019, vol. 12, no. 7, p. e005122. https://doi.org/10.1161/CIRCOUTCOMES.118.005122

  71. Shokri, R. and Shmatikov, V., Privacy-preserving deep learning, Proc. 22nd ACM SIGSAC Conf. on Computer and Communications Security, Denver CO, Oct. 2015, pp. 1310–1321. https://doi.org/10.1145/2810103.2813687

  72. Shamir, A., How to share a secret, Commun. ACM, 1979, vol. 22, no. 11, pp. 612–613.

    Article  MathSciNet  Google Scholar 

  73. Duan, J., Zhou, J., and Li, Y., Privacy-preserving distributed deep learning based on secret sharing, Inf. Sci., 2020, vol. 527, pp. 108–127.

    Article  MathSciNet  Google Scholar 

  74. Akushsky, I.A. and Yuditsky, D.I., Mashinnaya arifmetika v ostatochnykh klassakh (Modular Arithmetic in Residue Classes), Moscow: Sovetskoe radio, 1968.

  75. Bloom, J., A modular approach to key safeguarding, IEEE Trans. Inf. Theory, 1983, vol. 29, no. 2, pp. 208–210.

    Article  MathSciNet  Google Scholar 

  76. Mignotte, M., How to share a secret, Proc. Workshop on Cryptography, Springer, 1982, pp. 371–375.

  77. Tian, T., Wang, S., Xiong, J., Bi, R., Zhou, Z., and Bhuiyan, M.Z.A., Robust and privacy-preserving decentralized deep federated learning training: focusing on digital healthcare applications, IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2023. https://ieeexplore.ieee.org/abstract/document/10058838/.

  78. Barzu, M., Ţiplea, F.L., and Drăgan, C.C., Compact sequences of co-primes and their applications to the security of CRT-based threshold schemes, Inf. Sci., 2013, vol. 240, pp. 161–172.

    Article  MathSciNet  Google Scholar 

  79. Ge, Z., Zhou, Z., Guo, D., and Li, Q., Practical two-party privacy-preserving neural network based on secret sharing. http://arxiv.org/abs/2104.04709.

  80. Paillier, P., Public-key cryptosystems based on composite degree residuosity classes, in Proc. Conf. Advances in Cryptology – EUROCRYPT’99, Stern, J., Ed., Berlin, Heidelberg: Springer, 1999, vol. 1592, pp. 223–238. https://doi.org/10.1007/3-540-48910-X_16

  81. Benaloh, J., Dense probabilistic encryption, Proc. Workshop on Selected Areas of Cryptography, Kingston 1994, pp. 120–128. https://sacworkshop.org/proc/SAC_94_006.pdf.

    Google Scholar 

  82. Rivest, R.L., Shamir, A., and Adleman, L., A method for obtaining digital signatures and public-key cryptosystems, Commun. ACM, 1978, vol. 21, no. 2, pp. 120–126. https://doi.org/10.1145/359340.359342

    Article  MathSciNet  Google Scholar 

  83. ElGamal, T., A public key cryptosystem and a signature scheme based on discrete logarithms, IEEE Trans. Inf. Theory, 1985, vol. 31, no. 4, pp. 469–472.

    Article  MathSciNet  Google Scholar 

  84. Chen, T. and Zhong, S., Privacy-preserving backpropagation neural network learning, IEEE Trans. Neural Networks, 2009, vol. 20, no. 10, pp. 1554–1564.

    Article  Google Scholar 

  85. Gentry, C., A Fully Homomorphic Encryption Scheme, Stanford Univ., 2009.

    Google Scholar 

  86. Gentry, C., Computing arbitrary functions of encrypted data, Commun. ACM, 2010, vol. 53, no. 3, pp. 97–105.

    Article  Google Scholar 

  87. Gentry, C. and Halevi, S., Implementing Gentry’s fully-homomorphic encryption scheme, in Proc. 30th Annu. Int. Conf. on the Theory and Applications of Cryptographic Techniques Advances in Cryptology-EUROCRYPT 2011, Tallin, May 15–19, 2011, Springer, 2011, pp. 129–148.

  88. Gentry, C., Halevi, S., Peikert, C., and Smart, N.P., Ring switching in BGV-style homomorphic encryption, in Security and Cryptography for Networks, Visconti, I. and de Prisco, R., Eds., Berlin, Heidelberg: Springer, 2012, vol. 7485, pp. 19–37. https://doi.org/10.1007/978-3-642-32928-9_2

    Book  Google Scholar 

  89. Gentry, C., Sahai, A., and Waters, B., Homomorphic encryption from learning with errors: conceptually-simpler, asymptotically-faster, attribute-based, in Proc. Annu. Conf. on Cryptology, Springer, 2013, pp. 75–92.

  90. van Dijk, M., Gentry, C., Halevi, S., and Vaikuntanathan, V.V., Fully homomorphic encryption over the integers, in Proc. Annu. Int. Conf. on the Theory and Applications of Cryptographic Techniques, Springer, 2010, pp. 24–43.

  91. van Dijk, M., Gentry, C., Halevi, S., and Vaikuntanathan, V., Fully homomorphic encryption over the integers, in Proc. Conf. Advances in Cryptology – EUROCRYPT 2010, Gilbert, H., Ed., Berlin, Heidelberg: Springer, 2010. vol. 6110, pp. 24–43. https://doi.org/10.1007/978-3-642-13190-5_2

  92. Cheon, J.H., Kim, A., Kim, M., and Song, Y., Homomorphic encryption for arithmetic of approximate numbers, in Proc. Int. Conf. on the Theory and Application of Cryptology and Information Security, Springer, 2017, pp. 409–437.

  93. Gilad-Bachrach, R., Dowlin, N., Laine, K., Lauter, K., Naehrig, M., and Wernsing, J., Cryptonets: applying neural networks to encrypted data with high throughput and accuracy, Proc. Int. Conf. on Machine Learning, New York, 2016, pp. 201–210. https://proceedings.mlr.press/v48/gilad-bachrach16.html.

  94. van Elsloo, T., Patrini, G., and Ivey-Law, H., SEALion: a framework for neural network inference on encrypted data. http://arxiv.org/abs/1904.12840.

  95. TensorFlow. https://www.tensorflow.org/?hl=ru.

  96. Microsoft SEAL. https://github.com/microsoft/SEAL.

  97. Benaissa, A., Retiat, B., Cebere, B., and Belfedhal, A.E., TenSEAL: a library for encrypted tensor operations using homomorphic encryption. http://arxiv.org/abs/2104.03152.

  98. Chabanne, H., De Wargny, A., Milgram, J., Morel, C., and Prouff, E., Privacy-preserving classification on deep neural network, Cryptol. ePrint Arch., 2017. https://eprint.iacr.org/2017/035.

  99. Brakerski, Z., Gentry, C., and Vaikuntanathan, V., (Leveled) fully homomorphic encryption without bootstrapping, ACM Trans. Comput. Theory (TOCT), 2014, vol. 6, no. 3, pp. 1–36.

    Article  MathSciNet  Google Scholar 

  100. Lee, J.-W. et al., Privacy-preserving machine learning with fully homomorphic encryption for deep neural network, IEEE Access, 2022, vol. 10, pp. 30039–30054.

    Article  Google Scholar 

  101. Ryffel, T., Tholoniat, P., Pointcheval, D., and Bach, F., ARIANN: low-interaction privacy-preserving deep learning via function secret sharing, Oct. 28, 2021. http://arxiv.org/abs/2006.04593.

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This work was supported by the Russian Science Foundation 19-71-10033, https://rscf.ru/project/19-71-10033/.

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Shiriaev, E.M., Nazarov, A.S., Kucherov, N.N. et al. Analytical Review of Confidential Artificial Intelligence: Methods and Algorithms for Deployment in Cloud Computing. Program Comput Soft 50, 304–314 (2024). https://doi.org/10.1134/S0361768824700117

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