2025, Vol. 12, No. 2. - go to content...

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DOI: 10.15862/07INOR225 (https://doi.org/10.15862/07INOR225)

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Bubnova E.Yu., Yakupov D.O. Adaptive encryption of text data for natural language processing systems based on large neural network models. Russian journal of resources, conservation and recycling. 2025; 12(2). Available at: https://resources.today/PDF/07INOR225.pdf (in Russian). DOI: 10.15862/07INOR225

Adaptive encryption of text data for natural language processing systems based on large neural network models

Bubnova Elena Yurievna
Povolzhskiy State University of Telecommunications and Informatics, Samara, Russia
E-mail: 89061250055@mail.ru
ORCID: https://orcid.org/0009-0003-1598-8971

Yakupov Denis Olegovich
Povolzhskiy State University of Telecommunications and Informatics, Samara, Russia
E-mail: d.yakupov@psuti.ru
ORCID: https://orcid.org/0009-0003-2371-0822
RSCI: https://elibrary.ru/author_profile.asp?id=1175874

Abstract. Modern enterprises are actively implementing artificial intelligence and automated natural language processing systems to optimize work with corporate information. However, the use of these technologies carries significant risks of data privacy violations, especially when analyzing unstructured text materials. Existing cryptographic methods of information protection demonstrate limited effectiveness in processing linguistic data, and modern approaches to secure information processing have significant disadvantages: homomorphic encryption methods significantly reduce computing speed, and distributed learning technologies face problems with matching heterogeneous data.

In the presented study, a comprehensive information security method has been developed that combines the advantages of homomorphic encryption and distributed learning. The main scientific achievement of the work is the creation of specialized cryptographic algorithms for processing language data, including the identification of named objects in encrypted form, as well as optimizing the ratio between the level of protection and computational efficiency. The proposed methodology includes differentiated encryption of various types of data, decentralized information processing with automatic adjustment of learning parameters, as well as mechanisms to ensure anonymity and access control.

The experimental part of the study evaluates three key parameters: the system’s resilience to potential attacks, the processing speed of protected data, and the accuracy of analytical algorithms. The practical value of the work lies in creating a secure platform for the implementation of intelligent data analysis systems in the financial sector, healthcare and telecommunications industries, where information leakage can lead to serious consequences. A promising area of further research is the development of cryptographic algorithms that are resistant to quantum hacking methods.

Keywords: adaptive encryption; homomorphic encryption; federated learning; data privacy; tokenization; corporate databases; big data

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