Extraction of confidentiality markers from texts under conditions of high uncertainty in systems with data intensive usage [ОБ ОДНОМ ПОДХОДЕ К ФОРМИРОВАНИЮ В УСЛОВИЯХ ВЫСОКОЙ НЕОПРЕДЕЛЕННОСТИ МАРКЕРОВ КОНФИДЕНЦИАЛЬНОСТИ В СИСТЕМАХ ИНТЕНСИВНОГО ИСПОЛЬЗОВАНИЯ ДАННЫХ]

The main tasks, the results of the solution of which are reflected in the article, are associated with the formation of confidentiality markers when they are used in data-intensive systems under conditions when the composition and structure of the protected information cannot be determined in advance due to the lack of data or the high dynamics of their change, or their definition is not advisable due to the large number of entities whose information is subject to protection. In this paper, an approach is proposed for the formation of confidentiality markers for text materials in the indicated conditions. The article presents the semantic text analysis, which forms confidentiality markers when used to ensure information security in data-intensive systems under high uncertainty in the composition and structure of protected information. The obtained experimental results show that practical implementation of the considered approach in data-intensive systems is promising. © 2020 Federal Research Center "Computer Science and Control" of Russian Academy of Sciences. All rights reserved.

Authors
Budzko V.I.1 , Yadrintsev V.V. 1, 2 , Sochenkov I.V. 1 , Korolev V.I.1 , Belenkov V.G.1
Publisher
Федеральный исследовательский центр "Информатика и управление" РАН
Number of issue
4
Language
Russian
Pages
69-76
Status
Published
Volume
14
Year
2020
Organizations
  • 1 Federal Research Center “Computer Science and Control”, The Russian Academy of Sciences, 44-2 Vavilov Str., Moscow, 119333, Russian Federation
  • 2 Peoples' Friendship University of Russia, RUDN University, 6 Miklukho-Maklaya Str., Moscow, 117198, Russian Federation
Keywords
Confidentiality marker; Data leak prevention; Data-intensive domains; Detection of text reuse; Information security; Intelligent security tasks; Semantics; Text classification; Topical cluster
Date of creation
16.12.2021
Date of change
16.12.2021
Short link
https://repository.rudn.ru/en/records/article/record/76407/
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