MODELING THE PROTECTION OF PERSONAL DATA FROM TRUST AND THE AMOUNT OF INFORMATION ON SOCIAL NETWORKS

Keywords: social network, transfer, protection, user, parameter, information, metric, density, cycle

Abstract

The article analyzes the parameters of social networks. The analysis is performed to identify critical threats. Threats may lead to leakage or damage to personal data. The complexity of this issue lies in the ever-increasing volume of data. Analysts note that the main causes of incidents in Internet resources are related to the action of the human factor, the mass hacking of IoT devices and cloud services. This problem is especially exacerbated by the strengthening of the digital humanistic nature of education, the growing role of social networks in human life in general. Therefore, the issue of personal information protection is constantly growing. To address this issue, let’s propose a method of assessing the dependence of personal data protection on the amount of information in the system and trust in social networks. The method is based on a mathematical model to determine the protection of personal data from trust in social networks. Based on the results of the proposed model, modeling was performed for different types of changes in confidence parameters and the amount of information in the system.

As a result of mathematical modeling in the MatLab environment, graphical materials were obtained, which showed that the protection of personal data increases with increasing factors of trust in information. The dependence of personal data protection on trust is proportional to other data protection parameters. The protection of personal data is growing from growing factors of trust in information.

Mathematical modeling of the proposed models of dependence of personal data protection on trust confirmed the reliability of the developed model and proved that the protection of personal data is proportional to reliability and trust

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Author Biographies

Serhii Yevseiev, Simon Kuznets Kharkiv National University of Economics

Department of Cyber Security and Information Technology

Oleksandr Laptiev, State University of Telecommunications

Department of Information and Cybersecurity Systems

Sergii Lazarenko, National Aviation University

Department of Information Security

Anna Korchenko, National Aviation University

Department of IT-Security

Iryna Manzhul, National Academy of the Security Service of Ukraine

Special Departament No. 2

References

Perera, R., Nand, P. (2017). Recent Advances in Natural Language Generation: A Survey and Classification of the Empirical Literature. Computing and Informatics, 36 (1), 1–32. doi: https://doi.org/10.4149/cai_2017_1_1

Kravchenko, Y., Leshchenko, O., Dakhno, N., Trush, O., Makhovych, O. (2019). Evaluating the Effectiveness of Cloud Services. 2019 IEEE International Conference on Advanced Trends in Information Theory (ATIT). doi: https://doi.org/10.1109/atit49449.2019.9030430

Pennington, J., Socher, R., Manning, C. (2014). Glove: Global Vectors for Word Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). doi: https://doi.org/10.3115/v1/d14-1162

Kiros, R., Zhu, Y., Salakhutdinov, R. R. (2016). Skip-thought vectors. Advances in Neural Information Processing Systems, 3276–3284.

Duchnovska, K. K. (2015). Formation of the research dynamic vector space. Shtuchnyi intelekt, 3-4, 28–36.

Barabash, O. V., Shevchenko, H. V., Dakhno, N. B., Open’ko, P. V., Kopiika, O. V. (2019). Target Programming with Multicriterial Restrictions Application to the Defense Budget Optimization. Advances in Military Technology, 14 (2), 213–229.

Kreines, E. M., Kreines, M. G. (2016). Control model for the alignment of the quality assessment of scientific documents based on the analysis of content-related context. Journal of Computer and Systems Sciences International, 55 (6), 938–947. doi: https://doi.org/10.1134/s1064230716050099

Musienko, A. P., Serdyuk, A. S. (2013). Lebesgue-type inequalities for the de la Valée-Poussin sums on sets of analytic functions. Ukrainian Mathematical Journal, 65 (4), 575–592. doi: https://doi.org/10.1007/s11253-013-0796-4

Musienko, A. P., Serdyuk, A. S. (2013). Lebesgue-type inequalities for the de la Vallée-poussin sums on sets of entire functions. Ukrainian Mathematical Journal, 65 (5), 709–722. doi: https://doi.org/10.1007/s11253-013-0808-4

Grigoryan, D. S. (2012). Kogerentnaya obrabotka dannyh v zadachah spektral'nogo analiza radiolokatsionnyh signalov so sverhrazresheniem. Zhurnal Radioelektroniki, 3. Available at: http://jre.cplire.ru/jre/mar12/1/text.pdf

Yevseiev, S., Korolyov, R., Tkachov, A., Laptiev, O., Opirskyy, I., Soloviova, O. (2020). Modification of the algorithm (OFM) S-box, which provides increasing crypto resistance in the post-quantum period. International Journal of Advanced Trends in Computer Science and Engineering (IJATCSE), 9 (5), 8725–8729. doi: https://doi.org/10.30534/ijatcse/2020/261952020

Bakiko, V. N., Popovych, P. V., Shvaichenko, V. B. (2018). Estimation of noise immunity of the communication channel under the influence of random interference. Visnyk Natsionalnoho tekhnichnoho universytetu "KhPI". Seriya: Tekhnika ta elektrofizyka vysokykh napruh, 14, 7–10.

Milov, O., Yevseiev, S., Ivanchenko, Y., Milevskyi, S., Nesterov, O., Puchkov, O. et. al. (2019). Development of the model of the antagonistic agents behavior under a cyber conflict. Eastern-European Journal of Enterprise Technologies, 4 (9 (100)), 6–19. doi: https://doi.org/10.15587/1729-4061.2019.175978

Berkman, L., Barabash, O., Tkachenko, O., Musienko, A., Laptiev, O., Salanda, I. (2020). The Intelligent Control System for infocommunication networks. International Journal of Emerging Trends in Engineering Research, 8 (5), 1920–1925. doi: https://doi.org/10.30534/ijeter/2020/73852020

Laptiev, O., Shuklin, G., Hohonianc, S., Zidan, A., Salanda, I. (2019). Dynamic Model of Cyber Defense Diagnostics of Information Systems With The Use of Fuzzy Technologies. 2019 IEEE International Conference on Advanced Trends in Information Theory (ATIT). doi: https://doi.org/10.1109/atit49449.2019.9030465

Srivastav, S., Gupta, S. (2020). Results with Matlab coding of Middle Graph of Cycle and its related graphs in context of Sum Divisor Cordial. International Journal of Emerging Trends in Engineering Research, 8 (2), 398–401. doi: https://doi.org/10.30534/ijeter/2020/26822020

Africa, A. D. M., Bulda, L. R., Marasigan, M. Z., Navarro, I. (2020). Binary Phase Shift Keying Simulation with MATLAB and SIMULINK. International Journal of Emerging Trends in Engineering Research, 8 (2), 288–294. doi: https://doi.org/10.30534/ijeter/2020/08822020

Mashkov, O. A., Sobchuk, V. V., Barabash, O. V., Dakhno, N. B. et. al. (2019). Improvement of variational-gradient method in dynamical systems of automated control for integro-differential models. Mathematical Modeling and Computing, 6 (2), 344–357. doi: https://doi.org/10.23939/mmc2019.02.344

Barabash, O., Dakhno, N., Shevchenko, H., Sobchuk, V. (2018). Integro-Differential Models of Decision Support Systems for Controlling Unmanned Aerial Vehicles on the Basis of Modified Gradient Method. 2018 IEEE 5th International Conference on Methods and Systems of Navigation and Motion Control (MSNMC), 94–97. doi: https://doi.org/10.1109/MSNMC.2018.8576310

Barabash, O., Laptiev, O., Tkachev, V., Maystrov, O., Krasikov, O., Polovinkin, I. (2020). The Indirect method of obtaining Estimates of the Parameters of Radio Signals of covert means of obtaining Information. International Journal of Emerging Trends in Engineering Research (IJETER), 8 (8), 4133–4139. doi: https://doi.org/10.30534/ijeter/2020/17882020

Rakushev, M., Permiakov, O., Lavrinchuk, O., Tarasenko, S., Kovbasiuk, S., Kravchenko, Y. (2019). Numerical Method of Integration on the Basis of Multidimensional Differential-Taylor Transformations. 2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T). doi: https://doi.org/10.1109/picst47496.2019.9061339


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Published
2021-01-29
How to Cite
Yevseiev, S., Laptiev, O., Lazarenko, S., Korchenko, A., & Manzhul, I. (2021). MODELING THE PROTECTION OF PERSONAL DATA FROM TRUST AND THE AMOUNT OF INFORMATION ON SOCIAL NETWORKS. EUREKA: Physics and Engineering, (1), 24-31. https://doi.org/10.21303/2461-4262.2021.001615
Section
Computer Science