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

• Main Authors: Serhii Yevseiev, Oleksandr Laptiev, Sergii Lazarenko, Anna Korchenko, Iryna Manzhul
• Format: Article
• Language: English
• Published: Scientific Route OÜ 2021-01-01
• Series: EUREKA: Physics and Engineering
• Subjects: social network; transfer; protection; user; parameter; information; metric; density; cycle
• Online Access: http://journal.eu-jr.eu/engineering/article/view/1615
• ISSN: 2461-4254; 2461-4262

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