Due to legal restrictions or restrictions related to companies' internal information policies, businesses often do not trust sensitive information to public cloud providers. One of the mechanisms to ensure the security of sensitive data in clouds is homomorphic encryption. Privacy-preserving neural networks are used to design solutions that utilize neural networks under these conditions. They exploit the homomorphic encryption mechanism, thus enabling the security of commercial information in the cloud. The main deterrent to the use of privacy-preserving neural networks is the large computational and spatial complexity of the scalar multiplication algorithm, which is the basic algorithm for computing mathematical convolution. In this paper, we propose a scalar multiplication algorithm that reduces the spatial complexity from quadratic to linear, and reduces the computation time of scalar multiplication by a factor of 1.38.

Accounts are an integral part of most modern information systems and provide their owners with the ability to authenticate within the system. This paper presents an analysis of existing methods for detecting simple account passwords in automated systems. Their advantages and disadvantages are listed. A method was developed to detect simple exploitable passwords that administrators can use to supplement other existing methods to increase the overall security of automated systems against threats from accounts potentially compromised by attackers. The method was based on the analysis of commands executed in automated or manual modes with the indication of credentials in plain text. Minimum password strength requirements are provided based on the security level. A special case was considered in which all passwords analyzed in this way were found explicitly in the system logs. We developed a unified definition of the classification of passwords into simple and strong, and also developed machine learning technology for their classification. The method offers a flexible adaptation to a specific system, taking into account the level of significance of the information being processed and the password policy adopted, expressed in the possibility of retraining the machine learning model. The experimental method using machine learning algorithms, namely the ensemble of decision trees, for classifying passwords into strong and potentially compromised by attackers based on flexible password strength criteria, showed high results. The performance of the method is also compared against other machine learning algorithms, specifically XGBoost, Random Forest, and Naive Bayes. The presented approach also solves the problem of detecting events related to the use and storage of credentials in plain text. We used the dataset of approximately 770,000 passwords, allowing the machine learning model to accurately classify 98% of the passwords by their significance levels.

The modern transformation of all spheres of life is associated with rapid technological and digital development. The educational sphere, focusing on external demands, systematically introduces technological and digital solutions into the processes of pedagogical systems functioning, focusing on the expansion of the educational space, on increasing the availability of educational services. In order to create a comfortable and effective digital environment in universities, it is important to take into account not only the level of development of digital competencies of students and teachers, but also their basic needs and requirements in this area. We set a goal to identify the most successful digital solutions used at this stage in university education from the students’ perspective and to consider the factors inhibiting these processes. To solve the problems, we used methods of theoretical analysis and empirical processing of research data. The main method was a survey of students using the questionnaires “Digital learning environment and online learning format” and “Quality of digital educational resources and organizational aspects of e-learning” developed by the authors. More than a thousand students from Russian universities took part in the survey. The results of the survey showed what means of communication in online learning are considered by students to be the most effective, what means of increasing productivity and what means of organization and support in online learning are most useful to them. The platforms and learning systems were considered in detail to understand the important characteristics of these services for students. The analysis highlighted the main areas in the digital transformation of the educational environment, which should be targeted for their development and improvement.