## 1 Introduction  
The growth of Big Data and the vision of a data-driven world opens up many interesting opportunities, while simultaneously revealing many unresolved problems [1, 2]. In particular, the new era of Big Data, which involved many researchers in the \"data management game\" and forced them to abandon the usual ways of designing, developing and implementing data management solutions, has exacerbated the problem of ensuring data security, since interest in the information circulating inside information systems (IS) has increased not only from legitimate users and owners, but also from attackers. For the latter, databases and data warehouses, as the most important information resources, are some of the most vulnerable and attractive elements of the IS. Security is one of the most important characteristics of the quality of the IS as a whole [3], and databases (DBs), as their main component, in particular. In this regard, the presence of an information protection system, as a complex of software, technical, cryptographic, organizational and other methods, means and measures that ensure the integrity, confidentiality, authenticity and availability of information in conditions of exposure to threats of a natural or artificial nature, has become an integral feature of any modern IS and databases. At the same time, in order to be able to verify the conclusions about the security level, it must be measured in some way. By now there have been many major efforts to measure or evaluate security, including using the Trusted Computer System Evaluation Criteria (TCSEC) [4], Information Technology Security Evaluation Criteria (ITSEC) [5], the Systems Security Engineering Capability Maturity Model (SSE-CMM) [6], Common Criteria [7]. However, as stated by [PERSON] et al. [8], each attempt had only limited success. To measure the security of databases in [9], it was proposed to use such metrics as the metrics for losses that arise from security incidents, the database security control costs metric, and confidence metrics. However, specific mathematical expressions allowing to determine their quantitative value, as recommended by the performance measurement guide for information security [10], have not been given. It was also proposed to use a metric consisting of several levels to evaluate the security of databases [11; 12]. A set of requirements that must be met by the system in order to achieve the corresponding level of security was listed for each level. However, this assessment was qualitative, although ranked. [PERSON] et al. [13] proposed to evaluate the security of database configurations based on a survey of database administrators about the use of certain best practices in the system under study, followed by the definition of a security index. Developing this approach, the Oracle Corporation has developed a tool [14] to assess the security of its databases, which analyzes database configurations, users, their rights, security policies and determines where sensitive data are located in order to identify security risks and improve the state of database security. However, all of these decisions are usually based on intuition and are fragmented. In many cases, there is no integral metric to evaluate the security degree of the database as a whole. In this connection, the objectives of our paper are: 1. To present a technique for evaluating the security of relational databases, the security system of which is based on the provisions of the enhanced theoretical Clements-Hoffman model, and the degree of security is calculated on the basis of a determined integral quantitative metric. This metric is the reciprocal of the total residual risk associated with the possibility of implementing threat in relation to a database object when using security measures; 2. To show the practical application of this technique for measuring the security of relational databases, including in order to identify a more secure one (in which solutions are used that provide a higher degree of database security).