The article provides an overview of some methods of personal identification based on his biometric data, as well as the basic principles of the implementation of these methods. A method for the reliable storage of biometric data on remote spatially distributed storages using algorithms based on the residual class system is also proposed.

Keywords: personal identification methods, biometric human identifiers, residual class system, data separation schemes

The paper discusses the use of threshold data separation schemes, as one of the promising areas of protection of both private and commercial information. In such schemes, parts of information are distributed among spatially distributed data storages, and information recovery is possible using at least one of the parts. The main disadvantage of such schemes is that they can be easily compromised, for example, if any part of the data is lost, or if the data is falsified by an intruder, it is impossible to localize the error, and, therefore, restore the original information. To solve this disadvantage, it is proposed to use schemes based on the system of residual classes [1] (RNS). The use of data separation algorithms based on the RNS will lead to a decrease in computational complexity, and, consequently, to a decrease in the load on data transmission media. Another advantage of SOCs is that they have corrective properties. A proportional increase in the number of information and redundant modules when using classical data separation schemes will lead to a sharp decrease in the reliability of the entire data exchange scheme. To solve this disadvantage, it is proposed to divide the information into groups, and then distribute it among the participants in the data exchange scheme.

Keywords: threshold separation of data, System of residual classes, reliability of data exchange schemes