## 3 DT Security and Reliability  
Our main approach is to use RRNS to ensure the security of data transmission. Let us discuss and compare well-known solutions for providing reliability and security of distributed data storage and transmission. Four main methods are used to ensure reliability [17]: Replication, Erasure code, Erasure code modifications, and Error correction code. [PERSON] et al. [18] presented a modified data replication method, providing a high encoding and decoding speed. However, it requires additional cryptographic primitives to ensure security and has a high redundancy compared to erasure codes. Many different modifications of erasure codes have been proposed to create reliable methods for DT. The joint use of error correction and erasure codes maintains system performance and minimizes the load on the data transmission network when recovering lost fragments [19; 20]. Erasure codes based on the RRNS [21] allow data to be processed in the encoded form [14]. So, it can be used both in the design of low-power wireless data transmission devices and DT. Secure DTs are based on the use of cryptographic primitives--symmetric encryption algorithms (AES) and digital signatures based on RSA (Rivest, Shamir, Adleman) [22]. The advantages of these approaches are high speed of encryption and decryption and low data redundancy. The disadvantage is that an error in the encrypted data leads to its loss. To eliminate this shortcoming, the use of additional mechanisms for accessing data for a long time is required [23]. When building secure and reliable DT, the following methods are used: elliptic cryptography and erasure codes [24; 25], access structures [26; 27], error correction codes [28; 29], graph-based algorithms and modified data replication algorithm [30], attribute-based encryption [31], etc. An alternative approach is to use recovery codes [20], erasure codes, and error correction codes based on RRNS [19]. However, recovery codes and erasure codes do not allow encoded data processing. Homomorphic calculations process encoded data without additional computational costs for decoding. A significant breakthrough in the field of homomorphic computing came from the work of [PERSON] [32]. The authors proposed a fully homomorphic scheme to perform both addition and multiplication. The main disadvantages of this algorithm are significant data redundancy and lack of control over the results of arithmetic operations. Particular attention should be paid to the distributed data storage model proposed in [33], guaranteeing security, privacy, homomorphism, reliability, and scalability. The authors propose two approaches to building systems based on homomorphic access structures in RRNS, with RRNS moduli being used as secret keys stored by users. Data processing  
Figure 3: Data packet model. leads to an exponential increase in the load on the network and memory, which makes this model inapplicable in practice in modern conditions. Access structures [34; 35] ensure data security and confidentiality. RRNS implements the same functionality as the Mignotte scheme but allows you to control the results of data processing. DT is also characterized by collusion risks [36]. Several approaches have been developed to prevent cloud collusion [26]. As mentioned above, the non-stationarity of the cloud environment reduces the efficiency, performance, reliability, and security of the system. The adaptive paradigm reduces uncertainty but is rarely used in cloud computing [36]. Let us consider the following scenario. The user has confidential data and decides not to send it using a single path. He divides them into several shares and transmits them in different paths between nodes. There are several types of security threats in this scenario. Deliberate threats include unauthorized access to information, interception, falsification, hacker attacks, etc., in one or more nodes. Random threats include errors, crashes, etc.