# Regional Financial Data Processing Based on Distributed Decoding Technology

Authors: You Zhou
Journal: Security and Communication Networks
 (2022)

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## Abstract

Aiming at the problems of low storage efficiency, small storage space, and poor stability of traditional financial data storage systems, a dynamic and secure financial data storage system based on distributed decoding is designed. The system uses a cloud security storage service system to store a large number of financial data obtained from the sensing layer and achieve the reasonable scheduling of financial dynamic cloud data through the constraint software function. In addition, a distributed security computing scheme based on hierarchical coding is proposed, and the influence of different subtask sizes and different computing layers on the scheme is analyzed. The results show that the distributed security computing scheme based on hierarchical coding can significantly reduce the completion time of the overall computing task and enhance the stability of dynamic financial data storage. This system has a good storage performance of financial big data, and its efficiency is good, which provides strong support for the financial cause of our country.


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## Body

## 1. Introduction

The Internet of things (IoT) is a new form of network connection based on Internet, which turns the communication between people into the communication between things, and promotes the rapid development of society and economy. IoT finance means that financial institutions make full use of the thinking and technology of IoT to organically integrate information flow, capital flow, and logistics in economic activities such as user production scene and life scene, which provides users with financial services such as deposits, loan, and foreign exchange [1, 2]. Its financial services and innovation are oriented to the IoT, which can give the property of the real estate in the information system of the IoT through the IoT technology. On the one hand, financial institutions can clearly grasp the source, quality, location, and other information of physical assets and reduce the intensity of artificial supervision of physical assets and the operational risk of financial business; on the other hand, with the support of IoT technology, financial institutions can fully understand the business status and transaction details of enterprises, which is helpful for financial institutions to control the whole process of purchasing, production, sales, and even users’ operations in real time, thus realizing the whole life cycle management of chattel pledge by financial institutions and effectively improving the level of risk prevention and control. IoT finance has effectively solved the problem of information asymmetry commonly existing in traditional finance, which plays an important role in optimizing the allocation of social resources and reconstructing the risk management system of financial institutions.With the rapid development of the economy, the amount of financial data has gradually become huge, which makes the traditional financial data storage system solve a series of problems such as small storage capacity, low storage efficiency, and unstable storage system performance [3, 4]. The leakage of financial data will bring huge losses to enterprises, so the establishment of a dynamic and secure financial data storage system based on the IoT can not only realize the efficient dynamic storage of financial data but also effectively restrain the loss of financial data, improve the dynamic management performance of financial data, save the funds of financial enterprises, avoid the maintenance concerns of hardware and software, and enhance the stability performance of overall financial data dynamic storage.IoT is one of the most important applications in the future of mobile communication. Consider a typical IoT application scenario where a mobile wireless access point broadcasts messages to individual users. After receiving the message, each user needs to decode the message and make a response [5]. In general, after receiving the message from the transmission unit in the uplink center, it is assumed that the message is sent to the transmission center by the transmission unit in the traditional scenario. In a distributed computing system, the processing speed of each computing node is different, so it takes a different time to complete the assigned subtasks. Because the computing tasks are distributed among the computing nodes, the master node needs to wait for all the computing nodes to complete the subtasks and return the calculation results before the final results can be recovered [6].Aiming at the problems of low storage efficiency, small storage space, and poor stability of traditional financial data storage systems, a financial data dynamic storage system based on IoT is designed. This paper analyzes the influence of different subtask sizes and different computational layers on the distributed secure computing scheme based on hierarchical coding.


## 2. Related Works

### 2.1. Financial Data Storage

In order to meet the requirements of a large amount of financial data storage, the design of a financial big data dynamic security system based on the IoT came into being. For start-up financial enterprises, according to the design of this system, it can save a lot of money, and we do not have to worry about the maintenance of hardware and software. The advantages of high efficiency and large space of financial big data storage systems are crucial to the customers of financial enterprises [7]. If the data are lost, it will not only endanger the personal information security of customers but also cause the loss of customers to financial enterprises, which will cause huge economic losses. The big data storage settings of IP resource networks are proposed in the literature [8], where the monitoring function of IP is used to monitor the data in real time. If the data needs to be stored, the function of the IP can react quickly and complete a series of data storage processes. However, the complexity of the IP data network is extremely high, and its storage system space is small, which is not suitable for the current network technology, so it cannot meet the storage needs of a large number of financial data. In the literature [9], a system design method of dynamic data mass storage based on policy scheduling is proposed. Under the condition of certain load balance, the massive data are classified and stored again, which can realize the clustering analysis of financial big data. However, the design of this system is easy to be constrained by storage performance, which will lead to an increase in system design cost. The storage system design of SAN technology is proposed in the literature [10]. Although the design method of this system can make the storage speed of data faster, its technology is not mature, and the storage performance is still under study.


### 2.2. Distributed Decoding Technology

Due to the dynamic characteristics of the network, users as relay nodes need to change frequently according to the needs of the network so as to achieve complex network scheduling. In order to solve this problem, as a combination of coding theory and distributed computing, the recently proposed coding distributed computing is a promising solution. In distributed computing, the master node and the worker may not belong to the same entity. For example, a data owner may want to perform tasks on a large number of datasets that require intensive computation. Computing tasks may be split and distributed to multiple worker nodes on third-party computing services [11]. However, in the financial sector, these calculations may involve sensitive data [12]. In this case, the worker node may obtain the relevant original data information, and the malicious worker node may intentionally provide the wrong data, thus causing the data deviation of the calculation model [13]. In addition, in some cases, the dataset does not belong to either the master node or the worker node, so the original data set must be protected from both sides. Master-workers architecture is a common organization mode in distributed systems. Workers are responsible for the actual workload and need to design an efficient single engine, with global redundancy.Aiming at the problem that the existing coding calculation methods will increase the computational complexity of sparse matrix multiplication, Wang et al. proposed a coding sparse matrix multiplication method in [14], but the calculation results of this method only depend on the calculation results of a group of fastest worker nodes and completely ignore the calculation results of the slowest worker nodes. In the coding sparse matrix multiplication method, the straggler nodes are modeled as very slow nodes that are considered to have done nothing. In terms of error correcting codes, they are considered erasure errors. While in the actual distributed system, the calculation speed of the straggler node is slow, but when the fast node completes all the tasks, the left behind node can only complete part of the task. In addition, for the matrix multiplication problem under distributed security coding, Yang and Lee proposed a distributed security computing method based on polynomial code [15], but the completion time of the method depends on a group of fastest worker nodes, and the work completed by the straggler is ignored.


## 2.1. Financial Data Storage

In order to meet the requirements of a large amount of financial data storage, the design of a financial big data dynamic security system based on the IoT came into being. For start-up financial enterprises, according to the design of this system, it can save a lot of money, and we do not have to worry about the maintenance of hardware and software. The advantages of high efficiency and large space of financial big data storage systems are crucial to the customers of financial enterprises [7]. If the data are lost, it will not only endanger the personal information security of customers but also cause the loss of customers to financial enterprises, which will cause huge economic losses. The big data storage settings of IP resource networks are proposed in the literature [8], where the monitoring function of IP is used to monitor the data in real time. If the data needs to be stored, the function of the IP can react quickly and complete a series of data storage processes. However, the complexity of the IP data network is extremely high, and its storage system space is small, which is not suitable for the current network technology, so it cannot meet the storage needs of a large number of financial data. In the literature [9], a system design method of dynamic data mass storage based on policy scheduling is proposed. Under the condition of certain load balance, the massive data are classified and stored again, which can realize the clustering analysis of financial big data. However, the design of this system is easy to be constrained by storage performance, which will lead to an increase in system design cost. The storage system design of SAN technology is proposed in the literature [10]. Although the design method of this system can make the storage speed of data faster, its technology is not mature, and the storage performance is still under study.


## 2.2. Distributed Decoding Technology

Due to the dynamic characteristics of the network, users as relay nodes need to change frequently according to the needs of the network so as to achieve complex network scheduling. In order to solve this problem, as a combination of coding theory and distributed computing, the recently proposed coding distributed computing is a promising solution. In distributed computing, the master node and the worker may not belong to the same entity. For example, a data owner may want to perform tasks on a large number of datasets that require intensive computation. Computing tasks may be split and distributed to multiple worker nodes on third-party computing services [11]. However, in the financial sector, these calculations may involve sensitive data [12]. In this case, the worker node may obtain the relevant original data information, and the malicious worker node may intentionally provide the wrong data, thus causing the data deviation of the calculation model [13]. In addition, in some cases, the dataset does not belong to either the master node or the worker node, so the original data set must be protected from both sides. Master-workers architecture is a common organization mode in distributed systems. Workers are responsible for the actual workload and need to design an efficient single engine, with global redundancy.Aiming at the problem that the existing coding calculation methods will increase the computational complexity of sparse matrix multiplication, Wang et al. proposed a coding sparse matrix multiplication method in [14], but the calculation results of this method only depend on the calculation results of a group of fastest worker nodes and completely ignore the calculation results of the slowest worker nodes. In the coding sparse matrix multiplication method, the straggler nodes are modeled as very slow nodes that are considered to have done nothing. In terms of error correcting codes, they are considered erasure errors. While in the actual distributed system, the calculation speed of the straggler node is slow, but when the fast node completes all the tasks, the left behind node can only complete part of the task. In addition, for the matrix multiplication problem under distributed security coding, Yang and Lee proposed a distributed security computing method based on polynomial code [15], but the completion time of the method depends on a group of fastest worker nodes, and the work completed by the straggler is ignored.


## 3. Design of the Financial Data Security Storage System Based on Distributed Decoding

### 3.1. Overall Structure

IoT is an important part of the new generation of information technology, which takes the Internet as the core. On this basis, it extends to the information exchange between things; that is, the network that connects any object with the Internet carries out information exchange and realizes a series of measures such as intelligent identification, positioning, and tracking management of items [16]. As shown in Figure 1, the structure of the financial data dynamic security depository system based on IoT includes the perception layer, storage layer, and application layer.(1)
Perception layer. As the basis of financial data collection, the perception layer is at the forefront of the application of the IoT and plays a decisive role in the realization of the function of the IoT. It is composed of a wireless sensor network, radio frequency identification system, and wireless video monitoring network, which is used for the collection and fusion of financial data and the transmission of previous financial data.(2)
Storage layer. The storage layer is the core part of the system, which stores a large amount of financial data collected from the perception layer, and is divided into the IoT information storage center and government IoT security center. The IoT information storage center will store a large amount of financial data sorted out according to specific statistical specifications through the cloud security storage service system, and it is only open to users who meet the access rights of financial data. When statistical users want to query financial data through the system when they register for the first time, the IoT security center is responsible for calculating and generating passwords and obtaining passwords that match the user’s identity attributes so as to ensure that users can successfully access the statistical financial data.(3)
Application layer. The application layer obtains the corresponding financial data from the storage layer based on different types of user needs, completes the statistical business logic of user requirements, and displays the statistical results of various businesses with a graphical visual interface.Figure 1
System architecture.


### 3.2. Storage Management Service of Financial Data

#### 3.2.1. Overall Architecture

The storage layer uses a cloud security storage service system to store a large amount of financial data, and integrating the unique security storage technology into the process of financial data storage is the core technology of cloud security, which includes a large-scale secure capture framework and cumulative security network package. According to the abnormal information data fed back by each terminal, the service system rapidly develops the corresponding open-source package and shares financial data with the whole network. The architecture of the storage management service is shown in Figure2.Figure 2
Storage management service of financial data.The traditional financial data storage methods can no longer meet the needs of the step development of financial data at this stage while the way to build the security management log on the Internet through virtualization is the financial data cloud security deposit service system with cloud computing as the core. Therefore, the system storage layer adopts a cloud security storage service system, which can realize cloud security storage of massive financial data.


#### 3.2.2. Scheduling Algorithm

The financial data scheduling algorithm is of great value to the overall cloud security storage process, and cloud data scheduling is realized through constraint software functions. The calculation formula of financial data scheduling is as follows:(1)Ea=∑i=1nθikita+ωda.While the calculation formula of secure storage is as follows:(2)Rhmin=∑i=1nθi−hi−ωd1ω∑i=1nkihi.where θ1,θ2,…,θi∈2i is data vectors and data loss; d and ω represent regularization function and conjugate function, respectively; a and h represent the original data variable and the dual data function variable, respectively. t⩾0 is the parameters of regularization function.Each storage variableoi has a corresponding data variable ki, and the relationship between data variables and storage variables is as follows:(3)θikita=kita−oi2⋅da2=ai.According to formula (3), the constrained function is obtained as follows:(4)famin=12Ca−b33+ta2,where matrix C is the permutation of x data.


### 3.3. Security Computing Model Based on Distributed Decoding

#### 3.3.1. Matrix Partition

Firstly, the taskC=ATB is divided into L layer computing tasks. Since there are N worker nodes in each layer, the total number of computing tasks is LN. Specifically, the computation tasks related to each layer are performed by using N worker nodes, and each worker node starts from the first layer to compute and proceeds layer by layer in sequence [17]. Then, the input matrices A and B are divided into m and n parts, respectively, and mn+m+n≤LN. Then, A and B can be expressed as(5)A=A1,A2,…,Am,B=B1,B2,…,Bn.


#### 3.3.2. Matrix Coding

The master node securely codes the two input submatrices with the random matricesRA and RB, which are composed of elements extracted from independent and identically distributed random variables. The elements are independent of the input matrices A and B, respectively, which have the same size as the submatrices of matrices A and B, respectively, that is, RA∈Fqs×f/m and RB∈Fqs×t/n.The master node then uses the polynomial code onRA,A1,…,Am and RB,B1,…,Bn to generate the security code set A˜ and B˜, whose magnitude is LN. The master node makes the security code set A˜i−1L+1,A˜i−1L+2,…,A˜iL and B˜i−1L+1,B˜i−1L+2,…,B˜iL allocated to the worker node i, that is(6)A˜i=RAxi0+∑j=1mAjxij,B˜i=RBxi0+∑k=1nBkxikm+1−1,i∈1,2,…,LN.The polynomial exponents of the two safe coding sets need to be carefully selected during matrix coding to ensure that all theAjTBk terms in the submatrix C˜i=A˜iTB˜i have different xi exponents where j∈1,2,…,m,k∈1,2,…,n.


#### 3.3.3. Node Computing Tasks

The worker node i saves thel-pair safety coding submatrix sent from the master node, i.e.,(7)A˜i−1L+1,B˜i−1L+1,A˜i−1L+2,B˜i−1L+2,…,A˜iL,B˜iL,i∈1,2,…,N.Then, worker nodei starts to calculate the safety coding submatrix of the first layer and returns the result to the master node after the calculation. Then, the safety coding submatrix of the next layer is calculated in sequence, and the submatrix is calculated as(8)C˜i=A˜iTB˜i=RATRBxi0+∑j=1mAjTRBxij+∑k=1nRATBkxikm+1−1+∑j=1m∑k=1nAjTBkxij+km+1−1,i∈1,2,…,LN.Since allxi are set differently in the coding scheme, so the submatrix C˜i is the function value of a polynomial of degree mn+m+n−1 at the point x=xi. It can be obtained that the recovery threshold K of the distributed security computing scheme based on layered coding is mn+m+n, which is the same as that of the distributed security computing scheme based on polynomial code. Therefore, the distributed security computing scheme based on hierarchical coding reaches the upper bound of the optimal recovery threshold, namely(9)K=mn+m+n.At the same time,h(x) can be calculated as(10)hx=RATRBx0+∑j=1m−1AjTRBxj+AmTRB+RATB1xm+∑k=2nRATBkxikm+1−1+∑j=1m∑k=1nAjTBkxij+km+1−1.


#### 3.3.4. Decoding of Calculation Results

After the master node receives submatrix multiplication from the fastestmn+m+n, worker nodes can decode the final matrix multiplication result C. From the coefficient of h(x), the master node can be decoded to obtain the final result C = A^T B.


## 3.1. Overall Structure

IoT is an important part of the new generation of information technology, which takes the Internet as the core. On this basis, it extends to the information exchange between things; that is, the network that connects any object with the Internet carries out information exchange and realizes a series of measures such as intelligent identification, positioning, and tracking management of items [16]. As shown in Figure 1, the structure of the financial data dynamic security depository system based on IoT includes the perception layer, storage layer, and application layer.(1)
Perception layer. As the basis of financial data collection, the perception layer is at the forefront of the application of the IoT and plays a decisive role in the realization of the function of the IoT. It is composed of a wireless sensor network, radio frequency identification system, and wireless video monitoring network, which is used for the collection and fusion of financial data and the transmission of previous financial data.(2)
Storage layer. The storage layer is the core part of the system, which stores a large amount of financial data collected from the perception layer, and is divided into the IoT information storage center and government IoT security center. The IoT information storage center will store a large amount of financial data sorted out according to specific statistical specifications through the cloud security storage service system, and it is only open to users who meet the access rights of financial data. When statistical users want to query financial data through the system when they register for the first time, the IoT security center is responsible for calculating and generating passwords and obtaining passwords that match the user’s identity attributes so as to ensure that users can successfully access the statistical financial data.(3)
Application layer. The application layer obtains the corresponding financial data from the storage layer based on different types of user needs, completes the statistical business logic of user requirements, and displays the statistical results of various businesses with a graphical visual interface.Figure 1
System architecture.


## 3.2. Storage Management Service of Financial Data

### 3.2.1. Overall Architecture

The storage layer uses a cloud security storage service system to store a large amount of financial data, and integrating the unique security storage technology into the process of financial data storage is the core technology of cloud security, which includes a large-scale secure capture framework and cumulative security network package. According to the abnormal information data fed back by each terminal, the service system rapidly develops the corresponding open-source package and shares financial data with the whole network. The architecture of the storage management service is shown in Figure2.Figure 2
Storage management service of financial data.The traditional financial data storage methods can no longer meet the needs of the step development of financial data at this stage while the way to build the security management log on the Internet through virtualization is the financial data cloud security deposit service system with cloud computing as the core. Therefore, the system storage layer adopts a cloud security storage service system, which can realize cloud security storage of massive financial data.


### 3.2.2. Scheduling Algorithm

The financial data scheduling algorithm is of great value to the overall cloud security storage process, and cloud data scheduling is realized through constraint software functions. The calculation formula of financial data scheduling is as follows:(1)Ea=∑i=1nθikita+ωda.While the calculation formula of secure storage is as follows:(2)Rhmin=∑i=1nθi−hi−ωd1ω∑i=1nkihi.where θ1,θ2,…,θi∈2i is data vectors and data loss; d and ω represent regularization function and conjugate function, respectively; a and h represent the original data variable and the dual data function variable, respectively. t⩾0 is the parameters of regularization function.Each storage variableoi has a corresponding data variable ki, and the relationship between data variables and storage variables is as follows:(3)θikita=kita−oi2⋅da2=ai.According to formula (3), the constrained function is obtained as follows:(4)famin=12Ca−b33+ta2,where matrix C is the permutation of x data.


## 3.2.1. Overall Architecture

The storage layer uses a cloud security storage service system to store a large amount of financial data, and integrating the unique security storage technology into the process of financial data storage is the core technology of cloud security, which includes a large-scale secure capture framework and cumulative security network package. According to the abnormal information data fed back by each terminal, the service system rapidly develops the corresponding open-source package and shares financial data with the whole network. The architecture of the storage management service is shown in Figure2.Figure 2
Storage management service of financial data.The traditional financial data storage methods can no longer meet the needs of the step development of financial data at this stage while the way to build the security management log on the Internet through virtualization is the financial data cloud security deposit service system with cloud computing as the core. Therefore, the system storage layer adopts a cloud security storage service system, which can realize cloud security storage of massive financial data.


## 3.2.2. Scheduling Algorithm

The financial data scheduling algorithm is of great value to the overall cloud security storage process, and cloud data scheduling is realized through constraint software functions. The calculation formula of financial data scheduling is as follows:(1)Ea=∑i=1nθikita+ωda.While the calculation formula of secure storage is as follows:(2)Rhmin=∑i=1nθi−hi−ωd1ω∑i=1nkihi.where θ1,θ2,…,θi∈2i is data vectors and data loss; d and ω represent regularization function and conjugate function, respectively; a and h represent the original data variable and the dual data function variable, respectively. t⩾0 is the parameters of regularization function.Each storage variableoi has a corresponding data variable ki, and the relationship between data variables and storage variables is as follows:(3)θikita=kita−oi2⋅da2=ai.According to formula (3), the constrained function is obtained as follows:(4)famin=12Ca−b33+ta2,where matrix C is the permutation of x data.


## 3.3. Security Computing Model Based on Distributed Decoding

### 3.3.1. Matrix Partition

Firstly, the taskC=ATB is divided into L layer computing tasks. Since there are N worker nodes in each layer, the total number of computing tasks is LN. Specifically, the computation tasks related to each layer are performed by using N worker nodes, and each worker node starts from the first layer to compute and proceeds layer by layer in sequence [17]. Then, the input matrices A and B are divided into m and n parts, respectively, and mn+m+n≤LN. Then, A and B can be expressed as(5)A=A1,A2,…,Am,B=B1,B2,…,Bn.


### 3.3.2. Matrix Coding

The master node securely codes the two input submatrices with the random matricesRA and RB, which are composed of elements extracted from independent and identically distributed random variables. The elements are independent of the input matrices A and B, respectively, which have the same size as the submatrices of matrices A and B, respectively, that is, RA∈Fqs×f/m and RB∈Fqs×t/n.The master node then uses the polynomial code onRA,A1,…,Am and RB,B1,…,Bn to generate the security code set A˜ and B˜, whose magnitude is LN. The master node makes the security code set A˜i−1L+1,A˜i−1L+2,…,A˜iL and B˜i−1L+1,B˜i−1L+2,…,B˜iL allocated to the worker node i, that is(6)A˜i=RAxi0+∑j=1mAjxij,B˜i=RBxi0+∑k=1nBkxikm+1−1,i∈1,2,…,LN.The polynomial exponents of the two safe coding sets need to be carefully selected during matrix coding to ensure that all theAjTBk terms in the submatrix C˜i=A˜iTB˜i have different xi exponents where j∈1,2,…,m,k∈1,2,…,n.


### 3.3.3. Node Computing Tasks

The worker node i saves thel-pair safety coding submatrix sent from the master node, i.e.,(7)A˜i−1L+1,B˜i−1L+1,A˜i−1L+2,B˜i−1L+2,…,A˜iL,B˜iL,i∈1,2,…,N.Then, worker nodei starts to calculate the safety coding submatrix of the first layer and returns the result to the master node after the calculation. Then, the safety coding submatrix of the next layer is calculated in sequence, and the submatrix is calculated as(8)C˜i=A˜iTB˜i=RATRBxi0+∑j=1mAjTRBxij+∑k=1nRATBkxikm+1−1+∑j=1m∑k=1nAjTBkxij+km+1−1,i∈1,2,…,LN.Since allxi are set differently in the coding scheme, so the submatrix C˜i is the function value of a polynomial of degree mn+m+n−1 at the point x=xi. It can be obtained that the recovery threshold K of the distributed security computing scheme based on layered coding is mn+m+n, which is the same as that of the distributed security computing scheme based on polynomial code. Therefore, the distributed security computing scheme based on hierarchical coding reaches the upper bound of the optimal recovery threshold, namely(9)K=mn+m+n.At the same time,h(x) can be calculated as(10)hx=RATRBx0+∑j=1m−1AjTRBxj+AmTRB+RATB1xm+∑k=2nRATBkxikm+1−1+∑j=1m∑k=1nAjTBkxij+km+1−1.


### 3.3.4. Decoding of Calculation Results

After the master node receives submatrix multiplication from the fastestmn+m+n, worker nodes can decode the final matrix multiplication result C. From the coefficient of h(x), the master node can be decoded to obtain the final result C = A^T B.


## 3.3.1. Matrix Partition

Firstly, the taskC=ATB is divided into L layer computing tasks. Since there are N worker nodes in each layer, the total number of computing tasks is LN. Specifically, the computation tasks related to each layer are performed by using N worker nodes, and each worker node starts from the first layer to compute and proceeds layer by layer in sequence [17]. Then, the input matrices A and B are divided into m and n parts, respectively, and mn+m+n≤LN. Then, A and B can be expressed as(5)A=A1,A2,…,Am,B=B1,B2,…,Bn.


## 3.3.2. Matrix Coding

The master node securely codes the two input submatrices with the random matricesRA and RB, which are composed of elements extracted from independent and identically distributed random variables. The elements are independent of the input matrices A and B, respectively, which have the same size as the submatrices of matrices A and B, respectively, that is, RA∈Fqs×f/m and RB∈Fqs×t/n.The master node then uses the polynomial code onRA,A1,…,Am and RB,B1,…,Bn to generate the security code set A˜ and B˜, whose magnitude is LN. The master node makes the security code set A˜i−1L+1,A˜i−1L+2,…,A˜iL and B˜i−1L+1,B˜i−1L+2,…,B˜iL allocated to the worker node i, that is(6)A˜i=RAxi0+∑j=1mAjxij,B˜i=RBxi0+∑k=1nBkxikm+1−1,i∈1,2,…,LN.The polynomial exponents of the two safe coding sets need to be carefully selected during matrix coding to ensure that all theAjTBk terms in the submatrix C˜i=A˜iTB˜i have different xi exponents where j∈1,2,…,m,k∈1,2,…,n.


## 3.3.3. Node Computing Tasks

The worker node i saves thel-pair safety coding submatrix sent from the master node, i.e.,(7)A˜i−1L+1,B˜i−1L+1,A˜i−1L+2,B˜i−1L+2,…,A˜iL,B˜iL,i∈1,2,…,N.Then, worker nodei starts to calculate the safety coding submatrix of the first layer and returns the result to the master node after the calculation. Then, the safety coding submatrix of the next layer is calculated in sequence, and the submatrix is calculated as(8)C˜i=A˜iTB˜i=RATRBxi0+∑j=1mAjTRBxij+∑k=1nRATBkxikm+1−1+∑j=1m∑k=1nAjTBkxij+km+1−1,i∈1,2,…,LN.Since allxi are set differently in the coding scheme, so the submatrix C˜i is the function value of a polynomial of degree mn+m+n−1 at the point x=xi. It can be obtained that the recovery threshold K of the distributed security computing scheme based on layered coding is mn+m+n, which is the same as that of the distributed security computing scheme based on polynomial code. Therefore, the distributed security computing scheme based on hierarchical coding reaches the upper bound of the optimal recovery threshold, namely(9)K=mn+m+n.At the same time,h(x) can be calculated as(10)hx=RATRBx0+∑j=1m−1AjTRBxj+AmTRB+RATB1xm+∑k=2nRATBkxikm+1−1+∑j=1m∑k=1nAjTBkxij+km+1−1.


## 3.3.4. Decoding of Calculation Results

After the master node receives submatrix multiplication from the fastestmn+m+n, worker nodes can decode the final matrix multiplication result C. From the coefficient of h(x), the master node can be decoded to obtain the final result C = A^T B.


## 4. Experiment and Analysis

### 4.1. Experimental Parameter Setting

In this experiment, input matricesA∈Fqs×r and B∈Fqs×t are randomly generated by using the Python NumPy function library, where s and r, respectively, represent the number of rows and columns of input matrix A, and s and t, respectively, represent the number of rows and columns of input matrix B. q represents the range of elements in the input matrices A and B. Similarly, random matrices RA∈Fqs×1/m and RB∈Fqs×1/n are generated in A similar way as input matrices A and B and have the same size as the submatrices of input matrices A and B. To simplify the calculation, r/m and t/n are guaranteed to be integers. The parameters m and n represent the number of partition columns of the input matrix A and B, respectively. The parameter L represents the number of computational layers of the distributed secure computing scheme based on hierarchical coding.


### 4.2. Simulation Results of Distributed Decoding

Firstly, the completion time of the distributed security computing scheme based on layered coding is compared under different sizes of a computing task, that is, the change of the completion time of the distributed security computing scheme based on layered coding under differentm and n conditions. Two input matrices A and B and two random matrices RA and RB are generated, where s=3000, r=2400,t=2400,q=256.The number of computing layersL = 2, the number of worker nodes N = 13, and the number of dropped nodes μ=1 were designed. In addition, four subtask sizes, namely m=n=1,m=n=2,m=n=3, and m=n=4 were considered, and each experiment was repeated 10 times, as shown in Figure 3.Figure 3
Comparison of distributed decoding completion time under different tasks.As shown in Figure4, when m and N increase, that is, the master node allocates smaller computing tasks to the worker node, and the completion time of the master node gradually decreases. When m = 1 n = 1, the average completion time of the whole computing task is 129.35 s; while when m = 2 n = 2, the average completion time of the whole computing task is 36.69 s, which is 71.64% lower than that of m = 1 n = 1. Moreover, when m = 4, n = 4, compared with m = 2 n = 2, the average completion time of the overall task is reduced by 63.64%. It can be seen that reducing the size of subtasks can reduce the completion time of the overall computing task.Figure 4
Comparison of distributed decoding completion time under different computing layers.In addition, the completion time of distributed security computing schemes based on hierarchical coding is compared under different computing layers, that is, the change of completion time of distributed security computing schemes based on hierarchical coding under differentL conditions. Two input matrices A and B and two random matrices AR and BR are generated in this experiment, where s = 3000, r = 2400, t = 2400, and q = 256. The number of design worker nodes n = 13, and the number of missing nodes μ = 1; the completion time is counted every 1 from 1 to 4. In order to achieve better computing performance, different m and N are selected here, where m = n = 2, m = n = 3, and m = n = 4 are selected here, and three tests are carried out for each group of M and N under the same layer number to get the average value. The results are shown in Figure 4.Whenl > 1, compared with the polynomial code-based distributed security computing scheme, the layered coding-based distributed security computing scheme has less completion time. For example, when L = 1, m = 2, and n = 2, the completion time of the distributed security computing scheme based on polynomial code is 57.06 s, and when L = 2, the distributed security computing scheme based on hierarchical coding has a completion time of 57.06 s, while under the condition of n = 4, the optimal completion time is 13.34 s, so the completion time of the overall calculation task is reduced by 76.62%. When l < 2, with the increase of the number of computing layers, the completion time of the distributed security computing scheme based on layered coding is gradually increased. The main reason is that with the increase of L, the encoding time and decoding time of the scheme are also increasing. When the sum of coding time and decoding time exceeds the benefits of layered coding, the completion time of the whole computing task will increase. Therefore, in the distributed security computing scheme based on hierarchical coding, when the number of computing layers L is in a certain range, the completion time of the overall computing task will gradually decrease with the increase of the number of computing layers L.


### 4.3. System Testing

According to the dynamic security deposit system, a test attachment is established to upload the page, and the scenario of uploading two financial data on one page is simulated. Based on the client of the stress test, the script for accessing the attachment upload page is created, and the script for multiple users to synchronously call the script is launched by using the stress test tool. The response time and throughput of financial data upload are tested. The upload results are shown in Table1.Table 1
Test results of financial data upload.
SceneNumber of concurrent users90% response time/sFinancial data/numberData volume/mbDuration/minThroughput/mb/s1104.6781047535.54551.71122012.0921167602.20452.01133013.5311327667.72552.15145023.0751299650.78752.101510024.0761427715.032520.056Considering the number of financial data and response time, when the number of users is between 10 and 30, the number of financial data increases gradually. When the number of users is between 30 and 100, the number of financial data begins to decrease. When the number of users reaches 100 users, the number of financial data increases instantaneously due to the increase in the start and stop time. The response time was significantly increased when more than 30 users were used. Under the current architecture, the system can support 30 concurrent users to upload financial data at the same time, with a financial data upload rate of about 208.9/min, and the average throughput reaches 2 Mb/s. When the concurrent users are in 10 ∼100 periods, the number of failed events in this system to upload financial data is 0, and there is no abnormal phenomenon, which proves that the system can still ensure the stable operation of the financial data upload operation under high pressure.In order to verify the performance of the system, this paper designs a dynamic secure financial data storage system based on the IoT. The experiment analyzes the process of safe storage and management of financial data in a state-owned bank. The storage efficiency of the traditional financial data storage system is compared with that of this system. The results are shown in Figure5.Figure 5
Comparison of storage efficiency of different systems.The analysis of Figure5 shows that the depository system in this paper can improve the data storage efficiency. With the continuous improvement of data volume, its scheduling efficiency can be close to 100%. Compared with the traditional storage system, it better demonstrates the rationality and value of its design.The comparison of different storage space sizes between traditional storage systems and IoT-based storage systemsn is shown in Figure 6.Figure 6
Comparison of storage space of different systems.As can be seen from Figure6, the dynamic security storage system of financial data in the IoT environment has a larger space to store data, which can better store a large amount of data and ensure the security of data compared with the traditional storage system. The dynamic and secure financial big data storage system based on the IoT has high data storage speed and large storage space and can realize a large number of data storage and reading.


## 4.1. Experimental Parameter Setting

In this experiment, input matricesA∈Fqs×r and B∈Fqs×t are randomly generated by using the Python NumPy function library, where s and r, respectively, represent the number of rows and columns of input matrix A, and s and t, respectively, represent the number of rows and columns of input matrix B. q represents the range of elements in the input matrices A and B. Similarly, random matrices RA∈Fqs×1/m and RB∈Fqs×1/n are generated in A similar way as input matrices A and B and have the same size as the submatrices of input matrices A and B. To simplify the calculation, r/m and t/n are guaranteed to be integers. The parameters m and n represent the number of partition columns of the input matrix A and B, respectively. The parameter L represents the number of computational layers of the distributed secure computing scheme based on hierarchical coding.


## 4.2. Simulation Results of Distributed Decoding

Firstly, the completion time of the distributed security computing scheme based on layered coding is compared under different sizes of a computing task, that is, the change of the completion time of the distributed security computing scheme based on layered coding under differentm and n conditions. Two input matrices A and B and two random matrices RA and RB are generated, where s=3000, r=2400,t=2400,q=256.The number of computing layersL = 2, the number of worker nodes N = 13, and the number of dropped nodes μ=1 were designed. In addition, four subtask sizes, namely m=n=1,m=n=2,m=n=3, and m=n=4 were considered, and each experiment was repeated 10 times, as shown in Figure 3.Figure 3
Comparison of distributed decoding completion time under different tasks.As shown in Figure4, when m and N increase, that is, the master node allocates smaller computing tasks to the worker node, and the completion time of the master node gradually decreases. When m = 1 n = 1, the average completion time of the whole computing task is 129.35 s; while when m = 2 n = 2, the average completion time of the whole computing task is 36.69 s, which is 71.64% lower than that of m = 1 n = 1. Moreover, when m = 4, n = 4, compared with m = 2 n = 2, the average completion time of the overall task is reduced by 63.64%. It can be seen that reducing the size of subtasks can reduce the completion time of the overall computing task.Figure 4
Comparison of distributed decoding completion time under different computing layers.In addition, the completion time of distributed security computing schemes based on hierarchical coding is compared under different computing layers, that is, the change of completion time of distributed security computing schemes based on hierarchical coding under differentL conditions. Two input matrices A and B and two random matrices AR and BR are generated in this experiment, where s = 3000, r = 2400, t = 2400, and q = 256. The number of design worker nodes n = 13, and the number of missing nodes μ = 1; the completion time is counted every 1 from 1 to 4. In order to achieve better computing performance, different m and N are selected here, where m = n = 2, m = n = 3, and m = n = 4 are selected here, and three tests are carried out for each group of M and N under the same layer number to get the average value. The results are shown in Figure 4.Whenl > 1, compared with the polynomial code-based distributed security computing scheme, the layered coding-based distributed security computing scheme has less completion time. For example, when L = 1, m = 2, and n = 2, the completion time of the distributed security computing scheme based on polynomial code is 57.06 s, and when L = 2, the distributed security computing scheme based on hierarchical coding has a completion time of 57.06 s, while under the condition of n = 4, the optimal completion time is 13.34 s, so the completion time of the overall calculation task is reduced by 76.62%. When l < 2, with the increase of the number of computing layers, the completion time of the distributed security computing scheme based on layered coding is gradually increased. The main reason is that with the increase of L, the encoding time and decoding time of the scheme are also increasing. When the sum of coding time and decoding time exceeds the benefits of layered coding, the completion time of the whole computing task will increase. Therefore, in the distributed security computing scheme based on hierarchical coding, when the number of computing layers L is in a certain range, the completion time of the overall computing task will gradually decrease with the increase of the number of computing layers L.


## 4.3. System Testing

According to the dynamic security deposit system, a test attachment is established to upload the page, and the scenario of uploading two financial data on one page is simulated. Based on the client of the stress test, the script for accessing the attachment upload page is created, and the script for multiple users to synchronously call the script is launched by using the stress test tool. The response time and throughput of financial data upload are tested. The upload results are shown in Table1.Table 1
Test results of financial data upload.
SceneNumber of concurrent users90% response time/sFinancial data/numberData volume/mbDuration/minThroughput/mb/s1104.6781047535.54551.71122012.0921167602.20452.01133013.5311327667.72552.15145023.0751299650.78752.101510024.0761427715.032520.056Considering the number of financial data and response time, when the number of users is between 10 and 30, the number of financial data increases gradually. When the number of users is between 30 and 100, the number of financial data begins to decrease. When the number of users reaches 100 users, the number of financial data increases instantaneously due to the increase in the start and stop time. The response time was significantly increased when more than 30 users were used. Under the current architecture, the system can support 30 concurrent users to upload financial data at the same time, with a financial data upload rate of about 208.9/min, and the average throughput reaches 2 Mb/s. When the concurrent users are in 10 ∼100 periods, the number of failed events in this system to upload financial data is 0, and there is no abnormal phenomenon, which proves that the system can still ensure the stable operation of the financial data upload operation under high pressure.In order to verify the performance of the system, this paper designs a dynamic secure financial data storage system based on the IoT. The experiment analyzes the process of safe storage and management of financial data in a state-owned bank. The storage efficiency of the traditional financial data storage system is compared with that of this system. The results are shown in Figure5.Figure 5
Comparison of storage efficiency of different systems.The analysis of Figure5 shows that the depository system in this paper can improve the data storage efficiency. With the continuous improvement of data volume, its scheduling efficiency can be close to 100%. Compared with the traditional storage system, it better demonstrates the rationality and value of its design.The comparison of different storage space sizes between traditional storage systems and IoT-based storage systemsn is shown in Figure 6.Figure 6
Comparison of storage space of different systems.As can be seen from Figure6, the dynamic security storage system of financial data in the IoT environment has a larger space to store data, which can better store a large amount of data and ensure the security of data compared with the traditional storage system. The dynamic and secure financial big data storage system based on the IoT has high data storage speed and large storage space and can realize a large number of data storage and reading.


## 5. Conclusion

The design of financial big data dynamic security storage system based on the IoT greatly reduces the risk of the storage system, expands the storage space, and improves the storage speed. In the distributed security computing scheme based on hierarchical coding, when the number of computing layers is in a certain range, with the increase of computing layers, the completion time of the overall computing task will gradually decrease. In addition, from the designed experiment, it can be concluded that the system has good financial big data storage performance, and its efficiency is good, which provides strong support for China’s financial industry. From the perspective of network security, a secure distributed computing method can be envisaged, that is, on the premise that worker nodes are trusted, the weak data security from eavesdroppers can be protected by accessing the link between the master node and the worker node.



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*Source: 1043140-2022-09-25.xml*