A fast encryption method for enterprise financial data based on blockchain

Abstract

Aiming at the problems of poor encryption security and long encryption time in the existing enterprise financial data encryption, a fast encryption method of enterprise financial data based on blockchain is proposed. Firstly, the enterprise financial data is collected from the enterprise source database with the help of ETL Technology, and the collection is completed with the help of full extraction; Then, the interference data in the data is filtered with the help of Bloom filter, and the data index position is determined with the help of hash function mapping to complete the preprocessing. Finally, with the help of the data layer, network layer, consensus layer, motivation layer and application layer in the blockchain, the data and related encrypted data and timestamp are encrypted in each node, and the data encryption key is generated to complete the rapid encryption of enterprise financial data. The experimental results show that the security coefficient of enterprise financial data encrypted by the proposed method is high, and the encryption time is short.

Keywords

Blockchain ETL technology full extraction hash function mapping time stamp key generation

1. Introduction

With the continuous in-depth development of Internet technology, Internet technology has become an indispensable key in all walks of life. All sectors of society rely on the Internet for information communication, e-commerce and data transmission [9]. The emergence of Internet technology not only improves the work efficiency of all sectors of social fire, but also brings some threats [14]. Among them, the financial data of enterprises in operation and management is the key to the safe operation of enterprises. The security of enterprise financial data is the key to ensure the economic benefits of enterprises. The process of enterprise financial data input, processing and transmission is complex. Once the enterprise financial information is leaked or illegally stolen, the business secrets of the enterprise will be greatly threatened [11]. Therefore, the secure encryption of enterprise financial data is an important way of secure storage [15]. Therefore, relevant researchers in this field have done some research on enterprise financial data encryption and achieved some results.

Document [10] proposes a big data feature hiding encryption method based on information entropy suppression, which is used to encrypt enterprise financial data. Firstly, this method analyzes the accuracy of encryption in the existing encryption methods. Then, the information entropy suppression method is introduced to classify the enterprise financial data. Finally, the preprocessed data is encrypted by block cipher setting, and further encrypted according to different characteristic data. This method improves the accuracy of financial data encryption through the design of the method, but the method does not classify the data during encryption, resulting in poor encryption effect and needs to be further improved.Literature [13] proposed a homomorphic encryption method of character data under network active defense, and applied this method to financial data encryption. In this method, homomorphic encryption method is used to encrypt financial data of different character types. By analyzing the basic principle of homomorphism theory, the financial data in different character states are matched with the key, and then the data is further encrypted according to the matching results. This method effectively improves the security of financial data encryption. However, this method encrypts less data, which is easy to lead to incomplete encryption of some financial data. Literature [6] proposed a data security protection method for enterprise private cloud. This method starts with the problem of secure storage of financial data, carries out fine-grained segmentation before encryption of financial data, then scrambles the obtained data, then carries out mixed encryption of data with the help of cryptographic algorithm, and verifies the consistency of encrypted financial data to improve the effectiveness of financial data encryption. This method effectively improves the effectiveness of financial data encryption and enhances the security of data encryption, but the encryption speed is slow and has certain limitations, which needs to be further improved. Reference [12] proposed an SMI sensitive data encryption method based on DataSocket technology, and applied this method to financial data encryption. This method removes the redundancy of information by compressing the sequence monitoring image data, converts the information data into space, encrypts the information data to generate a new data key, and completes the data encryption. The encrypted information entropy of this method is large, the security is high, but the universality is not high.

In view of the shortcomings of the above methods, this paper designs a new fast encryption method of enterprise financial data based on blockchain. The specific technical route of this paper is as follows:

Step 1: collect enterprise financial data from the enterprise source database with the help of ETL Technology, and complete the collection of enterprise financial data with the help of full extraction;

Step 2: filter the interference data in the collected financial data with the help of Bloom filter, determine the index position of financial data with the help of hash function mapping, and complete the encrypted data preprocessing of enterprise financial data.

Step 3: with the help of the data layer, network layer, consensus layer, motivation layer and application layer in the blockchain, encrypt the data in each node by encapsulating the data and relevant encrypted data and timestamp, and generate the public key and key for data encryption. By strengthening the key, improve the security of rapid encryption of enterprise financial data and complete the rapid encryption of enterprise financial data.

2. Enterprise financial data encryption, data collection and preprocessing

2.1. Enterprise financial data encryption data collection

Enterprise financial data includes a large number of accounting vouchers, financial books and some related financial data, which are stored in the enterprise financial computer in a paperless form. However, due to the large quantity and scale of enterprise financial data, in order to realize the effective encryption of enterprise financial data. Firstly, this paper collects enterprise financial data, and uses ETL Technology to collect enterprise financial data in this paper [4]. This technology is extract, transform and load. This technology is extraction, transformation and loading. In the financial data extraction, the required enterprise financial data is obtained from the source system, and then the financial data is converted into the form required by the data source according to the encryption requirements, and then the collected financial data is loaded [8]. The process of collecting enterprise financial data with ETL Technology is shown in Fig. 1.

Fig. 1.

Enterprise financial data collection process.

In the process of enterprise financial data collection, relevant enterprise financial data are extracted from the source database. In this collection, the method of full data extraction is used to obtain enterprise financial data. Set the collected financial data set as: $\begin{matrix} (1) & A = {a_{1}, a_{2}, \dots a_{n}}, A \in R \end{matrix}$

In formula, A represents the financial data set collected, $a_{1}, a_{2}, \dots a_{n}$ represents the data composition factor in the set, and R represents the financial data source database.

According to the determined enterprise financial database, further full extraction is carried out, and the obtained enterprise financial data are: $\begin{matrix} (2) & A^{'} = β \sum_{a = 1}^{n} {a_{1}, a_{2}, \dots a_{n}} \end{matrix}$

In the formula, $A^{'}$ represents the final financial collection of enterprise data and β is the full extraction factor of financial data.

In the encrypted data collection of enterprise financial data, the enterprise financial data is collected from the enterprise source database with the help of ETL Technology, and the enterprise financial data collection is completed with the help of full extraction.

2.2. Enterprise financial data encryption data preprocessing

Based on the above collected enterprise financial data, the financial data is taken as the research object. There are many interference data and duplicate data in the above collected enterprise financial data. In order to reduce the complexity of subsequent financial data encryption, it is necessary to effectively preprocess the enterprise financial data. Firstly, the interference data in the collected financial data is filtered with the help of Bloom filter [7].

Assuming removal remove any collected financial data, the data is represented as $b (b \in B)$ ,set a bloom filter to: $\begin{matrix} (3) & {h_{i} (a)}_{i = 1}^{k} \end{matrix}$

In formula, k represents the index position of the Cologne filter and $h_{i} (a)$ represents the Cologne filter.

When the current position of the set bloom filter is 0, there is: $\begin{matrix} (4) & \exists a \in [1, k], b \in S \end{matrix}$

At this time, the filtered financial data is represented as: $\begin{matrix} (5) & h_{i} (a) = u h_{j} (b) \end{matrix}$

In formula, u represents the Bron filter filtering ratio.

The process of removing enterprise financial data by Bloom filter is shown in Fig. 2.

Fig. 2.

Process of removing enterprise financial data by Bloom filter.

In the above filtering process, due to the large amount of financial data processed in the bloom filtering process, some financial data are not deleted. Therefore, in order to further complete the complete preprocessing of financial data, it is necessary to further calculate the misjudgment rate in the financial data as the limiting condition of data preprocessing, and further filter the financial data according to the misjudgment conditions of data preprocessing [3].

Set the initial length of K, initialize the data and set the location of the data to 0. Mapping of the hash function is conducted according to the setting of the financial data location, the results of the mapping will be indexed, the determined data is deleted, and the preprocessing of the financial data is completed,that is: $\begin{matrix} (6) & p (h_{i} (a)) = Hp [h_{j} (b)] + 1 \end{matrix}$

In formula, H represents the hash function, p represents the initial length of the financial data, and $p (h_{i} (a))$ represents the mapping outcome parameters.

In the encrypted data preprocessing of enterprise financial data, the interference data in the collected financial data is filtered with the help of Bloom filter, and the index position of financial data is determined with the help of hash function mapping to complete the encrypted data preprocessing of enterprise financial data.

3. Fast encryption of enterprise financial data based on blockchain

After the above obtained enterprise financial data, in order to realize the rapid encryption of enterprise financial data, this paper introduces blockchain technology to encrypt enterprise financial data. In blockchain technology, transactions can be read publicly. Once data encryption is completed, it is difficult for anyone to change. Blockchain technology is an encryption technology of area centralized data composed of linked data and distributed consensus mechanism [2]. Blockchain technology types are shown in Fig. 3.

Figure 3 includes three types of blockchains, in which nodes of the public blockchain can join freely and participate in data reading and writing; In the alliance blockchain, all institutions and organizations combine for the same goal. This technology is widely used in enterprise data encryption [5].

When the blockchain encrypts data, it encrypts in each node through the data layer, network layer, consensus layer, motivation layer and application layer, encapsulates data and related encrypted data and timestamp. Each block is encrypted by each encryption block.

Fig. 3.

Block chain technology type.

According to the above analysis of blockchain technology, this paper uses blockchain technology to encrypt data security in enterprise financial data encryption.

First, the key generated in the blockchain will require the financial data to be encrypted to match the key.Set the security parameter of financial data to g and set two larger prime P and q, so: $\begin{matrix} (7) & p = q = | g | \end{matrix}$

At this time, formula (7) needs to meet: $\begin{matrix} (8) & y (p, q) = 1 \end{matrix}$

In the formula, $y (p, q)$ represents the generative element.

The generating element shall meet the following conditions, namely: $\begin{matrix} (9) & f = [d (g)] modn \end{matrix}$

In the formula, $d (g)$ represents a loss function, which is: $\begin{matrix} (10) & d (g) = \frac{f - 1}{vf} \end{matrix}$

In formula, v represents the key value generated by financial data and f represents the private key value of financial data.

Due to the real-time variability of enterprise financial data and a large amount of data. After encrypting the ciphertext, in order to further ensure the security of financial data, it is necessary to enhance the security of public key and private key values in encryption. Set the encrypted public key as: $\begin{matrix} (11) & z = p * q f_{pq} (modq) \end{matrix}$

In the formula, z represents the binomial of key generation and $modq$ represents the reversible generation process factor.

The result after generating the enhancement key is: $\begin{matrix} (12) & f * z = p * q f_{pq} (modq) j \end{matrix}$

In the formula, j represents the public key reinforcement factor.

Set the private key in encryption to: $\begin{matrix} (13) & s = p * q f_{pq} (mods) \end{matrix}$

In formula, $p * q$ represents private key and $mods$ represents private key pair.

Strengthen the security of the private key in encryption, that is: $\begin{matrix} f * s = p * q f_{pq} (mods) j \end{matrix}$

In formula, j represents the private key enhancement factor and $f * s$ represents the enhanced private key value.

The blockchain encryption processing is performed according to the above generated key and private key value. The ciphertext of the financial data of the encrypted key is expressed as: $\begin{matrix} (14) & c = f^{m} g^{n} modn \end{matrix}$

In formula, c represents the encrypted ciphertext results obtained after encryption, n represents the number of financial data encrypted to, and $f^{m} g^{n}$ represents the matching key in the encryption.

The fast encryption process of enterprise financial data based on blockchain technology is shown in Fig. 4.

Fig. 4.

Fast encryption process of enterprise financial data based on blockchain.

In the encryption process in Fig. 4, first set the encrypted public key and private key, determine whether the encryption key has been set successfully, output the encrypted key in the blockchain, apply it to the financial data to be encrypted, and finally output the encrypted data to complete the encryption process.

In the process of fast encryption of enterprise financial data, the basic principle of blockchain technology is analyzed. With the help of data layer, network layer, consensus layer, motivation layer and application layer in blockchain, the data and related encrypted data and timestamp are encrypted in each node, and the public key and key for data encryption are generated [1]. By strengthening the key, Improve the security of rapid encryption of enterprise financial data and complete the rapid encryption of enterprise financial data.

4. Experimental analysis

4.1. Experimental environment and parameter setting

According to the method set above, in order to highlight the effectiveness of the method, experimental analysis is carried out. In the experiment, taking a small and medium-sized listed enterprise in a certain place as the research object, the internal operation of the enterprise is stable, and the financial management and personnel management are relatively stable. In the experiment, the financial data of a manufacturing department of a calcium enterprise is selected as the research object, and these financial data are encrypted. In order to ensure the effectiveness of experimental encryption, the operating system is Windows XP system, and the encrypted results are verified for many times by SPSS13.0 software. The parameters required for the specific experiment are shown in Table 1.

Table 1
Relevant test parameters

Parameter Value

Sample data amount/GB 2

Number of keys/individual $[0, 10000]$

Attack traffic/GB 2

Encryption time interval/s 0.2

Number of iterations/times $[0, 100]$

Parameter	Value
Sample data amount/GB	2
Number of keys/individual	$[0, 10000]$
Attack traffic/GB	2
Encryption time interval/s	0.2
Number of iterations/times	$[0, 100]$

4.2. Experimental index design

Based on the experimental environment and experimental parameters set above, the proposed method, literature [13] method and literature [6] method are compared and analyzed. In the experiment, the security coefficient of sample financial data and the time-consuming of encryption are used for experimental analysis. Among them, the value range of security coefficient is $[0, 1]$ . The higher the security coefficient, the better the security of encryption; The shorter the encryption time, the faster the encryption speed.

4.3. Analysis of experimental results

4.3.1. Analysis of security coefficient of different encryption methods

The security coefficient after data encryption reflects the effectiveness of the encryption method. In the experiment, by encrypting the sample financial data through the methods in this paper, literature [13] and literature [6], the security coefficients after encryption by the three methods are analyzed. The results are shown in Fig. 5.

Fig. 5.

Security coefficient results of different encryption methods.

By analyzing the experimental results in Fig. 5, it can be seen that there are some differences in the security coefficient after encrypting the sample financial data by using the methods in this paper, literature [13] and literature [6]. Among them, the safety coefficient of the sample financial data by the method in this paper is always higher than 0.9, the highest safety coefficient of the sample financial data by the method in literature [13] is about 0.87, and the highest safety coefficient of the sample financial data by the method in literature [6] is about 0.8; In contrast, the encryption security coefficient of this method is higher. This is because this method enhances the public key and private key after the blockchain technology encryption, which improves the security of data encryption and the scientific effectiveness of this method.

4.3.2. Time consuming analysis of different encryption methods

Based on the above analysis, the experiment further analyzes the encryption of sample financial data by the methods of this paper, literature [13] and literature [6]. The encryption time-consuming results are shown in Table 2.

Table 2
Time consuming analysis of different encryption methods (s)

Number of iterations/times Document [13] methods Document [6] methods The method of this paper

10 1.2 1.4 0.8

20 1.3 1.6 0.7

30 1.3 1.8 0.7

40 1.4 2.0 0.8

50 1.5 2.1 0.9

60 1.6 2.2 0.6

70 1.8 2.3 0.7

80 2.0 2.2 0.8

90 2.2 2.4 0.7

100 2.4 2.3 0.7

Number of iterations/times	Document [13] methods	Document [6] methods	The method of this paper
10	1.2	1.4	0.8
20	1.3	1.6	0.7
30	1.3	1.8	0.7
40	1.4	2.0	0.8
50	1.5	2.1	0.9
60	1.6	2.2	0.6
70	1.8	2.3	0.7
80	2.0	2.2	0.8
90	2.2	2.4	0.7
100	2.4	2.3	0.7

By analyzing the experimental data in Table 2, it can be seen that with the change of iteration times, the time-consuming of encrypting the sample financial data by using the methods in this paper, literature [13] and literature [6] is different. When the number of iterations is 30, the time-consuming of encrypting the sample financial data by using the methods in this paper, literature [13] and literature [6] is about 0.7 s, 1.3 s and 1.8 s respectively; When the number of iterations is 60, the time-consuming of encrypting the sample financial data by using the methods in this paper, literature [13] and literature [6] is about 0.6 s, 1.6 s and 2.2 s respectively; When the number of iterations is 100, the time-consuming of encrypting the sample financial data by using the methods in this paper, literature [13] and literature [6] is about 0.7 s, 2.4 s and 2.3 s respectively; In contrast, this method takes less time. This is because this method preprocesses the enterprise financial data before encryption, which reduces the time-consuming of interference data removal, and then improves the effectiveness of this method.

5. Conclusion

In order to improve the security of enterprise financial data, this paper designs a new fast encryption method of enterprise financial data based on blockchain. Through the collection of enterprise financial data, take this as the starting point of the research, and effectively reduce the noise of the collected data. With the help of the data layer, network layer, consensus layer, motivation layer and application layer in the blockchain, the public key and key for data encryption are generated by encapsulating the data and relevant encrypted data and timestamp, so as to complete the rapid encryption of enterprise financial data. Through experimental analysis, it is verified that this method has the following advantages:

Using this method to encrypt the sample data, the security coefficient is higher than 0.9, which verifies the security of the encrypted data;

The encryption of sample data using this method takes a short time, which verifies the efficiency of this method.

Footnotes

Acknowledgements

The study was supported by “The second batch of high-level professional groups of provincial higher vocational colleges:Financial Services and Management, Guangdong,China (Grant No. GSPZYQ2021074)”.

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A fast encryption method for enterprise financial data based on blockchain

Abstract

Keywords

1. Introduction

2. Enterprise financial data encryption, data collection and preprocessing

2.1. Enterprise financial data encryption data collection

4.1. Experimental environment and parameter setting

Table 1 Relevant test parameters Parameter Value Sample data amount/GB 2 Number of keys/individual [ 0 , 10000 ] Attack traffic/GB 2 Encryption time interval/s 0.2 Number of iterations/times [ 0 , 100 ]

4.3. Analysis of experimental results

4.3.1. Analysis of security coefficient of different encryption methods

Footnotes

Acknowledgements

References

Table 1
Relevant test parameters

Parameter Value

Sample data amount/GB 2

Number of keys/individual $[0, 10000]$

Attack traffic/GB 2

Encryption time interval/s 0.2

Number of iterations/times $[0, 100]$