Electronic signature whole process data storage method based on block chain

Abstract

The invention relates to the technical field of electronic signature evidence storage, in particular to an electronic signature whole process data evidence storage method based on a block chain, and the method comprises the steps: collecting all historical transaction behavior data, including transaction users, transaction time and text data of signed files, of each user before each transaction in the block chain; determining the theme difference degree of the signed files of any two transactions; determining a transaction behavior abnormal coefficient of each user during each transaction; acquiring the malicious user confidence of each user during each transaction; and for each transaction, based on the number of transactions between each user and each to-be-signed user, in combination with the malicious user confidence of each to-be-signed user, obtaining a selectable coefficient of each to-be-signed user during each transaction of each user, screening the to-be-signed users, and performing electronic signature by using an encrypted signature algorithm. According to the application, the anonymity of the identity of the signer is ensured, and the privacy security of both transaction parties is improved.

Description

Technical Field

[0001] The present application relates to the technical field of electronic signature evidence storage, and specifically to a method for electronic signature full-process data evidence storage based on blockchain. Background Art

[0002] With the popularity of e-commerce and digital transactions, electronic contracts have become an indispensable part of modern business activities, and electronic signatures are a necessary element for the effectiveness of electronic contracts. However, traditional electronic contract signing and evidence storage methods have the risk of data being easily tampered with and lost, and cannot effectively guarantee the security and credibility of signed data.

[0003] At present, with the continuous development of information technology, blockchain technology, as a decentralized, tamper-proof distributed ledger technology, has gradually become an ideal choice for solving the security and credibility issues of electronic signatures due to its transparency, security and immutability. However, in practical applications, since the evidence data on the blockchain is open and transparent, the blockchain participants maintain a common ledger, and each participant can view and verify the evidence data of other participants, which increases the risk of user privacy data. Although the traditional ring signature algorithm provides a high degree of anonymity, the anonymity of the ring signature depends on the size and diversity of the members in the ring. If the information of the members in the ring is linked, the anonymity of the user identity will decrease, which will cause the privacy of both parties to the transaction to be leaked. Summary of the invention

[0004] In order to solve the above technical problems, this application provides a blockchain-based full-process data storage method for electronic signatures to solve existing problems.

[0005] The present application's blockchain-based electronic signature full-process data storage method adopts the following technical solution: An embodiment of the present application provides a blockchain-based method for storing data in the entire process of electronic signatures, the method comprising the following steps: Collect all historical transaction behavior data of each user in the blockchain before each transaction, including the transaction user, transaction time, and text data of the signed document; Analyze the similarity of the text data of any two transactions in the historical transaction behavior data, as well as the time interval between the two transactions, to determine the subject difference of the signed documents of the two transactions; Determine the abnormal coefficient of transaction behavior of each user at each transaction by combining the degree of repetition of transaction users in the historical transaction behavior data with the subject difference between any two transactions; Based on the historical transaction behavior data of each user, a transaction graph of each user at each transaction is constructed, and the distribution disorder degree and distribution range of the transaction times between transaction users in the transaction graph are analyzed. Combined with the transaction behavior abnormality coefficient, the malicious user confidence of each user at each transaction is determined; Based on the malicious user confidence, malicious users in the transaction graph of each user are identified and eliminated; the remaining users in the transaction graph of each user after the malicious users are eliminated are recorded as users to be signed; For each transaction, based on the number of transactions between each user and its users to be signed, combined with the malicious user confidence of each user to be signed, the selectability coefficient of each user to be signed for each transaction is obtained, and the users to be signed are screened. For each transaction of each user, the screened users to be signed are electronically signed using an encrypted signature algorithm.

[0006] In one embodiment, determining the topic difference degree includes: Use word segmentation tools to obtain keywords and their word frequencies in the text data of the signed documents, use word frequencies as weights, and use Simhash algorithm based on keywords in each signed document to obtain Simhash signatures of each signed document; The metric distance of the Simhash signatures of the signed files during the two arbitrary transactions is calculated, and the subject difference is positively correlated with the metric distance and negatively correlated with the time interval.

[0007] In one embodiment, the topic difference is the ratio of the metric distance to the time interval.

[0008] In one embodiment, the process of determining the abnormal coefficient of the transaction behavior is as follows: For each user's historical transaction behavior data at each transaction, calculate the average of the subject difference between any two transactions, divide all transaction users in the historical transaction behavior data into two parts according to the transaction order, and calculate the similarity coefficient between the two parts; The transaction behavior abnormality coefficient is positively correlated with the mean, and negatively correlated with the similarity coefficient.

[0009] In one embodiment, the nodes in the transaction graph are users, and the lines between the nodes are normalized values ​​of the number of transactions between the corresponding users, which are recorded as transaction weights.

[0010] In one embodiment, the determination of the malicious user confidence level includes: The transaction weights of each user's transaction graph and all other transaction users are combined into a transaction set, and the product of the degree of disorder of all data in the transaction set and the average level is calculated; The sum of the product and a preset value greater than 0 is calculated, and the malicious user confidence is the ratio of the transaction behavior abnormality coefficient to the sum.

[0011] In one embodiment, the identifying and removing malicious users from the transaction graph of each user based on the malicious user confidence level includes: Among the transaction users in the transaction graph of each user, transaction users whose malicious user confidence is greater than a preset threshold are determined as malicious users and are removed from the transaction graph of each user.

[0012] In one embodiment, the determination of the selectable coefficient includes: The Kruskal algorithm is used to generate a minimum spanning tree of the transaction graph of each user at each transaction according to the minimum weight principle. Based on the transaction weight between each user at each transaction and the user to be signed in the minimum spanning tree, the transaction separation degree between each user at each transaction and the user to be signed in the minimum spanning tree is calculated; The cumulative sum of the malicious user confidence of the user to be signed in the minimum spanning tree and a preset constant greater than 0 is calculated, and the selectable coefficient of each user to be signed is the ratio of the transaction separation degree to the cumulative sum.

[0013] In one embodiment, the transaction separation degree is the cumulative sum of the reciprocals of all transaction weights between each user and the users to be signed in the minimum spanning tree at each transaction.

[0014] In one embodiment, the step of screening the users to be signed includes: Arrange the selectable coefficients of all the users to be signed in descending order during each transaction of each user, and take the first preset number of users to be signed as the screened users to be signed.

[0015] This application has at least the following beneficial effects: This application collects all historical transaction behavior data before each transaction of each user in the blockchain, including transaction users, transaction time and text data of signed documents; analyzes the similarity of the text data of any two transactions in the historical transaction behavior data, and the time interval between the any two transactions, to determine the subject difference of the signed documents of the any two transactions; the subject difference reflects the difference degree of the text data of the signed documents in the historical transaction behavior data, thereby reflecting the possibility that the user is engaging in abnormal transactions; determines the transaction behavior anomaly coefficient of each user at each transaction through the degree of repetition of transaction users in the historical transaction behavior data, combined with the subject difference of all any two transactions; the transaction behavior anomaly coefficient reflects the stability of transaction users in the user's historical transaction behavior data, reflects the credibility of the user's transaction behavior, and improves the accuracy of malicious user identification; constructs a transaction map of each user at each transaction based on the historical transaction behavior data of each user, analyzes the distribution disorder of the number of transactions between transaction users in the transaction map, and the distribution range, and combines The transaction behavior abnormality coefficient determines the malicious user confidence of each user in each transaction; the malicious user confidence reflects the possibility that the user is a malicious user, that is, an accurate assessment of the user's frequent abnormal transactions is conducive to screening and obtaining safe and reliable users; based on the malicious user confidence, malicious users in the transaction map of each user are identified and eliminated; the remaining users in the transaction map of each user after the malicious users are eliminated are recorded as users to be signed; for each transaction, based on the number of transactions between each user and its users to be signed, combined with the malicious user confidence of each user to be signed, the selectable coefficient of each user to be signed in each transaction of each user is obtained, the selectable coefficient takes into account the association relationship between the user to be signed and the signer, evaluates the possibility of the user to be signed becoming a member of the signer's ring, and improves the security of the signer's electronic signature; and the users to be signed are screened, and the screened users to be signed in each transaction of each user are electronically signed using an encrypted signature algorithm, so as to better ensure the anonymity of the signer's identity, thereby protecting the privacy and security of both parties to the transaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 A flowchart of the steps of a blockchain-based electronic signature full-process data storage method provided for this application; Figure 2 This is a diagram of the minimum spanning tree of signer A; Figure 3 A flow chart is defined for the selectable coefficients. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by this application to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a blockchain-based electronic signature full-process data storage method proposed in this application in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0019] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0020] The following is a detailed description of the specific scheme of the blockchain-based full-process data storage method for electronic signatures provided by this application in conjunction with the accompanying drawings.

[0021] An embodiment of the present application provides a method for storing data of the entire process of electronic signature based on blockchain. Specifically, the following method for storing data of the entire process of electronic signature based on blockchain is provided. Figure 1 , the method comprises the following steps: Step S001, collect all historical transaction behavior data of each user in the blockchain before each transaction, including the transaction user, transaction time and text data of the signed document, and perform pre-processing.

[0022] The electronic signature data stored on the blockchain needs to consider the user's identity privacy. At present, due to the excessive openness and transparency of the blockchain, the user's identity privacy is maliciously obtained, which brings a poor experience to users. Therefore, this application aims to improve the electronic signature data storage method based on the blockchain.

[0023] First, taking the nth transaction of each user in the blockchain as an example, the n-1 historical transaction behavior data before the nth transaction behavior of each user in the blockchain are collected, wherein the historical transaction behavior data include the transaction user, transaction time and text data of the signed document. Secondly, for the convenience of analysis, the transaction user is processed using label encoding. In addition, in order to prevent incomplete information and data duplication in the collected data, the historical transaction behavior data of all users in the acquired blockchain are cleaned up, and the incomplete information and repeated data are deleted. In this embodiment, the text data of the signed document is segmented using the Harbin Institute of Technology LTP tool, and the keywords and word frequencies of the text data of the signed document are obtained based on the TF-IDF algorithm. Among them, label encoding, Harbin Institute of Technology LTP tool and TF-IDF algorithm are all well-known technologies, and this embodiment will not be described in detail here.

[0024] Step S002, analyzing the similarity of the text data of any two transactions in the historical transaction behavior data and the time interval between the any two transactions, and determining the subject difference of the signed documents of the any two transactions.

[0025] One of the core characteristics of blockchain is transparency and traceability. All transactions on the blockchain must be open to all nodes, and any node can verify the validity of the data. However, this characteristic of blockchain also brings a problem, that is, excessive openness and transparency may lead to the leakage of users' private information. With the rapid popularization and promotion of blockchain applications, the risk of privacy leakage is becoming more and more serious. Therefore, this embodiment improves the insufficient privacy protection of the ring signature algorithm, and constructs the selectable coefficient of the user to be signed by analyzing the transaction behavior of users on the blockchain and the correlation between users, so as to improve the data storage of the whole process of electronic signature based on blockchain.

[0026] First, considering that not all users on the blockchain are safe and reliable, and there are many malicious users on the blockchain, this embodiment records any user who needs to sign as signer A. When signer A uses the ring signature algorithm When there are malicious users in the ring members selected during the second signature, the security of the transaction may be threatened by forging signatures and other malicious attacks, so it is necessary to remove malicious users from the blockchain. Since the business activities of secure and trusted users in the blockchain are often continuous and stable, that is, each user has a main business area, and the main business area of ​​the user will not change significantly in a short period of time, the content of the electronic signature signed by the secure and trusted user in a short period of time is relatively stable, there will be no significant differences, and the possibility of signing a large number of documents in a short period of time is low. Malicious users usually conduct transactions with users by quickly generating a large number of forged electronic signatures and signing the content of the documents, which makes the content similarity between the two consecutive signed documents of the malicious user low.

[0027] Based on the above analysis, this embodiment takes user B in the blockchain as an example for analysis. First, the keywords and word frequencies of the signed documents of each transaction in the historical transaction behavior data of user B are obtained. The keywords of the signed documents at each transaction are used as input, and the word frequencies corresponding to the keywords are used as weights. The Simhash algorithm is used to obtain the Simhash signature of the signed documents of each transaction. The Simhash algorithm is an existing well-known technology, and the specific process is not repeated. The subject difference degree of the signed documents of any two transactions in the historical transaction behavior data of user B is calculated. The specific calculation method is: ; In the formula, is the subject difference between the signed documents of the i-th and j-th transactions in the historical transaction behavior data of user B when conducting the n-th transaction, is the measured distance between the Simhash signatures of the signed documents of the i-th and j-th transactions in the historical transaction behavior data of user B when he made the n-th transaction, is the time interval between the i-th and j-th transactions in the historical transaction behavior data of user B when performing the n-th transaction.

[0028] It should be noted that the calculation method of the metric distance described in this embodiment adopts the Hamming distance. The calculation of the Hamming distance is a well-known technology. The implementer can choose other existing feasible metric distance calculation methods at will, and this embodiment does not limit it here.

[0029] It should be understood that the smaller the measured distance is, the higher the similarity between the two signed documents is, and the smaller the difference is, that is, the smaller the subject difference is. If the time interval between the two signed documents of user B is closer and the measured distance is larger, it means that the subject content of the documents signed by user B is more different, and the possibility that user B is a malicious user is greater.

[0030] Step S003, determining the transaction behavior abnormality coefficient of each user in each transaction through the degree of repetition of transaction users in the historical transaction behavior data and combining the subject difference between any two transactions.

[0031] Secondly, in the blockchain, the transaction users of safe and reliable users are relatively stable, and long-term transactions are carried out between users, while malicious users usually frequently change transaction users to avoid risks. Therefore, this embodiment further divides the transaction users in the historical transaction behavior data of user B when conducting the nth transaction into two parts equally according to the transaction order, wherein, when the number of transaction users in the historical transaction behavior data is an odd number, the transaction users generated by the transaction behavior in the middle do not participate in the division. This embodiment uses the Jaccard algorithm to calculate the similarity coefficient between the two parts of transaction users divided in the historical transaction behavior data of user B when conducting the nth transaction, and records it as the transaction user consistency coefficient of user B. Among them, the larger the transaction user consistency coefficient, the more stable the transaction user of user B is, and the more likely user B is a safe and reliable user. Jaccard is an existing well-known technology, and the specific process is not repeated. The implementer can choose other existing feasible similarity coefficient calculation methods according to actual conditions, and this embodiment is not limited here.

[0032] Based on the above analysis, the abnormal coefficient of transaction behavior of each user is calculated for each transaction. The specific calculation method is: ; In the formula, is the abnormal coefficient of the user's transaction behavior during the nth transaction, is the subject difference between the signed documents of the i-th and j-th transactions in the historical transaction behavior data of user B when conducting the n-th transaction, N is the number of transactions in the historical transaction behavior data of user B when conducting the n-th transaction, is the similarity coefficient between the two parts of transaction users in the historical transaction behavior data of user B when he made the nth transaction, To preset a value greater than 0 to avoid the denominator being 0, in this embodiment The implementer can set it according to the actual situation, and this embodiment does not limit it here.

[0033] It should be understood that when user B in the blockchain is The historical transaction behavior data at the time of the transaction The signed document and When the contents of the two signed documents differ greatly, the calculated The signed document and The larger the value of the Hamming distance between the signed documents, the larger the The signed document and The closer the signing time between the two signed documents, the smaller the calculated time interval value; thus, the calculated The signed document and The greater the value of the subject difference between the two signed documents, the greater the difference between the two signed documents. The greater the difference in the subject content of the signed document in the historical transaction behavior data at the time of the transaction. Secondly, due to the frequent replacement of transaction users by malicious users, the calculated transaction user consistency coefficient is small; furthermore, if user B in the blockchain The larger the value of the transaction behavior abnormality coefficient in the transaction, the higher the risk of user B's transaction pattern, and the more likely he is to be a potential malicious user.

[0034] Step S004, based on the historical transaction behavior data of each user, construct a transaction map for each user at each transaction, analyze the distribution disorder and distribution range of the transaction times between transaction users in the transaction map, and determine the malicious user confidence of each user at each transaction in combination with the transaction behavior abnormality coefficient.

[0035] In the blockchain, the transaction patterns between users are complicated, and there may be direct or potential associations between any two users. In this environment, the selection of ring members becomes crucial when the ring signature algorithm generates an electronic signature. When the selected ring members are highly associated with the signer, the attacker is likely to infer the true identity of the signer through cluster analysis and other techniques, thereby destroying the anonymity of the signature. Therefore, this embodiment needs to analyze the associations between users on the blockchain.

[0036] Specifically, taking user B as an example, a transaction graph is constructed based on the transaction users in the historical transaction behavior data of user B in the blockchain. The purpose of constructing the transaction graph is to better present the potential transaction relationship between users in the blockchain, and then analyze the degree of association between user B and signer A in the blockchain. Each user is regarded as a node in the transaction graph, and the nodes corresponding to the two users with transaction behaviors are connected. The number of historical transactions between the two connected users is normalized and used as the connection weight between the two nodes, which is recorded as the transaction weight. That is, the larger the transaction weight between the two users, the more transactions between the two users, and the closer the association between the two users. In this embodiment, the Sigmoid function is used to normalize the number of transactions, and the implementer can choose other existing feasible normalization methods.

[0037] Secondly, when the transaction behavior anomaly coefficient of user B in the blockchain is larger, it means that the possibility that user B is a malicious user is greater, and the probability that user B conducts abnormal transactions with other users is higher. In addition, since malicious users often do not choose to conduct multiple transactions with the same user, the number of transactions between user B and other users in the transaction graph is relatively close, that is, the transaction weights between the transaction nodes corresponding to user B and other users in the transaction graph are usually small, and the difference is small. Therefore, in this embodiment, the transaction weights of user B and all other transaction users in the transaction graph of user B are recorded as the transaction set. Based on the data distribution in the transaction set and the transaction behavior anomaly coefficient, the malicious user confidence of user B in the nth transaction is calculated. The specific calculation method is: ; In the formula, is the malicious user confidence of user B during the nth transaction, is the abnormal coefficient of the user's transaction behavior during the nth transaction, is the average value of all data in the transaction set of user B during the nth transaction. is the degree of confusion of all data in the transaction set of user B during the nth transaction, To preset a value greater than 0 to avoid the denominator being 0, in this embodiment The implementer can set it according to the actual situation, and this embodiment does not limit it here.

[0038] It should be noted that the calculation method of the degree of disorder described in this embodiment adopts information entropy, and the implementer can choose other existing feasible calculation methods, such as coefficient of variation, standard deviation, etc.

[0039] It should be understood that when there is a high possibility that user B has abnormal transactions in the blockchain, the calculated The larger the value of the abnormal coefficient of transaction behavior at the time of the first transaction, and when user B conducts abnormal transactions with multiple users in the blockchain, the transaction weights between user B and other user nodes in the constructed transaction graph are usually smaller and basically equal in size, which makes the average value and information entropy of the transaction set between user B and other user nodes in the transaction graph composed of user B's historical transaction behavior data smaller, that is, the smaller the difference between the number of transactions between user B and other users, and the smaller the number of transactions, the higher the value of the abnormal coefficient of transaction behavior at the time of the first transaction. The larger the malicious user confidence value is during the transaction, the greater the possibility that user B is a malicious user.

[0040] Step S005, based on the malicious user confidence, malicious users in the transaction graph of each user are identified and eliminated; the remaining users in the transaction graph of each user after the malicious users are eliminated are recorded as users to be signed.

[0041] The malicious user confidence of each user in the blockchain can be obtained by using the same calculation method as the malicious user confidence of user B in the nth transaction. For signer A, the malicious user confidence of each transaction user in the transaction graph of signer A in the nth transaction is normalized, and the transaction users whose normalized malicious user confidence is greater than the preset threshold are determined as malicious users and removed from the transaction graph of signer A in the nth transaction, so as to obtain a transaction graph of signer A consisting of safe and reliable users.

[0042] In this embodiment, the Sigmoid function is used to normalize the malicious user confidence. The implementer can choose other existing feasible normalization methods. In this embodiment, the preset threshold is set to 0.8, and the implementer can set it according to the actual situation.

[0043] The transaction graph after removing malicious users during the nth transaction of signer A is recorded as transaction graph L, and the remaining users in transaction graph L except signer A are recorded as users to be signed.

[0044] Step S006, for each transaction, based on the number of transactions between each user and its users to be signed, combined with the malicious user confidence of each user to be signed, obtain the selectable coefficient of each user to be signed for each transaction, and screen the users to be signed. For each transaction of each user, the screened users to be signed are electronically signed using an encrypted signature algorithm.

[0045] This embodiment uses Kruskal algorithm to generate transaction graph The minimum spanning tree is generated according to the principle of minimum transaction weight. The minimum spanning tree diagram of signer A is as follows: Figure 2 As shown, Figure 2 middle represents the user in the blockchain, A represents the signer A, Represents the transaction weight between users. The Kruskal algorithm is a well-known technology and will not be described in detail in this embodiment. First, based on the minimum spanning tree, the transaction separation between the signer A and the user to be signed in the minimum spanning tree is calculated at the time of the nth transaction. The specific calculation method is: ; In the formula, is the transaction separation degree between the signer A at the time of the nth transaction and the user F to be signed in the minimum spanning tree, is the kth transaction weight between the signer A and the user F to be signed in the minimum spanning tree during the nth transaction, and K is the number of transaction weights between the signer A and the user F to be signed in the minimum spanning tree during the nth transaction.

[0046] Furthermore, the selectable coefficients of each user to be signed during the nth transaction of signer A are calculated as follows: ; In the formula, is the selectable coefficient of the user F to be signed when the signer A makes the nth transaction, is the transaction separation degree between the signer A at the time of the nth transaction and the user F to be signed in the minimum spanning tree, is the malicious user confidence of the user F to be signed during the nth transaction of signer A, To preset a constant greater than 0 to avoid the denominator being 0, in this embodiment , the implementer can set it according to the actual situation, and this embodiment does not limit it. The optional coefficient determination flow chart is as follows Figure 3 shown.

[0047] It should be understood that when user F in the blockchain is a safe and trusted user, the calculated malicious user confidence value of user F is smaller; in addition, the smaller the number of direct and indirect transactions between user F and signer A, the calculated direct or indirect transaction weight between user F and signer A is. The smaller the value of , the larger the value of the transaction separation between user F and signer A calculated; the larger the value of the selectability coefficient of the user F to be signed that is finally calculated; it indicates that the higher the security level of the user F to be signed becoming a member of the ring member of signer A. The selectability coefficient takes into account the trustworthiness of the user to be signed and the transaction separation between the user to be signed and signer A. According to the selectability coefficient, users in the ring member of signer A can be selected more safely.

[0048] Finally, considering that too many members in the ring will greatly increase the computational cost of the ring signature generation and verification process, and too few ring members will reduce the anonymity of the electronic signature, therefore, in this embodiment, the selectable coefficients of all users to be signed during the n-th transaction of signer A are arranged in descending order, and the first preset number of users to be signed form the ring members of signer A, and the ring signature algorithm is used to generate the electronic signature of signer A. In this embodiment, the preset value is recorded as g. In this embodiment, ,in, is the number of all users to be signed during the nth transaction of signer A, is a rounding function, and g is the number of members in the ring when the signer A uses the ring signature algorithm to generate an electronic signature. The ring signature algorithm is a well-known technology and will not be described in detail in this embodiment.

[0049] The generated electronic signature of signer A is stored on the blockchain to complete the data storage of the electronic signature.

[0050] By adopting the same operation steps as those used by signer A to generate an electronic signature during the nth transaction, the electronic signature generated by each user in the blockchain for each transaction can be obtained.

[0051] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0052] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0053] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Modifications to the technical solutions recorded in the aforementioned embodiments, or equivalent replacement of some of the technical features therein, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A blockchain-based method for storing data in the entire process of electronic signatures, characterized in that: The method comprises the following steps: Collect all historical transaction behavior data of each user in the blockchain before each transaction, including the transaction user, transaction time, and text data of the signed document; Analyze the similarity of the text data of any two transactions in the historical transaction behavior data, as well as the time interval between the two transactions, to determine the subject difference of the signed documents of the two transactions; Determine the abnormal coefficient of transaction behavior of each user at each transaction by combining the degree of repetition of transaction users in the historical transaction behavior data with the subject difference between any two transactions; Based on the historical transaction behavior data of each user, a transaction graph of each user at each transaction is constructed, and the distribution disorder degree and distribution range of the transaction times between transaction users in the transaction graph are analyzed. Combined with the transaction behavior abnormality coefficient, the malicious user confidence of each user at each transaction is determined; Based on the malicious user confidence, malicious users in the transaction graph of each user are identified and eliminated; the remaining users in the transaction graph of each user after the malicious users are eliminated are recorded as users to be signed; For each transaction, based on the number of transactions between each user and its users to be signed, combined with the malicious user confidence of each user to be signed, the selectability coefficient of each user to be signed for each transaction is obtained, and the users to be signed are screened. For each transaction of each user, the screened users to be signed are electronically signed using an encrypted signature algorithm.

2. The method for storing data of the entire process of electronic signature based on blockchain as claimed in claim 1 is characterized in that: The determination of the subject difference degree includes: Use word segmentation tools to obtain keywords and their word frequencies in the text data of the signed documents, use word frequencies as weights, and use Simhash algorithm based on keywords in each signed document to obtain Simhash signatures of each signed document; The metric distance of the Simhash signatures of the signed files during the two arbitrary transactions is calculated, and the subject difference is positively correlated with the metric distance and negatively correlated with the time interval.

3. A blockchain-based electronic signature full-process data storage method as claimed in claim 2, characterized in that: The topic difference is the ratio of the metric distance to the time interval.

4. The method for storing data of the entire process of electronic signature based on blockchain as claimed in claim 1 is characterized in that: The process of determining the abnormal coefficient of the transaction behavior is as follows: For each user's historical transaction behavior data at each transaction, calculate the average of the subject difference between any two transactions, divide all transaction users in the historical transaction behavior data into two parts according to the transaction order, and calculate the similarity coefficient between the two parts; The transaction behavior abnormality coefficient is positively correlated with the mean, and negatively correlated with the similarity coefficient.

5. The method for storing data of the entire process of electronic signature based on blockchain as claimed in claim 1 is characterized in that: The nodes in the transaction graph are users, and the lines between nodes are normalized values ​​of the number of transactions between the corresponding users, which are recorded as transaction weights.

6. A blockchain-based electronic signature full-process data storage method as claimed in claim 5, characterized in that: The determination of the malicious user confidence level includes: The transaction weights of each user's transaction graph and all other transaction users are combined into a transaction set, and the product of the degree of disorder of all data in the transaction set and the average level is calculated; The sum of the product and a preset value greater than 0 is calculated, and the malicious user confidence is the ratio of the transaction behavior abnormality coefficient to the sum.

7. The method for storing data of the entire process of electronic signature based on blockchain as claimed in claim 1 is characterized in that: The identifying and removing malicious users from the transaction graph of each user based on the malicious user confidence level includes: Among the transaction users in the transaction graph of each user, transaction users whose malicious user confidence is greater than a preset threshold are determined as malicious users and are removed from the transaction graph of each user.

8. The method for storing data of the entire process of electronic signature based on blockchain as claimed in claim 5 is characterized in that: The determination of the selectable coefficient includes: The Kruskal algorithm is used to generate a minimum spanning tree of the transaction graph of each user at each transaction according to the minimum weight principle. Based on the transaction weight between each user at each transaction and the user to be signed in the minimum spanning tree, the transaction separation degree between each user at each transaction and the user to be signed in the minimum spanning tree is calculated; The cumulative sum of the malicious user confidence of the user to be signed in the minimum spanning tree and a preset constant greater than 0 is calculated, and the selectable coefficient of each user to be signed is the ratio of the transaction separation degree to the cumulative sum.

9. A blockchain-based electronic signature full-process data storage method as claimed in claim 8, characterized in that: The transaction separation degree is the cumulative sum of the reciprocals of all transaction weights between each user and the users to be signed in the minimum spanning tree at each transaction.

10. The method for storing data of the entire process of electronic signature based on blockchain as claimed in claim 1, characterized in that: The user to be signed is screened, including: Arrange the selectable coefficients of all the users to be signed in descending order during each transaction of each user, and take the first preset number of users to be signed as the screened users to be signed.

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