Financial data security uploading method for enterprise finance

Abstract

The invention relates to the technical field of data encryption, in particular to a financial data security uploading method for enterprise finance. According to the method, in the financial data iteration clustering process, through the influence of newly-added backup financial data on clustering offset and the historical offset condition of a center point, the center influence index of the newly-added backup financial data in the distribution step and the center updating index of the newly-added backup financial data in the updating step are obtained; and obtaining a center offset scale through the iterative center influence index and the center update index to perform next iterative clustering until the obtained sensitive cluster is matched with the encryption algorithm, and performing hybrid encryption on the to-be-encrypted data through the sensitive cluster and the matched encryption algorithm to obtain encrypted data. According to the method, when financial data is divided according to risk conditions, a supervision mechanism is added to adjust the offset degree of an iterative clustering center, the preference of a division clustering result is improved, the category matching reliability of a multi-encryption algorithm is higher, and the subsequent data encryption security is better.

Description

Technical Field

[0001] The present invention relates to the technical field of data encryption, and in particular to a method for securely uploading financial data for corporate finance. Background Art

[0002] With the development of the information society, various information has an increasingly greater impact on all walks of life. In the process of information development, different industries will inevitably generate a large amount of industry data. For example, the most common financial industry usually uses the financial data related to the enterprise to upload and save in a unified manner to conduct data mining, and then formulate a path and strategy that is more in line with the development of the enterprise. However, due to the sensitivity of financial data, when uploading financial data related to the company's finances, in order to ensure that the data is not stolen and the company's assets are lost, it is inevitable to pay attention to the security of the data, so as to ensure that the upload of the company's financial data is efficient while taking into account privacy.

[0003] When uploading financial data of existing corporate finance, financial data is generally encrypted using a data encryption algorithm, and then communicated through protocol authentication to ensure data upload. However, the volume of general financial data is large, and in order to ensure secure upload, it needs to be encrypted as a whole. The overall multiple encryption will make the calculation relatively complex, the transmission efficiency is low, and the unified encryption strategy is strict in the custody of the key. When the key is not properly kept, it is easy to cause data leakage and poor encryption effect. Summary of the invention

[0004] In order to solve the technical problem of poor encryption effect in the prior art, the purpose of the present invention is to provide a method for securely uploading financial data for corporate finance. The technical solution adopted is as follows:

[0005] The present invention provides a method for securely uploading financial data for corporate finance, the method comprising:

[0006] Obtain the attack risk value of each backup financial data in the enterprise financial database and the performance parameters of different encryption algorithms;

[0007] K-means iterative clustering is performed based on the number of encryption algorithms and the attack risk value of the backup financial data. In the current iteration, the newly added backup financial data in each cluster is used as the allocated financial data of each cluster; the central influence index of each allocated financial data is obtained according to the deviation degree of the attack risk value of the allocated financial data in the cluster; the central update index of each allocated financial data is obtained according to the continuous deviation of the center point of each cluster in the current iteration in the previous iteration and the importance of each allocated financial data of each cluster;

[0008] Based on the deviation of the center point of each cluster in the current iteration in the previous iteration, the center influence index and center update index of all the allocated financial data in each cluster are adjusted to obtain the center deviation scale of each cluster in the current iteration; based on the center deviation scale of each cluster in the current iteration, the cluster of the next iteration is obtained; until the iteration stops, the sensitive cluster is obtained;

[0009] According to the distribution of attack risk values ​​in sensitive clusters and the ranking and matching of performance parameters of different encryption algorithms, the matching encryption algorithm of each sensitive cluster is obtained; according to the matching between the data to be encrypted and the sensitive cluster, the corresponding matching encryption algorithm is used for encryption to obtain encrypted data.

[0010] Furthermore, the method for obtaining the center influence index includes:

[0011] For any cluster in the current iteration process, calculate the difference between the attack risk value of each backup financial data in the cluster and the central point value as the risk deviation of each backup financial data; take the sum of all risk deviations in the cluster as the distribution deviation index of the cluster;

[0012] The ratio of the risk deviation to the distribution deviation index of each type of allocated financial data in the cluster is normalized to obtain the central influence index of each type of allocated financial data.

[0013] Furthermore, the method for obtaining the center update index includes:

[0014] For any cluster, the update influence of each type of allocated financial data in the cluster is obtained according to the deviation between the influence of each type of allocated financial data on the update of the center point and the historical continuous deviation.

[0015] The occurrence frequency of each type of allocated financial data in the cluster in the database is used as the important influence degree of each type of allocated financial data;

[0016] The central update index of each allocated financial data is obtained by combining the important influence and update influence of each allocated financial data in the cluster.

[0017] Furthermore, the method for obtaining the update impact includes:

[0018] Arrange the center point values ​​of the cluster in time series to obtain the center offset sequence of the cluster; calculate the mean of the difference between each two adjacent center point values ​​in the center offset sequence of the cluster to obtain the total offset update degree of the cluster;

[0019] The ratio of the attack risk value of each type of allocated financial data in the cluster to the total number of types of backup financial data in the cluster is used as the offset influence of each type of allocated financial data in the cluster;

[0020] The ratio of the offset influence degree of each type of allocated financial data in the cluster to the total offset update degree is used as the update influence degree of each type of allocated financial data.

[0021] Furthermore, the method for obtaining the center offset scale includes:

[0022] The difference between the center point values ​​of each cluster between the current iteration and the previous iteration is used as the center migration degree of each cluster; in the current iteration, the control coefficient of each cluster is obtained according to the size of the center migration degree of each cluster;

[0023] Combine the central influence index and central update index of all the distributed financial data in each cluster to obtain the adjustment degree of each cluster;

[0024] The product of the regulation degree and the regulation coefficient of each cluster is used as the migration regulation weight of each cluster; the product of the central migration degree of each cluster and the migration regulation weight is used as the central deviation scale of each cluster.

[0025] Furthermore, the control coefficient of each cluster is obtained according to the size of the center migration degree of each cluster, including:

[0026] For any cluster, when the center migration degree of the cluster is greater than the preset amplitude threshold, the center migration degree is negatively correlated and normalized, and the value is used as the control coefficient of the cluster;

[0027] When the center migration degree of the cluster is less than or equal to the preset amplitude threshold, the regulation coefficient of the cluster is set to 1.

[0028] Furthermore, the step of combining the central influence index and the central update index of all the distributed financial data in each cluster to obtain the adjustment degree of each cluster includes:

[0029] For any cluster, the product of the central influence index and the central update index of each type of allocated financial data in the cluster is negatively correlated to obtain the influence coefficient of each type of allocated financial data;

[0030] The mean of the influence coefficients of all the allocated financial data in the cluster is taken as the adjustment degree of the cluster.

[0031] Furthermore, the step of obtaining the clustering clusters of the next iteration based on the center offset scale of each clustering cluster in the current iteration includes:

[0032] For any cluster, the difference between the center point value of the cluster in the current iteration and the previous iteration is used as the iterative update value of the cluster;

[0033] When the iteration update value is a positive number, the sum of the center point value and the center offset scale of the cluster in the previous iteration is used as the updated center point of the cluster in the current iteration to perform the next iteration clustering, and obtain the cluster corresponding to the next iteration;

[0034] When the iterative update value is negative, the difference between the center point value of each cluster in the previous iteration and the center offset scale is used as the updated center point of the cluster in the current iteration to perform the next iterative clustering and obtain the cluster corresponding to the next iteration.

[0035] Furthermore, the method for obtaining the matching encryption algorithm includes:

[0036] Calculate the mean of the attack risk values ​​of all the backup financial data in each sensitive cluster as the sensitivity of each sensitive cluster; arrange the sensitive clusters in descending order of sensitivity to obtain a sensitive cluster sequence;

[0037] Arrange all encryption algorithms in descending order according to performance parameters to obtain an algorithm sequence;

[0038] For any sensitive cluster, the encryption algorithm with the same sequence number as the sensitive cluster in the algorithm sequence is used as the matching encryption algorithm of the sensitive cluster.

[0039] Furthermore, the method of encrypting the data to be encrypted using a corresponding matching encryption algorithm based on the matching between the data to be encrypted and the sensitive clusters to obtain the encrypted data includes:

[0040] According to the sensitive clusters to which each type of financial data in the data to be encrypted belongs, the matching encryption algorithm for each type of financial data is obtained, and the different financial data in the data to be encrypted are encrypted using the corresponding matching algorithms to obtain the ciphertext and the key as the encrypted data.

[0041] The present invention has the following beneficial effects:

[0042] The present invention considers reducing the local optimum during the iterative clustering of financial data, monitors the newly added backup financial data in each iteration, obtains the center influence index of the newly added backup financial data in the allocation step through the influence of the newly added backup financial data on the cluster offset and the historical offset of the center point, reflects the degree of influence on the allocation cluster cluster, and obtains the center update index of the newly added backup financial data in the update step, reflects the influence of the cluster cluster update offset. The center offset scale is obtained through the influence of the two steps in the iteration to supervise and adjust the next iterative clustering, avoid the possibility of local optimum, and perform more reasonable classification of the backup financial data. Then, when matching with the encryption algorithm, a better matching encryption algorithm can be obtained according to the risk sensitivity, and when the encrypted data is encrypted by multiple encryption algorithms, more secure encrypted data is obtained. When the historical financial data is classified according to the risk situation, the present invention adds a supervision mechanism to adjust the offset of the iterative cluster center, improves the selectivity of the clustering results, makes the category matching reliability of the multiple encryption algorithms higher, and the subsequent data encryption security is better. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention 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 invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 A flow chart of a method for securely uploading financial data for corporate finance provided by one embodiment of the present invention;

[0045] Figure 2 A flow chart of a method for obtaining a center update index provided by an embodiment of the present invention;

[0046] Figure 3 A flow chart of a method for obtaining a center offset scale provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a financial data secure upload method for corporate finance proposed by the present invention 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.

[0048] 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 invention belongs.

[0049] The following is a detailed description of a specific solution of a method for securely uploading financial data for corporate finance provided by the present invention in conjunction with the accompanying drawings.

[0050] See also Figure 1 , which shows a flow chart of a method for securely uploading financial data for corporate finance provided by an embodiment of the present invention, the method comprising the following steps:

[0051] S1: Obtain the attack risk value of each backup financial data in the enterprise financial database and the performance parameters of different encryption algorithms.

[0052] Because there are different types of corporate financial data, and different types of financial data have different sensitivities and corresponding security levels, in order to ensure data transmission efficiency, different encryption algorithms can be used to process financial data through a mixed strategy encryption method, so that there are multiple keys for the data to be uploaded, and it is impossible to decrypt the financial data to be uploaded with a single key. Even if some keys are leaked, all data cannot be leaked, thus ensuring the security of the uploaded financial data.

[0053] In order to more reliably match encryption algorithms for different types of financial data, this embodiment classifies different types of financial data according to risk conditions, and performs better strategy adaptation based on different types of risks and the security performance of encryption algorithms. Therefore, it is necessary to obtain the risk conditions of each type of backup financial data in the enterprise financial database, as well as the security performance of different encryption algorithms.

[0054] In the embodiment of the present invention, in order to facilitate the encryption analysis of different financial data, the historical data of the financial database of the enterprise finance within a period of time is accessed and backed up to obtain the backup financial data for subsequent analysis. The backup financial data in the enterprise financial database is called, and all the attacked data are acquired to form an attacked database. The frequency of occurrence of each backup financial data in the attacked database is recorded as the attack risk value of each backup financial data. The corresponding attack risk value of the backup financial data that does not appear in the attacked database is set to 0. The collection and acquisition of enterprise data in the embodiment of the present invention are all authorized, and the process does not violate relevant laws and regulations, and does not violate public order and good customs.

[0055] When analyzing multiple encryption algorithms, first select multiple encryption algorithms, and evaluate the encryption effect of different encryption algorithms on financial data. In an embodiment of the present invention, N encryption algorithms are selected, and any financial data of 1GB in size is selected as test data. The test data is encrypted using N encryption algorithms to obtain the ciphertext of each encryption algorithm. The security parameters of each encryption algorithm are obtained by attacking the ciphertext, which are used as the performance parameters of each encryption algorithm. The larger the performance parameter, the better the anti-attack ability of the encryption algorithm. It should be noted that the number of types of encryption algorithms can be adjusted by the implementer according to the specific implementation scenario, and no restriction is made here. At the same time, the attack test of the encryption algorithm is a technical means well known to those skilled in the art, such as ciphertext-only attack or plaintext attack, etc., which will not be elaborated here.

[0056] At this point, the data preparation is completed.

[0057] S2: K-means iterative clustering is performed based on the number of encryption algorithms and the attack risk value of the backup financial data. In the current iteration, the newly added backup financial data in each cluster is used as the allocated financial data of each cluster; according to the deviation degree of the attack risk value of the allocated financial data in the cluster, the central influence index of each allocated financial data is obtained; according to the continuous offset of the center point of each cluster in the current iteration in the previous iteration and the importance of each allocated financial data of each cluster, the central update index of each allocated financial data is obtained.

[0058] When classifying and dividing the backup financial data into sensitive risk categories, since there are N types of encryption algorithms, it is also necessary to divide the backup financial data into N categories based on the attack risk value, and adapt different encryption algorithms to each category, so that subsequent financial data can correspond to different encryption algorithms by category.

[0059] The embodiment of the present invention selects the K-means clustering algorithm to cluster the backup financial data. In the clustering process, N initial center points need to be selected for clustering according to the attack risk value, and the K-means clustering algorithm is essentially an iterative algorithm, which optimizes the clustering clusters by allocating data and updating the center points iteratively. When the selection of the initial center is not accurate enough, inaccurate clusters will be generated or converge to a local optimal solution, which may make some clusters too sparse or overlapping, affecting the final clustering quality. Therefore, the data allocation and center point update are supervised in each iteration to adjust the update.

[0060] First, in the data allocation step of the clustering cluster in the current iteration process, the impact of each newly added backup financial data analysis on the change of the clustering cluster center point is analyzed. The newly added backup financial data in each clustering cluster is first used as the allocated financial data of each clustering cluster. The overall deviation of each financial data in the clustering cluster is used to reflect the possible impact on the subsequent update of the clustering cluster center point selection.

[0061] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the center influence index includes:

[0062] First, for any cluster in the current iteration process, the difference between the attack risk value of each backup financial data in the cluster and the center point value is calculated as the risk deviation of each backup financial data, where the center point value is the mean of all attack risk values ​​in the cluster. The deviation reflects the discreteness of the value distribution in the cluster.

[0063] Furthermore, the sum of all risk deviations in the cluster is used as the distribution deviation index of the cluster. The deviation reflects the concentration of the attack risk value in the cluster. The smaller the distribution deviation index is, the better the concentration of the cluster is.

[0064] Finally, the ratio of the risk deviation to the distribution deviation index of each type of allocated financial data in the cluster is normalized to obtain the central influence index of each type of allocated financial data. For each type of allocated financial data, the greater the risk deviation, the more discrete the performance of the allocated financial data in the cluster. Therefore, the greater the central influence index, the greater the impact of the allocated financial data on the update of the cluster. As an example, the expression of the central influence index is:

[0065] Where M i,n A is the central influence index of the i-th distribution of financial data in the n-th cluster. i Denotes the attack risk value of the ith type of distributed financial data; B n Represented as the center point value of the nth cluster; U n It is represented by the total number of types of backup financial data in the nth cluster, A u It is represented as the attack risk value of the u-th backup financial data in the n-th cluster; |A u -B n | represents the risk deviation of the u-th backup financial data in the n-th cluster; |A i -B n | represents the risk deviation of the i-th distribution of financial data in the n-th cluster; It is represented as the distribution deviation index of the nth cluster; Norm() is represented as the normalization function. It should be noted that normalization is a technical means well known to those skilled in the art. The normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0066] Secondly, in the step of updating the center point of the cluster in the current iteration process, the influence of each newly allocated financial data on the center shift is analyzed to reflect the importance of the impact of the allocated financial data when the center point is updated and adjusted. Therefore, by combining the iterative center point change shift with the importance of the allocated financial data itself, the possible shift impact of each currently allocated financial data is quantified.

[0067] Preferably, in the embodiment of the present invention, the method for obtaining the center update indicator can be found in Figure 2 , which shows a flow chart of a method for obtaining a center update indicator provided by an embodiment of the present invention, the method comprising the following steps:

[0068] S211: For any cluster, the update influence of each type of allocated financial data in the cluster is obtained according to the deviation between the influence of each type of allocated financial data in the cluster on the update of the center point and the historical continuous deviation.

[0069] First, when the center point is updated, the update impact brought by each type of allocated financial data is reflected according to the degree to which each type of allocated financial data participates in the center point deviation after allocation, combined with the difference changes in the center point update in historical iterations.

[0070] Preferably, in an embodiment of the present invention, the method for obtaining the update influence includes:

[0071] Arrange the center point values ​​of the cluster in time series to obtain the center offset sequence of the cluster, calculate the difference between each two adjacent center point values ​​in the center offset sequence of the cluster, and find the average to obtain the total offset update degree of the cluster, reflecting the average size of the center point offset change in historical iterations. It should be noted that even for the first iteration, the center offset sequence also contains the initial center point and the center point value of the current iteration, so there is no situation where the difference cannot be calculated.

[0072] The ratio of the attack risk value of each type of allocated financial data in the cluster to the total number of types of backup financial data in the cluster is further used as the offset influence of each type of allocated financial data in the cluster. Since the center point is the mean of all attack risk values, the attack risk value of each type of allocated financial data is evenly divided among all types of the cluster, which reflects the proportion of influence of a single type on the update of the center point.

[0073] Finally, the ratio of the offset influence of each type of allocated financial data in the cluster to the total offset update is used as the update influence of each type of allocated financial data. By comparing the offset influence of a single type with the historical overall update offset, the impact of the change in the center offset of the current iteration is reflected. The closer the offset influence is to the total offset update, that is, the greater the update influence, the greater the degree of center offset caused by the allocated financial data in the current iteration.

[0074] S212: The occurrence frequency of each type of allocated financial data in the cluster in the database is used as the important influence degree of each type of allocated financial data.

[0075] The frequency of occurrence of each type of allocated financial data in the database can reflect the proportion of this type of financial data in the original database. The greater the proportion of allocated financial data in the database, the more important the data performance is, and it is more frequent in actual application. It is easy to be cracked by regularity when it is not considered properly. The proportion of its influence on the change of clustering clusters should also be high, so as to improve clustering impact analysis and avoid falling into the local optimal situation of clustering division only through numerical analysis.

[0076] S213: Combining the importance influence and update influence of each type of allocated financial data in the cluster, obtain a central update index for each type of allocated financial data.

[0077] In the embodiment of the present invention, the product of the importance influence and the update influence of each type of allocated financial data is normalized to obtain the central update index of each type of allocated financial data. The greater the importance influence and the greater the update influence, the higher the influence of the corresponding allocated financial data on the central point update, and therefore the larger the central update index needs to be. As an example, the expression of the central update index is:

[0078] R n,i =Norm(P i ×T n,i );where R n,i It is represented as the central update index of the i-th distribution financial data in the n-th cluster, P i It represents the important influence of the i-th type of financial data in the n-th cluster, T n,i It represents the update influence of the i-th distributed financial data in the n-th cluster, and Norm() represents the normalization function.

[0079] At this point, the supervision analysis of the two steps in each iteration process is completed, and the impact indicators of each type of allocated financial data are obtained.

[0080] S3: Based on the degree of center point offset of each cluster in the current iteration, the center influence index and center update index of all allocated financial data in each cluster are adjusted to obtain the center offset scale of each cluster in the current iteration; based on the center offset scale of each cluster in the current iteration, the cluster of the next iteration is obtained; until the iteration stops, the sensitive cluster is obtained.

[0081] In the K-means iterative clustering process, the center points of all data in the clusters are regained after cluster assignment for the next iterative cluster assignment. However, considering that the re-acquisition of the center points is prone to local optimality or extreme discreteness of the clusters, the scale is adjusted based on the allocation data and update impact of each iteration.

[0082] Preferably, in the embodiment of the present invention, the method for obtaining the center offset scale can be found in Figure 3 , which shows a flow chart of a method for obtaining a center offset scale provided by an embodiment of the present invention, the method comprising the following steps:

[0083] S301: The difference in the center point value of each cluster between the current iteration and the previous iteration is used as the center migration degree of each cluster; in the current iteration, the control coefficient of each cluster is obtained according to the size of the center migration degree of each cluster.

[0084] The difference in the center point value between the current iteration and the previous iteration is used to reflect the offset of the original center point, and then the scale is adjusted according to the offset. By allocating influencing indicators of financial data, it is avoided that the discreteness of some data leads to a small offset of the iterative center, resulting in poor clustering convergence or falling into a local optimal solution.

[0085] Preferably, in an embodiment of the present invention, a method for obtaining a control coefficient includes:

[0086] For any cluster, when the center migration degree of the cluster is greater than the preset amplitude threshold, it means that the deviation is high and the control strength needs to be reduced. The center migration degree is negatively correlated and normalized to the value used as the control coefficient of the cluster. The greater the deviation, the smaller the degree of control required. It should be noted that negative correlation mapping is a technical means well known to technicians in this field, and can be in the form of inverse proportional values ​​or negative exponential power, etc., and is not limited here.

[0087] When the center migration degree of the cluster is less than or equal to the preset amplitude threshold, it means that the deviation is small and the control coefficient needs to be larger, then the control coefficient of the cluster is set to 1. In a specific implementation of the embodiment of the present invention, the preset amplitude threshold is set to 0.53, and the specific value can be adjusted by the implementer, and is not limited here.

[0088] S302: Combining the central influence index and the central update index of all the distributed financial data in each cluster, to obtain the adjustment degree of each cluster.

[0089] The influence of the allocated financial data is then combined to further restrict the adjustment scale of the clustering clusters. The greater the influence of the allocated financial data on the clustering clusters after allocation, the more changes the clustering clusters have, and the smaller the adjustment force required. Therefore, in the embodiment of the present invention, the method for obtaining the adjustment negative includes:

[0090] For any cluster, the product of the central influence index and the central update index of each type of allocated financial data in the cluster is negatively correlated to obtain the influence coefficient of each type of allocated financial data. The greater the influence, the smaller the adjustment force should be.

[0091] Further combined with the impact analysis results of all the allocated financial data, the mean of the impact coefficients of all the allocated financial data in the cluster is used as the adjustment degree of the cluster.

[0092] S303: The product of the adjustment degree and the control coefficient of each cluster is used as the migration adjustment weight of each cluster; the product of the central migration degree of each cluster and the migration adjustment weight is used as the offset adjustment degree of each cluster; the sum of the central migration degree and the offset adjustment degree of each cluster is calculated to obtain the central offset scale of each cluster.

[0093] By analyzing the degree of cluster center offset and the impact of distributed financial data, the adjustment weight of each cluster offset is comprehensively obtained, and the migration adjustment weight is obtained by combining them in the form of product.

[0094] Furthermore, on the basis of improving the degree of offset adjustment, the higher the migration adjustment weight, the greater the adjustment strength. The larger the offset adjustment degree is obtained by multiplying the center migration degree and the migration adjustment weight. Then, the center offset scale after supervision adjustment is obtained by the sum of the center migration degree and the offset adjustment degree, avoiding the local optimization situation with a smaller center point migration.

[0095] At this point, the supervision and adjustment process for each iteration is completed, and the adaptive update strength of the cluster center point of the current iteration is obtained.

[0096] Therefore, preferably, in an embodiment of the present invention, obtaining the clustering clusters of the next iteration based on the center offset scale of each clustering cluster in the current iteration includes:

[0097] For any cluster, the difference between the center point values ​​of the cluster in the current iteration and the previous iteration is used as the iterative update value of the cluster, and the sign of the iterative update value reflects the update direction of the center point after the original iteration.

[0098] When the iterative update value is positive, it means that the update direction of the center point value is positive. The sum of the center point value of the cluster cluster in the previous iteration and the center offset scale is used as the updated center point of the cluster cluster in the current iteration for the next iterative clustering. The cluster cluster corresponding to the next iteration is obtained, and the center point of the cluster cluster is updated through the adjusted offset scale.

[0099] When the iterative update value is negative, it means that the update direction of the center point value is negative. The difference between the center point value of each cluster in the previous iteration and the center offset scale is used as the updated center point of the cluster in the current iteration to perform the next iterative clustering and obtain the cluster corresponding to the next iteration.

[0100] By continuously supervising and adjusting the scale of the center point of the cluster cluster, the iterative cluster division is made more accurate until the iteration stops, and the final cluster cluster result is a sensitive cluster cluster. It should be noted that the iterative stopping condition of K-means iterative clustering is cluster convergence or reaching the number of iterations. The embodiment of the present invention can set the maximum number of iterations to 12 times. The implementer can adjust the condition for iterative stopping according to the specific implementation situation, and there is no limitation here.

[0101] S4: According to the distribution of attack risk values ​​in the sensitive clusters and the ranking and matching of performance parameters of different encryption algorithms, the matching encryption algorithm of each sensitive cluster is obtained; according to the matching between the data to be encrypted and the sensitive clusters, the corresponding matching encryption algorithm is used for encryption to obtain encrypted data.

[0102] After obtaining the sensitive clusters, a corresponding matching encryption algorithm may be selected for each sensitive cluster. In this embodiment, the matching is performed by ranking and matching the degree of attack risk and the performance of the encryption algorithm.

[0103] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the matching encryption algorithm includes:

[0104] First, the mean of the attack risk values ​​of all the backup financial data in each sensitive cluster is calculated as the sensitivity of each sensitive cluster, reflecting the possibility of the sensitive cluster as a whole being attacked. The greater the sensitivity, the higher the risk of attack.

[0105] Arrange the sensitive clusters in descending order of sensitivity to obtain a sensitive cluster sequence, and arrange the sensitive clusters in descending order of attack risk. Arrange all encryption algorithms in descending order of performance parameters to obtain an algorithm sequence, and arrange the encryption algorithms in descending order of encryption security.

[0106] Therefore, for any sensitive cluster, the encryption algorithm with the same serial number as the sensitive cluster in the algorithm sequence is used as the matching encryption algorithm of the sensitive cluster. The two sequences are matched by the same serial number. The smaller the serial number value, the higher the risk of attack of the sensitive cluster, and the higher the encryption security of the matching encryption algorithm. Since the clustering process is based on the number of encryption algorithms as the number of clusters, each sensitive cluster corresponds to an encryption algorithm with the same serial number.

[0107] By backing up the financial data, the correspondence between the encryption algorithms of different financial data is completed. As a matching encryption strategy, the corresponding matching encryption algorithm can be used to encrypt the data to be encrypted and the sensitive clusters directly to obtain the encrypted data. In an embodiment of the present invention, the matching encryption algorithm of each financial data in the data to be encrypted is obtained according to the sensitive clusters in which each financial data in the data to be encrypted is located, and the corresponding matching algorithm is used to encrypt the different financial data in the data to be encrypted to obtain the ciphertext and the key as the encrypted data.

[0108] The matching encryption strategy is used to reduce the computational complexity of the overall encryption, improve efficiency while ensuring the security of the encrypted data, and increase the difficulty of data cracking. In an embodiment of the present invention, the ciphertext can be converted into the same format and securely uploaded to the data storage terminal, and can be subsequently decrypted based on the key and the matching encryption strategy.

[0109] In summary, the present invention considers reducing the local optimum during the iterative clustering of financial data, supervises the newly added backup financial data in each iteration, obtains the center influence index of the newly added backup financial data in the allocation step through the influence of the newly added backup financial data on the cluster offset and the historical offset of the center point, reflects the degree of influence on the allocation cluster cluster, and obtains the center update index of the newly added backup financial data in the update step, reflects the influence of the cluster cluster update offset. The center offset scale is obtained through the influence of the two steps in the iteration to supervise and adjust the next iteration clustering, avoid the possibility of local optimum, and perform more reasonable classification of the backup financial data. Then, when matching with the encryption algorithm, a better matching encryption algorithm can be obtained according to the risk sensitivity, and when the encrypted data is encrypted by multiple encryption algorithms, more secure encrypted data is obtained. When the historical financial data is classified according to the risk situation, the present invention adds a supervision mechanism to adjust the offset of the iterative cluster center, improves the selectivity of the clustering results, makes the category matching reliability of the multiple encryption algorithms higher, and makes the subsequent data encryption more secure.

[0110] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. 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.

[0111] 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.

Claims

1. A method for securely uploading financial data for corporate finance, characterized in that: The method comprises: Obtain the attack risk value of each backup financial data in the enterprise financial database and the performance parameters of different encryption algorithms; K-means iterative clustering is performed based on the number of encryption algorithms and the attack risk value of the backup financial data. In the current iteration, the newly added backup financial data in each cluster is used as the allocated financial data of each cluster. According to the deviation degree of the attack risk value of the allocated financial data in the cluster, the central influence index of each allocated financial data is obtained. According to the continuous deviation of the center point of each cluster in the current iteration in the previous iteration and the importance of each allocated financial data of each cluster, the central update index of each allocated financial data is obtained. Based on the deviation of the center point of each cluster in the current iteration in the previous iteration, the center influence index and center update index of all the allocated financial data in each cluster are adjusted to obtain the center deviation scale of each cluster in the current iteration; based on the center deviation scale of each cluster in the current iteration, the cluster of the next iteration is obtained; until the iteration stops, the sensitive cluster is obtained; According to the distribution of attack risk values ​​in sensitive clusters and the ranking and matching of performance parameters of different encryption algorithms, the matching encryption algorithm of each sensitive cluster is obtained; according to the matching between the data to be encrypted and the sensitive cluster, the corresponding matching encryption algorithm is used for encryption to obtain encrypted data.

2. A method for securely uploading financial data for corporate finance according to claim 1, characterized in that: The method for obtaining the center impact indicator includes: For any cluster in the current iteration process, calculate the difference between the attack risk value of each backup financial data in the cluster and the central point value as the risk deviation of each backup financial data; take the sum of all risk deviations in the cluster as the distribution deviation index of the cluster; The ratio of the risk deviation to the distribution deviation index of each type of allocated financial data in the cluster is normalized to obtain the central influence index of each type of allocated financial data.

3. A method for securely uploading financial data for corporate finance according to claim 1, characterized in that: The method for obtaining the center update index includes: For any cluster, the update influence of each type of allocated financial data in the cluster is obtained according to the deviation between the influence of each type of allocated financial data on the update of the center point and the historical continuous deviation. The occurrence frequency of each type of allocated financial data in the cluster in the database is used as the important influence degree of each type of allocated financial data; The central update index of each allocated financial data is obtained by combining the important influence and update influence of each allocated financial data in the cluster.

4. A method for securely uploading financial data for corporate finance according to claim 3, characterized in that: The method for obtaining the update impact includes: Arrange the center point values ​​of the cluster in time series to obtain the center offset sequence of the cluster; calculate the mean of the difference between each two adjacent center point values ​​in the center offset sequence of the cluster to obtain the total offset update degree of the cluster; The ratio of the attack risk value of each type of allocated financial data in the cluster to the total number of types of backup financial data in the cluster is used as the offset influence of each type of allocated financial data in the cluster; The ratio of the offset influence degree of each type of allocated financial data in the cluster to the total offset update degree is used as the update influence degree of each type of allocated financial data.

5. A method for securely uploading financial data for corporate finance according to claim 1, characterized in that: The method for obtaining the center offset scale includes: The difference between the center point values ​​of each cluster between the current iteration and the previous iteration is used as the center migration degree of each cluster; in the current iteration, the control coefficient of each cluster is obtained according to the size of the center migration degree of each cluster; Combine the central influence index and central update index of all the distributed financial data in each cluster to obtain the adjustment degree of each cluster; The product of the regulation degree and the regulation coefficient of each cluster is used as the migration regulation weight of each cluster; the product of the central migration degree of each cluster and the migration regulation weight is used as the central deviation scale of each cluster.

6. A method for securely uploading financial data for corporate finance according to claim 5, characterized in that: The step of obtaining the control coefficient of each cluster according to the size of the center migration degree of each cluster includes: For any cluster, when the center migration degree of the cluster is greater than the preset amplitude threshold, the center migration degree is negatively correlated and normalized, and the value is used as the control coefficient of the cluster; When the center migration degree of the cluster is less than or equal to the preset amplitude threshold, the regulation coefficient of the cluster is set to 1.

7. A method for securely uploading financial data for corporate finance according to claim 5, characterized in that: The step of combining the central influence index and the central update index of all the distributed financial data in each cluster to obtain the adjustment degree of each cluster includes: For any cluster, the product of the central influence index and the central update index of each type of allocated financial data in the cluster is negatively correlated to obtain the influence coefficient of each type of allocated financial data; The mean of the influence coefficients of all the allocated financial data in the cluster is taken as the adjustment degree of the cluster.

8. A method for securely uploading financial data for corporate finance according to claim 1, characterized in that: The step of obtaining the clustering clusters for the next iteration based on the center offset scale of each clustering cluster in the current iteration includes: For any cluster, the difference between the center point value of the cluster in the current iteration and the previous iteration is used as the iterative update value of the cluster; When the iteration update value is a positive number, the sum of the center point value and the center offset scale of the cluster in the previous iteration is used as the updated center point of the cluster in the current iteration to perform the next iteration clustering, and obtain the cluster corresponding to the next iteration; When the iterative update value is negative, the difference between the center point value of each cluster in the previous iteration and the center offset scale is used as the updated center point of the cluster in the current iteration to perform the next iterative clustering and obtain the cluster corresponding to the next iteration.

9. A method for securely uploading financial data for corporate finance according to claim 1, characterized in that: The method for obtaining the matching encryption algorithm includes: Calculate the mean of the attack risk values ​​of all the backup financial data in each sensitive cluster as the sensitivity of each sensitive cluster; arrange the sensitive clusters in descending order of sensitivity to obtain a sensitive cluster sequence; Arrange all encryption algorithms in descending order according to performance parameters to obtain an algorithm sequence; For any sensitive cluster, the encryption algorithm with the same sequence number as the sensitive cluster in the algorithm sequence is used as the matching encryption algorithm of the sensitive cluster.

10. A method for securely uploading financial data for corporate finance according to claim 1, characterized in that: The method of encrypting the data to be encrypted using a corresponding matching encryption algorithm according to the matching between the data to be encrypted and the sensitive clusters to obtain the encrypted data includes: According to the sensitive clusters to which each type of financial data in the data to be encrypted belongs, the matching encryption algorithm for each type of financial data is obtained, and the different financial data in the data to be encrypted are encrypted using the corresponding matching algorithms to obtain the ciphertext and the key as the encrypted data.

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