Classification and grading method for traceability sensitive data

Abstract

The invention provides a classification and grading method for traceability sensitive data, and relates to the field of data classification and grading. According to the method, first-level classification and second-level classification are sequentially carried out on all traceability sensitive data; constructing grading indexes; constructing a data classification model based on grading indexes except for the enterprise business association degree and the weight thereof so as to perform initial sensitivity grading on the traceability sensitive data under each first-level subclass; and based on the enterprise business association degree and the weight thereof, optimizing the initial sensitivity grading result, and obtaining a final classification grading result of each piece of traceability sensitive data. On the basis of a traditional scheme, in combination with traceability service characteristics, the service association degree between enterprises is used as an index of sensitive data grading, and a new thought is provided for enterprise traceability sensitive data classification and grading. Furthermore, a block chain is used as a bottom layer storage tool to store the traceability sensitive data, so that the credibility during data sharing is ensured, and the situation that the traceability process is not credible due to malicious tampering of the data is prevented.

Description

Technical Field

[0001] The present invention relates to the field of data classification and grading, and in particular to a classification and grading method, system, storage medium and electronic device for traceability sensitive data. Background Art

[0002] The advent of the big data era has made data an important asset for enterprises. When enterprises share data with others, they must determine which data can be shared and which data cannot be leaked. Therefore, in order to ensure the data security of enterprises and regulate the management of enterprise privacy data, the classification and grading of sensitive data has become a research hotspot.

[0003] In related technologies, traditional classification and grading schemes generally classify data by the business characteristics of the industry first, and then grade the importance of the data to the country, society, and enterprises. For example, the paper (Zhang Xiaoyi, Dai Yicong. Water conservancy data classification and security protection technology [J]. People's Yangtze River, 2023, 54 (S2): 232-237. DOI: 10.16232 / j.cnki.1001-4179.2023.S2.053) proposes a data security protection system that adapts to the characteristics of water conservancy business.

[0004] However, the classification concept of this method is vague and not very business-specific. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the deficiencies in the prior art, the present invention provides a classification and grading method, system, storage medium and electronic device for traceability sensitive data, which solves the technical problems that traditional classification and grading schemes have vague concepts and lack of business specificity.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] A classification and grading method for traceability sensitive data, comprising:

[0010] All traceability sensitive data are classified into primary and secondary categories in turn;

[0011] Constructing a grading index; wherein the grading index includes at least one or a combination of the enterprise business relevance and its weight, and data sensitivity, data importance, and the impact of data loss or leakage;

[0012] Based on the classification indicators other than the enterprise business relevance and its weight, a data classification model is constructed to classify the traceability sensitive data under each first-level subcategory into initial sensitivity levels;

[0013] Based on the enterprise business relevance and its weight, the initial sensitivity grading results are optimized to obtain the final classification and grading results of each traceable sensitive data.

[0014] Preferably, the method of sequentially classifying all traceability sensitive data into primary and secondary categories includes:

[0015] The traceability sensitive data is classified into a primary category according to attribute characteristics, and is classified into a secondary category based on the data content, ownership and access rights of the traceability sensitive data under each primary subcategory.

[0016] Preferably, the process of constructing the enterprise business relevance includes:

[0017] Acquire and pre-process transaction data between upstream and downstream enterprises in the large-scale manufacturing industry chain; wherein the data indicators of the transaction data include enterprise ID, transaction ID, order quantity, unit price, and invoice date;

[0018] Based on the preprocessed transaction data, the R, F, and M values ​​are calculated with the enterprise ID as the unit;

[0019] Using the social network analysis (SNA) method, we build a social network model of enterprise transaction relationships and calculate the business relevance of each enterprise; including:

[0020] Count the freshness Fre and contractual relationship Con between each enterprise, combine the corresponding R, F, and M values, sum them up to measure the transaction relationship strength between any two enterprises, traverse all enterprises, and obtain the adjacency matrix of enterprise transaction relationship strength;

[0021] Among them, the freshness Fre is used to indicate whether there is a transaction relationship between the two enterprises on the reference date. If so, the value is 1, otherwise the value is 0; the contractual relationship Con is used to indicate whether the transaction volume between the two enterprises on the reference date reaches the set reference value. If so, the value is 1, otherwise the value is 0;

[0022] The adjacency matrix is ​​converted into a social network model of enterprise transaction relationships using the ucinet visualization tool; wherein the nodes on the social network model of enterprise transaction relationships represent different enterprises, the lines between the nodes represent the existence of a transaction relationship between the two enterprises, and the data on the lines represent the business correlation between the nodes;

[0023] Based on the enterprise transaction relationship social network model, the degree centrality result of the node corresponding to each enterprise is calculated and used as the enterprise business relevance of the enterprise.

[0024] Preferably, the preprocessing process of the transaction data includes:

[0025] The consumption amount is represented by the variable amount, amount = quantity * unit price; where quantity represents the order quantity and unit price represents the unit price;

[0026] Extract the purchase date and purchase time through the invoice date to distinguish transactions created by the enterprise at different time points;

[0027] Delete invalid data.

[0028] Preferably, the calculation of R, F, and M values ​​based on the pre-processed transaction data in units of enterprise ID includes:

[0029] Select the enterprise ID and calculate the R value of the enterprise based on the interval between the enterprise's most recent purchase date and the base date;

[0030] The number of transactions of the enterprise per unit time after the base date is counted to obtain the F value of the enterprise; different transaction IDs between the enterprise and the same transaction object in the same unit time are regarded as one transaction;

[0031] The amount of money spent on products purchased by the enterprise after the statistical base date is used to obtain the M value of the enterprise.

[0032] Preferably, the initial sensitivity classification result is optimized based on the enterprise business relevance and its weight; expressed as:

[0033] S i ' j =S ij +C o w

[0034] Among them, S ij Represents the set A ij Sensitivity Initial sensitivity, A ij The label set representing the traceability sensitive data with the first-level subclass j and the sensitivity level i; C o represents the business relevance of the enterprise; w represents the weight of the business relevance of the enterprise.

[0035] Preferably, the classification and grading method further comprises:

[0036] All traceability sensitive data and their final classification and grading results are stored on the blockchain.

[0037] A classification and grading system for traceability sensitive data, including:

[0038] The classification module is used to classify all traceability sensitive data into primary and secondary classifications in sequence;

[0039] A construction module, used to construct a grading index; wherein the grading index includes at least one of the following: enterprise business relevance and its weight, and data sensitivity, data importance, and the impact of data loss or leakage, or a combination of any several of them;

[0040] A classification module is used to build a data classification model based on classification indicators other than the enterprise business relevance and its weight, so as to perform initial sensitivity classification on the traceability sensitive data under each first-level subcategory;

[0041] And it is used to optimize the initial sensitivity grading results based on the enterprise business relevance and its weight, and obtain the final classification and grading results of each traceable sensitive data.

[0042] A storage medium stores a computer program for classifying and grading traceability sensitive data, wherein the computer program enables a computer to execute the method for classifying and grading traceability sensitive data as described above.

[0043] An electronic device, comprising:

[0044] One or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include a method for executing the classification and grading of traceable sensitive data as described above.

[0045] (III) Beneficial effects

[0046] The present invention provides a classification and grading method, system, storage medium and electronic device for tracing sensitive data. Compared with the prior art, it has the following beneficial effects:

[0047] In the present invention, all traceability sensitive data are classified into primary and secondary categories in turn; classification indicators are constructed; based on classification indicators other than enterprise business relevance and its weight, a data classification model is constructed to perform initial sensitivity classification on the traceability sensitive data under each primary subclass; based on the enterprise business relevance and its weight, the initial sensitivity classification result is optimized to obtain the final classification and classification result of each traceability sensitive data. On the basis of the traditional solution, combined with the characteristics of the traceability business, the business relevance between enterprises is used as an indicator for sensitive data classification, providing a new idea for the classification and grading of enterprise traceability sensitive data. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be 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.

[0049] Figure 1 A block diagram of a classification and grading method for traceability sensitive data provided by an embodiment of the present invention;

[0050] Figure 2 A block diagram of another method for classifying and grading sensitive data for source tracing provided by an embodiment of the present invention;

[0051] Figure 3 A schematic diagram of a data classification tree for tracing sensitive data provided by an embodiment of the present invention;

[0052] Figure 4 A schematic diagram of a social network model of enterprise transaction relationships provided by an embodiment of the present invention;

[0053] Figure 5 An example diagram of a traceability sensitive data classification scheme provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] The embodiments of the present application solve the technical problems that traditional classification and grading schemes have vague concepts and lack of business specificity by providing a classification and grading method, system, storage medium and electronic device for traceability sensitive data.

[0056] The technical solution in the embodiment of the present application is to solve the above technical problems, and the overall idea is as follows:

[0057] First of all, the term "traceability" refers to the tracing and management of the entire product life cycle by recording and tracking information from each link of product production, processing, transportation, and sales. Since traceability of sensitive data involves too many links and stakeholders, for example, when determining responsibility through traceability of sensitive data, the sensitivity level of the data must be considered to determine which data can be shared externally and which data cannot be disclosed publicly.

[0058] The present invention mainly classifies and grades the sensitive data involved in the traceability business, so as to realize the on-demand sharing of product data in the traceability process. Based on the traditional solution, the present invention combines the characteristics of the traceability business and takes the business relevance between enterprises (traceability entities) as an indicator for sensitive data classification.

[0059] Furthermore, most traditional sensitive data classification and grading schemes adopt a centralized data storage solution, that is, the data is stored in a central database. When the database is attacked maliciously, the stored data will be lost or leaked, causing irreversible losses.

[0060] In this regard, the embodiment of the present invention also uses blockchain technology as a storage medium to store all traceable sensitive data and its final classification and grading results, so as to eliminate the security risks of sensitive data classification and grading scheme in data storage and transmission.

[0061] In summary, the embodiments of the present invention solve the problems of the traditional classification and grading schemes being vague in concept, not very business-specific, and having weak system security, and realize the classification and grading sharing of traceable sensitive data.

[0062] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0063] Embodiment 1:

[0064] like Figure 1 As shown, an embodiment of the present invention provides a classification and grading method for traceability sensitive data, including:

[0065] S1. Classify all traceability sensitive data into primary and secondary categories in turn;

[0066] S2. Constructing a grading index; wherein the grading index at least includes the enterprise business relevance and its weight, and one or a combination of any of the following: data sensitivity, data importance, and the impact of data loss or leakage;

[0067] S3. Based on the classification indicators other than the enterprise business relevance and its weight, a data classification model is constructed to classify the traceability sensitive data under each first-level subcategory into initial sensitivity levels;

[0068] S4. Based on the enterprise business relevance and its weight, optimize the initial sensitivity grading results to obtain the final classification and grading results of each traceable sensitive data.

[0069] Based on the traditional solution, the embodiment of the present invention combines the characteristics of traceability business, takes the business correlation between enterprises as an indicator for sensitive data classification, and provides a new idea for the classification and classification of enterprise traceability sensitive data.

[0070] In an alternative embodiment, as Figure 2 shown, the classification and grading method provided by the embodiments of the present invention further includes:

[0071] S5. Store all traceability-sensitive data and their final classification and grading results on the blockchain.

[0072] The embodiments of the present invention use the blockchain as the underlying storage tool to store traceability-sensitive data, ensuring the credibility during data sharing and preventing the traceability process from being untrustworthy due to malicious tampering of the data.

[0073] Next, each step of the above solution will be introduced in detail:

[0074] In step S1, all traceability-sensitive data are classified at the first level and the second level in sequence.

[0075] Referring to the opinions given in the "Guidelines for the Classification and Grading of Industrial Data (Trial)", classify the large-scale manufacturing industry data according to factors such as manufacturing industry requirements, characteristics, business needs, data sources, and uses, and classify the data in the industrial and informatization fields into three levels: general data, important data, and core data according to the degree of harm caused by data being tampered with, damaged, leaked, illegally obtained, or illegally used to national security, public interests, or the legitimate rights and interests of individuals and organizations. After determining the data classification target, determine the data classification system according to the manufacturing industry characteristics, and then divide and classify the traceability-sensitive data according to the classification system and rules.

[0076] In this step of classification, first classify the traceability-sensitive data according to their attribute characteristics at the first level, and then classify them at the second level based on the data content, ownership, and access rights of the traceability-sensitive data under each first-level subclass, so as to group the same type of data into one category.

[0077] Exemplarily, data classification can use the form of a classification tree to subdivide data types layer by layer to form a detailed and clear classification scheme. The form of the classification tree is as Figure 3 shown.

[0078] In step S2, construct grading indicators.

[0079] After the data classification is completed, this step continues to grade the sensitivity of the data through the following four indicators to restrict the access rights of other users:

[0080] 1) Data sensitivity S, which divides the data into three levels: first level, second level, and third level. The higher the level, the higher the data sensitivity level.

[0081] Exemplarily, assume 0 < S < 10. When 0 < S ≤ S l the data sensitivity level is the first level. When S l < S ≤ S mWhen the data sensitivity level is level 2, S>S m The data sensitivity level is level 3. For example, level 3 sensitivity data includes personal information, financial data, etc.

[0082] 2) The importance of the system where the data is located.

[0083] If the system where the data is located is critical to the business, the data level is high; conversely, if a system interruption has little impact on the business, the data level is low.

[0084] 3) The impact of data loss or leakage.

[0085] Data loss or leakage will cause significant losses and impacts, which are high-level, while smaller impacts are medium-to-low levels.

[0086] 4) Enterprise business relevance C o with weight w.

[0087] The enterprise association degree indicates the business association degree between two enterprises, and its construction process is described later. The higher the business association degree, the closer the transactions between enterprises, the more sensitive data related to the two enterprises are involved, and the higher the degree of sharing between enterprises, that is, the lower the sensitivity level of such data relative to the two enterprises. The degree of association between different enterprises is slightly different, so the sensitivity level of the same category of data is also different when facing different enterprises. Sensitive data can be classified by different methods. The embodiment of the present invention classifies sensitive data by the business characteristics of the enterprise, and then classifies different categories of data in combination with the business association degree. Enterprises can assign weights to business association degrees according to their own needs. The higher the weight value, the more the enterprise considers business associations, which means that enterprises with high association values ​​can access sensitive data at a higher level.

[0088] There are many upstream and downstream enterprises in the large-scale manufacturing industry chain, and the business relationships between enterprises are complex and changeable. Some traceability sensitive data may involve multiple enterprises, but it is often difficult to measure the correlation between data and enterprises when traceability sensitive data is shared. Therefore, the RFM model is an important tool for analyzing customer value. It judges the customer's value status by analyzing the time from the last consumption to the present (Recency), the frequency of consumption within a certain period of time (Frequency), and the amount of consumption within a certain period of time (Monetary). The RFM model is used to describe the business relationship between manufacturing enterprises, and the VSM model is used to construct a business correlation model.

[0089] Specifically, for the business relevance C oThe embodiment of the present invention adopts the RFM method to calculate the business correlation index, and uses the SNA method to abstract and network large-scale manufacturing enterprises and their relationships to form a network diagram, and obtain the business correlation between enterprises. The specific steps include:

[0090] S100, obtain and pre-process transaction data between upstream and downstream enterprises in the large-scale manufacturing industry chain.

[0091] Large-scale manufacturing companies have huge orders, but the transaction situation between companies is relatively fixed. Here we select the following data indicators: company ID (user ID), transaction ID (trans ID), (quantity), unit price, and invoice date (invoice data).

[0092] When calculating the three indicators of the RFM model, the data must be preprocessed first:

[0093] First, the consumption amount is represented by the variable amount, amount = quantity * unit price; where quantity represents the order quantity and unit price represents the unit price;

[0094] Secondly, the purchase date and time are extracted through the invoice date to distinguish transactions created by the enterprise at different time points;

[0095] Finally, delete invalid data, that is, delete data with missing information (such as missing user ID or order quantity or amount with zero or negative invalid data), and only keep valid data.

[0096] S200, based on the pre-processed transaction data, calculating the R, F, and M values ​​in units of enterprise ID; including:

[0097] R value: Select an enterprise ID and calculate the R value of the enterprise based on the interval between the enterprise's most recent purchase date and the base date.

[0098] For example, assuming that the base date is October 9, 2024, and the company's most recent purchase date is October 9, 2024, the corresponding R value is 0 (R∈[0,12], in months).

[0099] F value: The number of transactions per unit time of the enterprise after the statistical base date is obtained to obtain the F value of the enterprise, which is expressed as count trans ID It represents the sum of the number of different transaction IDs within n months after the base date. It is particularly important to note that different transaction IDs between the enterprise and the same transaction object within the same unit of time are regarded as one transaction.

[0100] M value: The amount of money spent on products purchased by the enterprise after the statistical base date is used to obtain the M value of the enterprise (M>0).

[0101] S300, using the social network analysis (SNA) method to build a social network model of enterprise transaction relationships and calculate the business relevance of each enterprise.

[0102] The process of tracing information sharing contains a large amount of data, and each piece of information has a different effect on data classification. If the overall data is considered when calculating the correlation between nodes, the calculation difficulty will be greatly increased. By selecting data with large node similarity distinctions for sensitivity level classification, the amount of calculation can be reduced while optimizing the classification effect. Social network analysis (SNA) was proposed by British anthropologist Radcliffe-Brown in analyzing social structure. Considering that the relationship between various upstream and downstream enterprises in the large-scale manufacturing industry chain can constitute a corresponding network model, the embodiment of the present invention uses the SNA method to abstract and network large-scale manufacturing enterprises and their mutual relationships to form a network diagram, which is conducive to dividing nodes with different transaction correlations.

[0103] Specifically, step S300 includes:

[0104] S301. Count the freshness Fre and contractual relationship Con between each enterprise, combine the corresponding R, F, and M values, sum them up to measure the transaction relationship strength between any two enterprises, traverse all enterprises, and obtain the adjacency matrix of enterprise transaction relationship strength.

[0105] Among them, the freshness Fre is used to characterize whether there is a transaction relationship between the two enterprises on the reference date. If so, the value is 1, otherwise the value is 0; the contractual relationship Con is used to characterize whether the transaction volume of the two enterprises on the reference date reaches the set reference value. If so, the value is 1, otherwise the value is 0.

[0106] For example, the relationship degrees between enterprises are summarized based on the above five relationships and converted into an adjacency matrix as shown in Table 1:

[0107] Table 1

[0108]

[0109]

[0110] In this table, A1-A7 are 7 suppliers, B1-B8 are 8 manufacturers, C1-C8 are 8 transporters, and D1-D8 are 8 dealers. The five indicators of R, F, M value, freshness Fre, and contractual relationship Con between each enterprise are scored and summarized to form the adjacency matrix (1-module matrix) of the above transaction relationship strength, that is, the values ​​in the matrix are obtained by R+F+M+Fre+Con. 0 means that there is no transaction relationship between the two enterprises during this period, and the larger the value, the closer the transaction.

[0111] S302, using ucinet visualization tool, convert the adjacency matrix into Figure 4 The social network model of enterprise transaction relationships shown.

[0112] like Figure 4 As shown, the nodes on the business transaction relationship social network model represent different enterprises, the lines between the nodes represent the existence of a transaction relationship between the two enterprises, and the data on the lines represent the business correlation between the nodes.

[0113] In addition, in order to make the transaction relationship between nodes clearer, the figure screens out nodes with a correlation value greater than or equal to 3.0, and the nodes with the highest correlation value are bolded. It can be clearly seen from the figure that A6, B2, B5, B3, and C8 are at the center of the transaction relationship network, and have a close relationship with other enterprises in the transaction network; while A1, A2, D1, D5, and D6 are at the edge of the transaction relationship network, and have a loose relationship with other nodes in the transaction relationship network.

[0114] S303: Based on the enterprise transaction relationship social network model, calculate the degree centrality result of the node corresponding to each enterprise and use it as the enterprise business relevance of the enterprise.

[0115] Centrality is to measure whether a node is in a central or peripheral position in the network. There are three indicators for measuring centrality, namely degree centrality, betweenness centrality, and proximity centrality. The present invention measures the closeness between nodes, and specifically selects degree centrality to calculate the degree.

[0116] Continuing with the above example, Figure 4 From the social network model of enterprise transaction relations, we can qualitatively see the influence of each node. By calculating the degree centrality, the degree centralities of A6, B2, B5, B3, and C8 are 91, 83, 85, 80, and 79, respectively, while the degree centralities of A1, A2, D1, D5, and D6 are 50, 53, 62, 58, and 57, respectively.

[0117] So far, this step completes the process of building the enterprise business relevance.

[0118] In summary, the indicators 1), 2), and 3) above refer to the general data classification and grading method, while indicator 4) is innovatively introduced by the embodiment of the present invention. When the traceability data is interactively shared, the business relationship between the two enterprises is considered, which avoids the inability to accurately locate the sharing subject and scope by simply using the general classification and grading scheme. For details, see the contents of steps S3 and S4:

[0119] In step S3, based on the grading indicators other than the enterprise business relevance and its weight, a data classification model is constructed to perform initial sensitivity grading on the traceability sensitive data under each first-level subclass.

[0120] Considering the business characteristics of manufacturing enterprises, traceability data can be divided into R&D, production, operation and maintenance, management and external data, and further subdivided into five primary subcategories: Figure 5 The secondary sub-category data shown. The classified data can be divided into corresponding sensitivity levels through the qualitative indicators of 1), 2), and 3), and the enterprise can then determine the sharing conditions of each level of data according to their respective management needs.

[0121] Figure 5 The traceability sensitive data classification and grading scheme divides the first-level subcategories from the business dimension, and divides the data contained in the major categories into more fine-grained categories. In order to adapt to the scalability of the scheme under different classification methods, Figure 5 The specific classification results are abstracted into the data classification model shown in Table 2.

[0122] Table 2

[0123]

[0124] In this table, T class -j indicates the first-level subclass of traceable sensitive data, which is the coarsest classification; T sense -i indicates the sensitivity level of traceable sensitive data. Here, data sensitivity levels are divided into three levels for example, and different disclosure methods and management modes are set for data of different sensitivity levels; A ij represents the set of traceability sensitive data labels with first-level subclass j and sensitivity level i, where A ij ={a1,a2,...,a n}, a k Represents the secondary feature label of subclass j and sensitivity level i.

[0125] In step S4, based on the enterprise business relevance and its weight, the initial sensitivity grading result is optimized to obtain the final classification and grading result of each traceability sensitive data.

[0126] It is understandable that based on the obtained correlation analysis results, we can see the closeness of the relationship between enterprises. In the actual process of sharing sensitive traceability data, traceability sensitive data is often only of reference value to enterprises with which they have transactions. Therefore, considering the relationship between enterprises can accurately locate the scope of data sharing and prevent data from being used maliciously.

[0127] The determination of association relationships provides a new idea for the classification and grading of traceable sensitive data. Limiting the scope of data sharing based on the strength of association between enterprises can more effectively ensure the security and privacy of data and maximize data sharing.

[0128] Accordingly, this step is based on the above 4) business relevance of the enterprise, and adjusts the proportion of indicators through weight coefficients to optimize the initial sensitivity classification results; it is expressed as:

[0129] S i ' j =S ij +C o w

[0130] Among them, S ij Represents the set A ij Sensitivity Initial sensitivity, A ij The label set representing the traceability sensitive data with the first-level subclass j and the sensitivity level i; C o represents the business relevance of the enterprise; w represents the weight of the business relevance of the enterprise.

[0131] Finally, A is reclassified according to the optimized sensitivity. ij The sensitivity level of internal data can realize the classification and grading of traceable sensitive data based on business relevance.

[0132] In step S5, all traceability sensitive data and their final classification and grading results are stored on the blockchain.

[0133] So far, the embodiment of the present invention has completed the entire process of classification and grading of traceable sensitive data.

[0134] Embodiment 2:

[0135] The embodiment of the present invention provides a classification and grading system for traceability sensitive data, including:

[0136] The classification module is used to classify all traceability sensitive data into primary and secondary classifications in sequence;

[0137] A construction module, used to construct a grading index; wherein the grading index includes at least one of the following: enterprise business relevance and its weight, and data sensitivity, data importance, and the impact of data loss or leakage, or a combination of any several of them;

[0138] A classification module is used to build a data classification model based on classification indicators other than the enterprise business relevance and its weight, so as to perform initial sensitivity classification on the traceability sensitive data under each first-level subcategory;

[0139] And it is used to optimize the initial sensitivity grading results based on the enterprise business relevance and its weight, and obtain the final classification and grading results of each traceable sensitive data.

[0140] Embodiment 3:

[0141] An embodiment of the present invention provides a storage medium storing a computer program for classifying and grading traceability sensitive data, wherein the computer program enables a computer to execute the method for classifying and grading traceability sensitive data as described above.

[0142] Embodiment 4:

[0143] An embodiment of the present invention provides an electronic device, including:

[0144] One or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include a method for executing the classification and grading of traceable sensitive data as described above.

[0145] It is understandable that the classification and grading system, storage medium and electronic device for traceability sensitive data provided in the embodiments of the present invention correspond to the classification and grading method for traceability sensitive data provided in the embodiments of the present invention, and the explanations, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the classification and grading method, which will not be repeated here.

[0146] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0147] 1. The embodiments of the present invention are aimed at the sharing of traceability sensitive data in large-scale manufacturing industries, and provide ideas for the classification and grading of traceability sensitive data of manufacturing enterprises.

[0148] 2. The embodiments of the present invention take into account the business relationship between enterprises when classifying and grading sensitive data, thereby solving the problem of weak data sharing caused by the lack of pertinence of traditional classification and grading schemes, and resolving the contradiction between data sharing and privacy protection.

[0149] 3. The embodiment of the present invention uses blockchain as the underlying storage tool to store traceability sensitive data, which ensures the credibility of data sharing and prevents data from being maliciously tampered with, resulting in the traceability process being unreliable.

[0150] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0151] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A classification and grading method for traceability sensitive data, characterized in that: include: All traceability sensitive data are classified into primary and secondary categories in turn; Constructing a grading index; wherein the grading index includes at least one or a combination of the enterprise business relevance and its weight, and data sensitivity, data importance, and the impact of data loss or leakage; Based on the classification indicators other than the enterprise business relevance and its weight, a data classification model is constructed to classify the traceability sensitive data under each first-level subcategory into initial sensitivity levels; Based on the enterprise business relevance and its weight, the initial sensitivity grading results are optimized to obtain the final classification and grading results of each traceable sensitive data.

2. The classification and grading method according to claim 1, characterized in that: The above method is to classify all traceability sensitive data into primary and secondary categories in turn; including: The traceability sensitive data is classified into a primary category according to attribute characteristics, and is classified into a secondary category based on the data content, ownership and access rights of the traceability sensitive data under each primary subcategory.

3. The classification and grading method according to claim 1, characterized in that: The process of constructing the enterprise business relevance includes: Acquire and pre-process transaction data between upstream and downstream enterprises in the large-scale manufacturing industry chain; wherein the data indicators of the transaction data include enterprise ID, transaction ID, order quantity, unit price, and invoice date; Based on the preprocessed transaction data, the R, F, and M values ​​are calculated with the enterprise ID as the unit; Using the social network analysis (SNA) method, we build a social network model of enterprise transaction relationships and calculate the business relevance of each enterprise; including: Count the freshness Fre and contractual relationship Con between each enterprise, combine the corresponding R, F, and M values, sum them up to measure the transaction relationship strength between any two enterprises, traverse all enterprises, and obtain the adjacency matrix of enterprise transaction relationship strength; Among them, the freshness Fre is used to indicate whether there is a transaction relationship between the two enterprises on the reference date. If so, the value is 1, otherwise the value is 0; the contractual relationship Con is used to indicate whether the transaction volume between the two enterprises on the reference date reaches the set reference value. If so, the value is 1, otherwise the value is 0; The adjacency matrix is ​​converted into a social network model of enterprise transaction relationships using the ucinet visualization tool; wherein the nodes on the social network model of enterprise transaction relationships represent different enterprises, the lines between the nodes represent the existence of a transaction relationship between the two enterprises, and the data on the lines represent the business correlation between the nodes; Based on the enterprise transaction relationship social network model, the degree centrality result of the node corresponding to each enterprise is calculated and used as the enterprise business relevance of the enterprise.

4. The classification and grading method according to claim 3, characterized in that: The preprocessing process of the transaction data includes: The consumption amount is represented by the variable amount, amount = quantity * unit price; where quantity represents the order quantity and unit price represents the unit price; Extract the purchase date and purchase time through the invoice date to distinguish transactions created by the enterprise at different time points; Delete invalid data.

5. The classification and grading method according to claim 4, characterized in that: The R, F and M values ​​are calculated based on the pre-processed transaction data with the enterprise ID as the unit; include: Select the enterprise ID and calculate the R value of the enterprise based on the interval between the enterprise's most recent purchase date and the base date; The number of transactions of the enterprise per unit time after the base date is counted to obtain the F value of the enterprise; different transaction IDs between the enterprise and the same transaction object in the same unit time are regarded as one transaction; The amount of money spent on products purchased by the enterprise after the statistical base date is used to obtain the M value of the enterprise.

6. The classification and grading method according to claim 1, characterized in that: The initial sensitivity classification result is optimized based on the enterprise business relevance and its weight; expressed as: S i ′ j =S ij +C o w Among them, S ij Represents the set A ij Sensitivity Initial sensitivity, A ij The label set representing the traceability sensitive data with the first-level subclass j and the sensitivity level i; C o represents the business relevance of the enterprise; w represents the weight of the business relevance of the enterprise.

7. The classification and grading method according to any one of claims 1 to 6, characterized in that: Also includes: All traceability sensitive data and their final classification and grading results are stored on the blockchain.

8. A classification and grading system for traceability sensitive data, characterized in that: include: The classification module is used to classify all traceability sensitive data into primary and secondary classifications in sequence; A construction module, used to construct a grading index; wherein the grading index includes at least one of the following: enterprise business relevance and its weight, and data sensitivity, data importance, and the impact of data loss or leakage, or a combination of any several of them; A classification module is used to build a data classification model based on classification indicators other than the enterprise business relevance and its weight, so as to perform initial sensitivity classification on the traceability sensitive data under each first-level subcategory; And it is used to optimize the initial sensitivity grading results based on the enterprise business relevance and its weight, and obtain the final classification and grading results of each traceable sensitive data.

9. A storage medium, characterized in that: It stores a computer program for classifying and grading traceability sensitive data, wherein the computer program enables a computer to execute the method for classifying and grading traceability sensitive data as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: one or more processors; Memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include a method for executing the classification and grading method of traceability sensitive data as described in any one of claims 1 to 7.

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