Information security protection method and system for multi-dimensional data

Abstract

The present invention relates to the field of data encryption technology, and more particularly to a method and system for information security protection of multi-dimensional data, comprising: obtaining business data and access logs for each dimension of a venture capital enterprise database; constructing a data association structure graph based on the direct associations between business data of different dimensions, combined with access time sequences and the distribution of access objects in the access logs; analyzing the association stability characteristics between nodes in the data association structure graph based on the connection relationships between nodes on access paths and access information corresponding to business data on the nodes on the access paths, and obtaining dynamic risk values ​​for the nodes in the data association structure graph; and performing hierarchical access encryption on the multi-dimensional business data in the venture capital enterprise database based on the dynamic risk values ​​of nodes corresponding to dimensional business data accessed by users of the venture capital enterprise. The present invention ensures the security of hierarchical access encryption of the enterprise's multi-dimensional business data using dynamic risk values.

Description

Technical Field

[0001] The present invention relates to the field of data encryption technology, and in particular to an information security protection method and system for multi-dimensional data. Background Art

[0002] With the rapid development of information technology, data, as a core production factor in the venture capital industry, has seen explosive growth in management scenarios such as project due diligence and investment decision-making. According to a venture capital industry research report, leading institutions process over 10 million pieces of project data daily, encompassing structured data such as startup business information and equity structures, as well as multimodal unstructured data such as due diligence video recordings, market sentiment text, and financial statement images. In the venture capital sector in particular, data dimensions are expanding from traditional, limited fields such as project valuations and basic investor information to include complex dimensions such as geographic location (such as the industrial park where the project is located), time series (such as cash flow cycles), and audio conference records. The integration of these multidimensional data enables more comprehensive investment decision-making analysis, but also presents unique challenges for information security.

[0003] In venture capital business scenarios, the limitations of traditional information security protection methods are becoming increasingly apparent. For example, one venture capital firm adopted a unified encryption strategy for all data, resulting in highly sensitive investor bank account information being encrypted identically to publicly available industry research reports. This not only increases the risk of core data leakage, but also reduces user access efficiency due to over-encryption of less sensitive data. More critically, users in different roles within venture capital firms have varying granular access requirements for data dimensions. For example, when investment managers frequently access enterprise database data, the access behavior itself carries dynamic risks, such as abnormal IP logins and access during off-hours. Traditional methods, using fixed encryption levels, ignore dynamic risk factors such as access timing and access operations, resulting in poor data security. Summary of the Invention

[0004] In order to solve the technical problem that traditional methods use fixed encryption levels and ignore dynamic risk factors such as access timing and access operations, resulting in poor security of data encryption results, the purpose of the present invention is to provide an information security protection method and system for multi-dimensional data. The technical solutions adopted are as follows:

[0005] In a first aspect, the present invention provides an information security protection method for multi-dimensional data, comprising:

[0006] Obtain business data of each dimension in the venture capital enterprise database and access logs within a preset time period;

[0007] Based on the direct association between business data of different dimensions, combined with the access time sequence and the distribution of access objects in the access log, a data association structure diagram is constructed with the business data of each dimension as a node;

[0008] According to the connection relationship between nodes on the access path in the data association structure diagram and the access information of the business data corresponding to the nodes on the access path, the association stability characteristics between the nodes are analyzed to obtain the dynamic risk value of the nodes in the data association structure diagram;

[0009] According to the dynamic risk value of the node of the dimensional business data corresponding to the user access operation of the venture capital enterprise, hierarchical access encryption is performed on the multi-dimensional business data of the venture capital enterprise database.

[0010] Preferably, the data association structure diagram is constructed based on the direct association relationship between business data of different dimensions, in combination with the access sequence and the distribution of access objects in the access log, using the business data of each dimension as a node, specifically including:

[0011] Based on the business association relationship between the business data of each dimension and the business data of other dimensions, a display link relationship between the business data of different dimensions is constructed;

[0012] Sort different access objects based on the access sequence of each user in the access log, and build implicit link relationships between business data of different dimensions;

[0013] Taking the business data of each dimension as a node, a data association structure diagram is constructed based on the explicit link relationship and implicit link relationship.

[0014] Preferably, the process of sorting different access objects according to the access sequence of each user in the access log and building implicit link relationships between business data of different dimensions specifically includes:

[0015] Obtain all access data of each user in the access log. Each access data includes the access time and access object. The access object is business data of different dimensions.

[0016] The access data of each user are connected to the access objects in the order of access time to form an access data chain for each user's each access. The association relationship between business data of different dimensions in all access data chains of each user is used as an implicit link relationship between business data of different dimensions.

[0017] Preferably, the step of analyzing the association stability characteristics between the nodes based on the connection relationship between the nodes on the access path in the data association structure diagram and the access information of the business data corresponding to the nodes on the access path to obtain the dynamic risk value between the nodes in the data association structure diagram specifically includes:

[0018] Determine the display stability index between different nodes according to the display link relationship between different nodes in the data association structure diagram;

[0019] According to the implicit association relationship between different nodes in the data association structure graph, combined with the access frequency and access time distribution of the corresponding nodes, the implicit stability index between different nodes is obtained;

[0020] According to the implicit stability index and explicit stability index between different nodes on each access data chain, the dynamic risk value between nodes in the data association structure graph is obtained.

[0021] Preferably, the implicit association relationship between different nodes in the data association structure diagram is combined with the access frequency and access time distribution of the corresponding nodes to obtain the implicit stability index between different nodes, which specifically includes:

[0022] Build an FP tree based on the business data of each dimension contained in the access data chain of each user's access, and extract each associated access chain from the root node to the leaf node of the FP tree;

[0023] The access characteristic factor of each associated access chain is obtained by calculating the average value of the ratio between the frequency of each tree node on each associated access chain and the frequency of the adjacent previous tree node.

[0024] The average time interval between visits of two adjacent tree nodes on each associated access chain is obtained as the average time length; the normalized value of the ratio between the access characteristic factor of each associated access chain and the corresponding average time length is used as the implicit stability indicator between the nodes corresponding to the business data of each dimension contained in each associated access chain.

[0025] Preferably, determining the display stability index between different nodes according to the display link relationship between different nodes in the data association structure diagram specifically includes:

[0026] The display stability index between nodes in each display link relationship is set to a first preset value, wherein the first preset value is greater than or equal to all implicit stability indexes.

[0027] Preferably, obtaining the dynamic risk value of the node in the data association structure diagram according to the implicit stability index and the explicit stability index between different nodes on each access data chain specifically includes:

[0028] Using the display stability index between the nodes in the display link relationship, the implicit stability index between the nodes corresponding to the display link relationship in the implicit link relationship in the data association structure diagram is updated;

[0029] The implicit stability indicators between nodes in each user's access data chain are negatively correlated and normalized to obtain the dynamic risk value between nodes in each user's access data chain.

[0030] Preferably, the step of performing hierarchical access encryption on the multi-dimensional business data in the venture capital enterprise database according to the dynamic risk value of the node of the dimensional business data corresponding to the user access operation of the venture capital enterprise specifically includes:

[0031] Based on the security attributes of the business data in each dimension, a preset static risk value of the business data in each dimension is obtained;

[0032] The static risk value and dynamic risk value of the business data in each dimension are combined to obtain the cumulative risk value of the business data in each dimension;

[0033] According to the cumulative risk value of all dimensions of business data accessed by each user in the access log of the venture capital enterprise database, hierarchical access encryption is performed on multi-dimensional business data.

[0034] Preferably, performing hierarchical access encryption on multi-dimensional business data based on the cumulative risk value of business data of all dimensions accessed by each user in the access log of the venture capital enterprise database specifically includes:

[0035] Normalize the cumulative risk values ​​of all dimensions of business data accessed by each user in the access log of the venture capital enterprise database to obtain the access risk of each user in the access log;

[0036] The access risks of all users are divided into risk levels, and the access logs of users corresponding to different risk levels are encrypted separately.

[0037] In a second aspect, the present invention further provides an information security protection system for multi-dimensional data, which is used to implement the steps of an information security protection method for multi-dimensional data. The information security protection system for multi-dimensional data specifically includes:

[0038] The data acquisition module is used to obtain business data and access logs of each dimension of the venture capital enterprise database;

[0039] The graph structure construction module is used to construct a data association structure graph using the business data of each dimension as a node based on the direct association relationship between business data of different dimensions and the access time sequence and distribution of access objects in the access log;

[0040] The risk analysis module is used to analyze the association stability characteristics between nodes based on the connection relationship between nodes on the access path in the data association structure diagram and the access information of the business data corresponding to the nodes on the access path, and obtain the dynamic risk value of the nodes in the data association structure diagram;

[0041] The data encryption module is used to encrypt access to the multi-dimensional business data of the venture capital enterprise database based on the dynamic risk value of the dimensional business data corresponding to the node.

[0042] The embodiments of the present invention have at least the following beneficial effects:

[0043] The present invention first collects enterprise multi-dimensional data and access logs to provide a data basis for the subsequent analysis of the dynamic risk level generated by enterprise user access behavior. Then, combining the data association characteristics of the direct association relationship between business data and the access-related characteristic information when the business data is accessed, a data association structure diagram is constructed. Furthermore, by analyzing the connection relationship of the nodes on the access path corresponding to the access operation in the data association structure diagram, as well as the accessed information, the stability association characteristics between the nodes are measured. During the user access process, the stronger the stability of the data chain, the lower the corresponding risk level. The dynamic risk value of the node is quantified, integrating the inherent sensitivity of the direct association of data and the dynamic impact of access behavior, realizing the dual risk assessment of business rules combined with user behavior, so that the security of hierarchical access encryption of the enterprise's multi-dimensional business data using dynamic risk values ​​is finally guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. 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 any creative work.

[0045] Figure 1 This is a flowchart of the steps of a method for information security protection of multi-dimensional data provided by the present invention;

[0046] Figure 2 It is a flowchart of the steps of the method for constructing a data association structure diagram provided by the present invention;

[0047] Figure 3 It is a schematic diagram of a partial graph structure showing link relationships provided by the present invention;

[0048] Figure 4 It is a partial schematic diagram of the data association graph structure provided by the present invention;

[0049] Figure 5It is a flowchart of the steps of the method for obtaining the dynamic risk value of a node in the data association graph structure provided by the present invention;

[0050] Figure 6 This is a flowchart of the steps for hierarchical access encryption of multi-dimensional business data provided by the present invention. DETAILED DESCRIPTION

[0051] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features and effects of a method and system for information security protection of multi-dimensional data proposed by the present invention. 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 of one or more embodiments may be combined in any suitable form.

[0052] 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.

[0053] The following describes in detail a method and system for protecting multi-dimensional data security provided by the present invention with reference to the accompanying drawings.

[0054] The specific implementation scenario targeted by this invention is as follows: While currently used traditional encryption technologies can guarantee data confidentiality to a certain extent, they are mostly used to encrypt single or low-dimensional data and have significant limitations when processing multi-dimensional data. These technologies are unable to perform targeted encryption based on the different dimensions and their weights of the data, resulting in a cumbersome and inefficient encryption process, and they cannot fully meet the security requirements of multi-dimensional data in different application scenarios. Therefore, it is necessary to analyze the risk characteristics of multi-dimensional data in venture capital companies' databases to achieve the goal of encrypting data with different security levels according to different risk levels.

[0055] See also Figure 1 , which shows a flowchart of a method for information security protection of multi-dimensional data provided by an embodiment of the present invention, the method comprising the following steps:

[0056] Step S100: Obtain business data of each dimension of the venture capital enterprise database and access logs within a preset time period.

[0057] In modern information technology, data is typically stored in databases. Therefore, we first obtain the basic characteristics of multidimensional data from the venture capital firm's database. Specifically, we obtain business data and access logs for each dimension from the venture capital firm's database. Each dimension corresponds to a specific type of business data. To monitor the dynamic risk of changes in user access information within the venture capital firm in real time, we collect access logs within a preset time period to monitor access risk levels and enhance dynamic risk analysis.

[0058] Furthermore, the access log includes access information corresponding to each access operation of one or more users. Specifically, each access operation by each user corresponds to the access time and access object, where the access object refers to business data of different dimensions in the venture capital enterprise database. The access information of each user's access operation in the access log reflects the user's operation behavior over time. For example, a user accesses customer information at time t, accesses contract data for modification at time t+1, and submits the data for approval at time t+6.

[0059] As a specific example, business data is divided into categories according to business areas or business departments. This is only an exemplary display. The data category division results of databases of different companies may be different. Implementers can obtain them according to the specific implementation scenario to ensure that the business data of one dimension corresponds to one category of business data. For example, the dimensions of business data may include investment project information dimension, investor dimension, project financial data dimension, project risk due diligence dimension, etc. It should be noted that the data types of the same dimension in the venture capital enterprise database are the same. Only a partial explanation is given here without specific restrictions. At the same time, the length of the preset time period can be selected as 10 minutes, or other time lengths, and the implementer can set it according to the specific implementation scenario.

[0060] Step S200 , based on the direct association relationship between business data of different dimensions and in combination with the access sequence and the distribution of access objects in the access log, a data association structure diagram is constructed with the business data of each dimension as a node.

[0061] In multidimensional data, a tight causal logic chain exists between data associations and data risks and security requirements. This relationship is not simply juxtaposed or independent; rather, it dynamically defines and amplifies data risks through associations, thereby driving differentiated security requirements. Therefore, when constructing the data association structure diagram, this embodiment considers both the direct connections between business data of different dimensions during enterprise business transactions, as well as the data associations between business data of different dimensions during user access. This allows for a more comprehensive reflection of the distribution of data associations within an enterprise database.

[0062] Furthermore, data associations can be generally categorized as explicit associations and implicit associations. Explicit associations primarily refer to data connections established based on clear technical constraints and clear business logic requirements. The connections between data are often visible, such as primary and foreign keys between data tables. Implicit associations are indirectly formed based on data content, access patterns, or statistical characteristics. Because these connections are based on business or access patterns, they are not mandatory and therefore lack visible connections. They must be inferred through algorithms and are probabilistic. In this embodiment, feature analysis is primarily performed on implicit associations between data generated by user access operations.

[0063] As a specific example, Figure 2 As shown, the method for constructing a data association structure diagram can be implemented by steps S201 to S203. Step S201 is intended to analyze the explicit associations between data, step S202 is intended to analyze the implicit associations between data, and step S203 is intended to analyze the associations between the combined enterprise data in two aspects to construct a more comprehensive data structure diagram.

[0064] Step S201 : constructing a display link relationship between business data of different dimensions based on the business association relationship between the business data of each dimension and the business data of other dimensions.

[0065] Specifically, by querying the foreign keys in the data tables of enterprise data of different dimensions in the venture capital enterprise database, the direct association relationship between enterprise data of different dimensions can be obtained as a display link relationship, and the business data of each dimension can be used as a node, such as Figure 3 A partial graph structure showing link relationships is shown.

[0066] More specifically, a primary key refers to the field or combination of fields that uniquely identifies a record in an enterprise data table. A primary key can be a single field or a combination of multiple fields. Its purpose is to ensure the uniqueness of each record in the table and to establish relationships with other tables. A primary key has the following functions: It ensures the uniqueness of each record in the table, preventing data redundancy and duplication. It also serves as an important basis for establishing relationships between tables.

[0067] A foreign key is a field in an enterprise data table that establishes a relationship with the primary key of another table. The value of the foreign key field points to the primary key value of the other table, ensuring data integrity and consistency. Foreign keys can be used to establish relationships between tables and implement data join queries. Foreign keys have the following functions: Foreign keys ensure data integrity and consistency between tables, preventing data inconsistencies and errors; foreign keys can be used to perform join queries between tables, facilitating data acquisition and analysis.

[0068] In other words, by querying foreign keys in data tables, one can directly obtain direct relationships between business data of different dimensions. For example, the primary key of an investment project forms a primary-foreign key association with the foreign key of the order table, indicating that "an order belongs to a certain investment project." Displaying associations represents the risk transmission path of business rules; even without user access, risks are inevitably transmitted. In other embodiments, implementers can configure these based on the specific implementation scenarios of their enterprises.

[0069] Step S202 : sorting different access objects according to the access sequence of each user in the access log, and building implicit link relationships between business data of different dimensions.

[0070] It should be noted that in the above steps, all access data of each user in the access log has been obtained. Each access data includes access time and access object. The access object is business data of different dimensions and will not be repeated here.

[0071] Specifically, each user's access data is connected to the access objects in the order of access time to form an access data chain for each user's access, and the association relationship between business data of different dimensions in all access data chains of each user is used as an implicit link relationship between business data of different dimensions.

[0072] The construction of each access data chain for each user is essentially a modeling of the user's access behavior trajectory. Its core is to organize disordered access records into directed links in chronological order, thereby revealing the temporal patterns and potential correlations of data access. The access data chain reflects the temporal dependencies formed between access objects by user operations. For example, when conducting competitive analysis, an investment manager user may access the "Project A Valuation Report" and then the "Competitor B Research Report." When conducting risk verification, risk control personnel users will frequently access the "Founder Credit Report" and "Project Legal Opinion."

[0073] The access data chain will not only be a time series record of data access, but also a dynamic model that can characterize user behavior characteristics and identify potential risks, providing a more accurate assessment basis for business data security.

[0074] Step S203 : Taking the business data of each dimension as a node, and building a data association structure diagram based on the explicit link relationship and the implicit link relationship.

[0075] Specifically, if Figure 4This is a partial view of the data association structure diagram. Each circle in the diagram represents a node. Solid lines represent explicit links, while dashed lines represent implicit links. It should be understood that explicit links represent direct relationships between business data of different dimensions in an enterprise database, while implicit links represent access relationships between business data of different dimensions in an enterprise database due to user access operations. Each dimension of business data corresponds to one node.

[0076] It should also be noted that the implicit link relationship exists due to the user's access operation. There may be data overlap between the business data of different dimensions contained in the implicit link relationship and the business data of different dimensions contained in the explicit link relationship (not shown in the figure). That is, in the implicit link relationship, there is an implicit association between the business data in chronological order, and there may also be an explicit association between the business data. For example, when certain explicitly associated business data is accessed by a user, the explicitly associated business data also exists in a user's access data chain, and there is also an implicit association between the business data of the two dimensions of the explicit association.

[0077] Step S300 , analyzing the association stability characteristics between nodes based on the connection relationship between nodes on the access path in the data association structure diagram and the access information of the business data corresponding to the nodes on the access path, and obtaining the dynamic risk value of the nodes in the data association structure diagram.

[0078] In a user's short-term access data, the stronger the stability of the access data chain, the more compliant the user's operation is with regulations, that is, the more compliant it is with normal business processes, and the lower the risk transfer value of their access data. Finally, through the user's short-term access data chain, risk transfer and accumulation are performed, the short-term risk accumulation value is calculated, and the user's dynamic risk value is determined. Based on this, this embodiment measures the stability of the association relationship between nodes in the graph structure by analyzing whether the access operations of different users in the access log are regular. The access data chain corresponding to the user's access operation represents the distribution of access paths in the data association graph structure. When the user's access operation corresponds to the access object on the access data chain, the more regular the access operation is, and the more compliant the access operation is with normal business processes, the higher the stability between the nodes on the user's corresponding access data chain is, which in turn reflects the lower risk value of the corresponding node in the data association structure graph.

[0079] As a specific example, Figure 5 As shown, the method for obtaining the dynamic risk value of a node in the data association structure graph can be implemented by steps S301 to S303.

[0080] Step S301 : determining display stability indices between different nodes according to display link relationships between different nodes in a data association structure graph.

[0081] Specifically, the display stability index between nodes in each display link relationship is set to a first preset value, wherein the first preset value is greater than or equal to all implicit stability indexes. The display stability index characterizes the association stability between every two nodes in each display link relationship.

[0082] More specifically, since the display association is a hard connection, as long as the two nodes belonging to the display association are accessed, a connection will inevitably appear. Therefore, the stability between the two hard-connected nodes is the highest. In this embodiment, the stability characteristic value of the connection between the nodes in the display link relationship is set to the maximum value, that is, the first preset value in this embodiment is 1.

[0083] Step S302 : Obtain implicit stability indicators between different nodes based on the implicit association relationship between different nodes in the data association structure diagram and in combination with the access frequency and access time distribution of the corresponding nodes.

[0084] Implicit associations are often derived through access data chains. The stability between different nodes in an implicit link relationship is specific to different access data chains. First, the association stability is determined for the access data chain. The stronger the dependency between enterprise data access records across different dimensions and the closer the temporal connection, the greater the degree of stability between business data across different dimensions. Within an associated access chain, the greater the overall access frequency, the lower the access frequency of business data across each dimension, and the shorter the time interval between accesses to business data across different dimensions, indicating a more stable access relationship between the business data. This allows the association stability of each chain to be calculated.

[0085] In the first step, an FP tree is constructed based on the business data of each dimension contained in the access data chain of each user each time, and each associated access chain is extracted from the root node to the leaf node of the FP tree.

[0086] Each access data chain for each user in the access log is fed into the FP-Growth algorithm to construct a conditional FP tree. Each node in the conditional FP tree has a different frequency. To extract the primary conditional path, the frequencies of all nodes are obtained. A frequency threshold is then determined using the maximum inter-class variance method. Nodes below the threshold are filtered using the frequency threshold to obtain the core FP tree. In the core FP tree, each associated access chain is extracted, starting from the root node and ending at each leaf node (i.e., the end node).

[0087] It should be understood that the core FP tree is constructed based on all access data chains of all users. The method of constructing the FP tree is a well-known technology and will not be described in detail here. The associated access chain represents the main access association of all user access behaviors.

[0088] It should be further explained that in this step, all nodes are tree nodes in the FP tree, which are different from the nodes in the data association structure diagram in other steps, but they are all represented by the business data of each dimension. Therefore, the business data of each dimension corresponding to the tree nodes contained in the association access chain can find corresponding business data with the business data of each dimension corresponding to each node in the data association graph structure, and thus there is a corresponding relationship between the association access chain and the data association graph structure.

[0089] In the second step, the average value of the ratio between the frequency of each tree node in each associated access chain and the frequency of the adjacent previous tree node is calculated to obtain the access characteristic factor of each associated access chain.

[0090] Specifically, taking the i-th associated access chain as an example, the ratio of the frequency corresponding to the n-th tree node to the frequency corresponding to the n-1-th tree node is obtained, and the average of all ratios in the i-th associated access chain is used as the access characteristic factor of the i-th associated access chain. The access characteristic factor reflects the ratio of adjacent access frequencies of business data in the associated access chain and reflects the probability of access continuity. When the ratio between the frequency of the n-th tree node and the frequency of the n-1-th tree node is greater than 1 and the larger the ratio, the probability of accessing the n-th tree node after accessing the n-1-th tree node is higher than random access, indicating a higher probability of access continuity.

[0091] It should be noted that each tree node in the FP tree contains the accessed business data object and the frequency of the tree node appearing in all access data chains. That is, the frequency corresponding to each tree node can be directly obtained through the FP tree, which represents the number of times the business data corresponding to the tree node appears in all user access operations.

[0092] The third step is to obtain the average time interval between accesses to two adjacent tree nodes on each associated access chain as the average duration of each associated access chain. The average duration characterizes the time characteristics between adjacent data access operations on the corresponding associated access chain.

[0093] In the fourth step, the normalized value of the ratio between the access characteristic factor of each associated access chain and the corresponding average time length is used as the implicit stability indicator between the nodes corresponding to the business data of each dimension contained in each associated access chain.

[0094] The larger the value of the access characteristic factor of each associated access chain, the higher the access continuity between business data of different dimensions on the corresponding associated access chain. At the same time, the smaller the value of the average time length of the associated access chain, the shorter the time of adjacent data access operations. This further indicates that the probability of simultaneous occurrence of business data accessed on the associated access chain is greater. At this time, the degree to which the user access operation corresponding to the associated access chain conforms to the conventional characteristics is greater. Therefore, the stability characteristic of the associated access chain is higher, that is, the value of the implicit stability index is larger.

[0095] It should be noted that the normalization method can be processed using the maximum-minimum normalization method. The implicit stability index represents the stability of the association between each two nodes in each implicit link relationship. It should be understood that the implicit stability index values ​​between each node in the implicit link relationship on the same association access chain are equal.

[0096] Step S303: obtaining the dynamic risk values ​​between the nodes in the data association structure diagram according to the implicit stability index and the explicit stability index between different nodes on each access data chain.

[0097] In a user's short-term access data, the stronger the stability of the access data chain, the more compliant the user's operation is, that is, the more it conforms to normal business processes, and the lower the risk transfer value of the access data; finally, through the user's access data chain, the risk transfer and accumulation characteristics are measured to determine the dynamic risk value of the user's corresponding access data.

[0098] In the first step, the implicit stability indicators between the nodes corresponding to the explicit link relationships in the implicit link relationships in the data association structure graph are updated by using the explicit stability indicators between the nodes in the explicit link relationships.

[0099] Among them, the updating method means that if there is a display connection relationship in the implicit link relationship in the data association structure diagram, the display stability index corresponding to the display association between each two nodes in the implicit link relationship is assigned to the implicit stability index between the corresponding two nodes, that is, the value of the implicit stability index at this time is the first preset value, and the value in this embodiment is 1, indicating that the value of the implicit stability index at this time is the maximum value, reflecting that the stability characteristics between the corresponding nodes at this time are the greatest.

[0100] It should be understandable that in the data association graph structure, the stability of the explicit link relationship between nodes is greater than the implicit link relationship between nodes. When both exist between two nodes at the same time, it can be seen that the explicit link relationship with the greatest stability dominates, indicating a higher stability association relationship.

[0101] In the second step, the implicit stability indicators between nodes in each user's access data chain are negatively normalized to obtain the dynamic risk value between nodes in each user's access data chain.

[0102] Specifically, in this embodiment, since the implicit stability index value is a normalized value, it can be directly used The negative correlation normalization is performed in the form of represents the implicit stability index between nodes in the user's x-th access data chain, then the dynamic risk value between nodes in the user's x-th access data chain is It can be expressed as .

[0103] It should be understood that the dynamic risk values ​​corresponding to the implicit links between all nodes in the same access data chain are equal. The dynamic risk value measurement process between nodes in the access data chain integrates the inherent sensitivity of the data with the dynamic impact of access behavior, achieving a dual risk assessment that combines business rules with user behavior. It accurately identifies normal business processes and abnormal access patterns, providing differentiated encryption strategies for highly sensitive data such as investor information and cash flow, balancing security protection and business efficiency.

[0104] Step S400 , performing hierarchical access encryption on the multi-dimensional business data in the venture capital enterprise database according to the dynamic risk value of the node of the dimensional business data corresponding to the user access operation of the venture capital enterprise.

[0105] In a venture capital firm database, each dimension of business data corresponds to a different level of security. The security level of business data is an inherent security attribute of each dimension of business data in the venture capital firm database. For example, in a venture capital firm, the security of business data in dimensions such as due diligence, cash flow, and financial data is relatively high. In other words, the loss of business data in these dimensions would cause significant losses or harm to the company. For example, in a venture capital firm, the security of business data in dimensions such as publicly available investment institution names and project names is relatively low. Therefore, in the process of hierarchical encryption of access data, the inherent security attributes of business data can also be considered. Combining the static and dynamic risks of multi-dimensional business data for risk assessment can more accurately reflect the risk level of the access operation process, making the hierarchical security encryption more effective.

[0106] As a specific example, Figure 6 As shown, the specific method of performing hierarchical access encryption on multi-dimensional business data can be implemented by steps S401 to S403.

[0107] Step S401 : Based on the security attributes of the business data in each dimension, a preset static risk value of the business data in each dimension is obtained.

[0108] In this embodiment, the business data for each dimension is scored by the company's staff, and the score is set as the static risk value of the business data for that dimension. It should be noted that in this embodiment, the static risk value ranges from [0, 1] to maintain a consistent range between the static risk value and the dynamic risk value. It should be understood that the larger the static risk value for each dimension of business data, the higher the security attribute value of the business data in that dimension, as certified by professionals, and therefore the higher the security level of the business data in that dimension.

[0109] It's important to further clarify that, for example, business data involving identity verification, capital flow, and core decision-making processes has a higher static risk value, meaning that any issues with this core data could have a significant impact on the business. However, for publicly available, basic data without sensitive information, the static risk value is lower, meaning the risk of losing publicly available data is lower.

[0110] Step S402 : The static risk value and dynamic risk value of the enterprise in each dimension are integrated to obtain the cumulative risk value of the business data in each dimension.

[0111] Specifically, the cumulative risk value of each dimension's business data is calculated by adding the static and dynamic risk values. This cumulative risk value represents the risk assessment results based on the inherent security attributes or risk characteristics of the business data in the corresponding dimension, as well as the degree of dynamic risk generated when accessed.

[0112] Step S403 : performing hierarchical access encryption on the multi-dimensional business data according to the cumulative risk value of the business data of all dimensions accessed by each user in the access log of the venture capital enterprise database.

[0113] Specifically, the cumulative risk values ​​of all dimensions of business data accessed by each user in the access logs of the venture capital enterprise database are normalized to obtain the access risk of each user in the access logs. All users' access risks are classified into risk levels, and the access logs of users corresponding to different risk levels are encrypted separately. The normalization method is well-known and can employ, for example, the maximum-minimum normalization method, which is not limited here.

[0114] In this embodiment, the AES encryption algorithm is used to encrypt the user's access log. Considering the three encryption levels of the AES encryption algorithm, namely AES-128, AES-192 and AES-256, the access risks of all users are divided into three risk levels.

[0115] Specifically, when the value range of the access risk is greater than or equal to 0 and less than 0.3, the user's access risk is a low risk level; when the value of the user's access risk is greater than or equal to 0.3 and less than 0.6, the user's access risk is a medium risk level; when the value of the user's access risk is greater than or equal to 0.6, the user's access risk is a high risk level.

[0116] For each dimension of business data contained in the access data of high-risk users, a high-level encryption method should be used. For each dimension of business data contained in the access data of low-risk users, a low-level encryption method should be used. Based on this, encryption algorithms are selected in ascending order of risk level, and all dimensions of business data for each user's access in the access log are encrypted. Among them, AES-256 has the highest encryption level, and AES-128 has the lowest encryption level. These encryption methods are well-known technologies and will not be further explained here.

[0117] It should be noted that when business data of the same dimension corresponds to different levels of encryption methods, encryption should be performed at the highest encryption level.

[0118] An embodiment of the present invention further provides an information security protection system for multi-dimensional data, which is used to implement the steps of an information security protection method for multi-dimensional data. The information security protection system for multi-dimensional data specifically includes:

[0119] The data acquisition module is used to obtain business data and access logs of each dimension of the venture capital enterprise database;

[0120] The graph structure construction module is used to construct a data association structure graph using the business data of each dimension as a node based on the direct association relationship between business data of different dimensions and the access time sequence and distribution of access objects in the access log;

[0121] The risk analysis module is used to analyze the association stability characteristics between nodes based on the connection relationship between nodes on the access path in the data association structure diagram and the access information of the business data corresponding to the nodes on the access path, and obtain the dynamic risk value of the nodes in the data association structure diagram;

[0122] The data encryption module is used to encrypt access to the multi-dimensional business data of the venture capital enterprise database based on the dynamic risk value of the dimensional business data corresponding to the node.

[0123] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for information security protection of multi-dimensional data, characterized in that: The method comprises the following steps: Obtain business data of each dimension in the venture capital enterprise database and access logs within a preset time period; Based on the direct association between business data of different dimensions, combined with the access time sequence and the distribution of access objects in the access log, a data association structure diagram is constructed with the business data of each dimension as a node; According to the connection relationship between nodes on the access path in the data association structure diagram and the access information of the business data corresponding to the nodes on the access path, the association stability characteristics between the nodes are analyzed to obtain the dynamic risk value of the nodes in the data association structure diagram; Based on the dynamic risk value of the node of the corresponding dimensional business data of the venture capital enterprise's user access operation, hierarchical access encryption is performed on the multi-dimensional business data of the venture capital enterprise database; The method for hierarchical encryption of multi-dimensional business data in the venture capital enterprise database includes: Based on the security attributes of the business data in each dimension, a preset static risk value of the business data in each dimension is obtained; The static risk value and dynamic risk value of the business data in each dimension are combined to obtain the cumulative risk value of the business data in each dimension; Based on the cumulative risk value of all dimensions of business data accessed by each user in the access log of the venture capital enterprise database, hierarchical access encryption is performed on multi-dimensional business data, specifically including: Normalize the cumulative risk values ​​of all dimensions of business data accessed by each user in the access log of the venture capital enterprise database to obtain the access risk of each user in the access log; The access risks of all users are divided into risk levels, and the access logs of users corresponding to different risk levels are encrypted separately.

2. The information security protection method for multi-dimensional data according to claim 1, characterized in that: According to the direct association relationship between business data of different dimensions, combined with the access time sequence and the distribution of access objects in the access log, a data association structure diagram is constructed with the business data of each dimension as a node, specifically including: Based on the business association relationship between the business data of each dimension and the business data of other dimensions, a display link relationship between the business data of different dimensions is constructed; Sort different access objects based on the access sequence of each user in the access log, and build implicit link relationships between business data of different dimensions; Taking the business data of each dimension as a node, a data association structure diagram is constructed based on the explicit link relationship and implicit link relationship.

3. The information security protection method for multi-dimensional data according to claim 2, characterized in that: The process of sorting different access objects according to the access sequence of each user in the access log and building implicit link relationships between business data of different dimensions specifically includes: Obtain all access data of each user in the access log. Each access data includes the access time and access object. The access object is business data of different dimensions. The access data of each user are connected to the access objects in the order of access time to form an access data chain for each user's each access. The association relationship between business data of different dimensions in all access data chains of each user is used as an implicit link relationship between business data of different dimensions.

4. The information security protection method for multi-dimensional data according to claim 3, characterized in that: The method of analyzing the association stability characteristics between the nodes based on the connection relationship between the nodes on the access path and the access information of the business data corresponding to the nodes on the access path to obtain the dynamic risk value between the nodes in the data association structure diagram specifically includes: Determine the display stability index between different nodes according to the display link relationship between different nodes in the data association structure diagram; According to the implicit association relationship between different nodes in the data association structure graph, combined with the access frequency and access time distribution of the corresponding nodes, the implicit stability index between different nodes is obtained; According to the implicit stability index and explicit stability index between different nodes on each access data chain, the dynamic risk value between nodes in the data association structure graph is obtained.

5. The information security protection method for multi-dimensional data according to claim 4, characterized in that: According to the implicit association relationship between different nodes in the data association structure diagram, combined with the access frequency and access time distribution of the corresponding nodes, the implicit stability index between different nodes is obtained, which specifically includes: Build an FP tree based on the business data of each dimension contained in the access data chain of each user's access, and extract each associated access chain from the root node to the leaf node of the FP tree; The access characteristic factor of each associated access chain is obtained by calculating the average value of the ratio between the frequency of each tree node on each associated access chain and the frequency of the adjacent previous tree node. The average time interval between visits of two adjacent tree nodes on each associated access chain is obtained as the average time length; the normalized value of the ratio between the access characteristic factor of each associated access chain and the corresponding average time length is used as the implicit stability indicator between the nodes corresponding to the business data of each dimension contained in each associated access chain.

6. The information security protection method for multi-dimensional data according to claim 5, characterized in that: Determining the display stability index between different nodes according to the display link relationship between different nodes in the data association structure diagram specifically includes: The display stability index between nodes in each display link relationship is set to a first preset value, wherein the first preset value is greater than or equal to all implicit stability indexes.

7. The information security protection method for multi-dimensional data according to claim 5, characterized in that: The method of obtaining the dynamic risk value between nodes in the data association structure diagram according to the implicit stability index and the explicit stability index between different nodes on each access data chain specifically includes: Using the display stability index between the nodes in the display link relationship, the implicit stability index between the nodes corresponding to the display link relationship in the implicit link relationship in the data association structure diagram is updated; The implicit stability indicators between nodes in each user's access data chain are negatively correlated and normalized to obtain the dynamic risk value between nodes in each user's access data chain.

8. An information security protection system for multi-dimensional data, characterized in that: The system is used to implement the steps of a method for information security protection of multi-dimensional data according to any one of claims 1 to 7, wherein the information security protection system for multi-dimensional data specifically includes: The data acquisition module is used to obtain business data and access logs of each dimension of the venture capital enterprise database; The graph structure construction module is used to construct a data association structure graph using the business data of each dimension as a node based on the direct association relationship between business data of different dimensions and the access time sequence and distribution of access objects in the access log; The risk analysis module is used to analyze the association stability characteristics between nodes based on the connection relationship between nodes on the access path in the data association structure diagram and the access information of the business data corresponding to the nodes on the access path, and obtain the dynamic risk value of the nodes in the data association structure diagram; The data encryption module is used to encrypt access to the multi-dimensional business data of the venture capital enterprise database based on the dynamic risk value of the dimensional business data corresponding to the node.

Images