Data processing method and device, equipment and storage medium

Abstract

The invention discloses a data processing method and device, equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: carrying out the classification and grading processing of a source data set, and determining the data type and data privacy level of each piece of data in the source data set; a data index tree is constructed according to the data of the source data set, each piece of data is a node in the data index tree, the node position of each piece of data in the classification index tree and the privacy index tree is determined, and a transmission index is generated according to the node position; combining the classification index tree and the privacy index tree based on transmission indexes to generate a data index map; and when a data retrieval request is received, generating a retrieval value based on the data retrieval request, performing matching according to index values of the retrieval value in the nodes in the data index map to obtain a matching result, determining target data according to the matching result, and outputting the target data as a retrieval result. By means of the mode, the data form the multi-dimensional data network diagram from the two aspects of privacy and type, and the data retrieval efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to data processing methods, devices, equipment and storage media. Background Art

[0002] As the Internet continues to run, a large number of data samples will continue to accumulate, resulting in an increasing amount of stored information in the database and an increasing number of data types. The data contains a large amount of private data with different privacy levels and data types. When retrieving private data from the database, independent data retrieval is usually performed with the primary key as the retrieval target. This retrieval method consumes a lot of waiting time. If the data is in an encrypted state, the retrieval time will increase again. Therefore, the current processing efficiency of data retrieval, especially private data retrieval, is seriously low, with low data processing efficiency.

[0003] The increase of database data will consume a lot of waiting time when extracting or retrieving data. The current processing method is usually,

[0004] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a data processing method, device, equipment and storage medium, aiming to solve the technical problem of low processing efficiency of privacy data retrieval in the prior art.

[0006] To achieve the above purpose, the present application provides a data processing method, which includes:

[0007] Classify and grade the data in the source data set based on the data type, and determine the data type and data privacy level of each data in the source data set;

[0008] Based on the data type and the data privacy level, construct a data index tree according to the data of the source data set, each data is a node in the data index tree, and the data index tree includes a classification index tree and a privacy index tree;

[0009] Determine a first node position and a second node position of each data in the classification index tree and the privacy index tree, respectively, and generate a transmission index according to the first node position and the second node position;

[0010] Combining the classification index tree and the privacy index tree based on the transmission index to generate a data index graph;

[0011] When a data retrieval request is received, a retrieval value is generated based on the data retrieval request, the index value of the node in the data index graph is matched according to the retrieval value to obtain a matching result, the target data is determined according to the matching result, and the target data is output as a retrieval result.

[0012] In one embodiment, the step of constructing a data index tree based on the data type and the data privacy level includes:

[0013] Grouping the data in the source data set according to the data type and the data privacy level to obtain a plurality of grouped data sets;

[0014] Performing hash calculations on the data in the grouped data sets to obtain hash insertion values;

[0015] Mapping the hash insertion value to a corresponding position in the initial bit position to obtain an index value;

[0016] Performing binary tree encoding on the index value based on the data volume of the grouped data set to obtain an index code;

[0017] A data index tree is constructed according to the index code.

[0018] In one embodiment, the step of determining the first node position and the second node position of each data in the classification index tree and the privacy index tree, respectively, and generating a transmission index according to the first node position and the second node position comprises:

[0019] Determine the first node position of each data in the classification index tree;

[0020] Determine the second node position of each data in the privacy index tree;

[0021] Generate a bidirectional node vector with the first node position and the second node position as starting and ending points respectively;

[0022] The bidirectional node vector is encoded to obtain a transmission index.

[0023] In one embodiment, the step of combining the classification index tree and the privacy index tree to generate a data index graph based on the transmission index includes:

[0024] Determine the node position of the data in the classification index tree and the privacy index tree by using the transmission index;

[0025] Obtain an undirected edge according to the node position and the transmission index;

[0026] The nodes in the classification index tree and the privacy index tree are connected by the undirected edges to obtain a data index graph.

[0027] In one embodiment, the step of classifying and grading the data of the source data set based on the data type and determining the data type and data privacy level of each data in the source data set includes:

[0028] Performing data filtering on the source data set, filtering out abnormal data in the source data set, and obtaining an initial source data set;

[0029] Identify the data in the initial source data set and determine the type tag and privacy tag of the data;

[0030] The type tag and the privacy tag are classified to determine the data type and the data privacy level.

[0031] In one embodiment, upon receiving a data retrieval request, generating a retrieval value based on the data retrieval request, matching an index value of a node in the data index graph according to the retrieval value to obtain a matching result, determining target data according to the matching result, and outputting the target data as a retrieval result comprises:

[0032] Upon receiving a data retrieval request, determining retrieval request data according to the data retrieval request;

[0033] Performing data conversion on the search request data to convert the search request data into a search value;

[0034] Traversing from a first search starting point and a second search starting point of the index graph based on the search value, matching the search value with an index value of a node of the index graph, and determining a search direction;

[0035] Determine a traversal direction according to the search direction, and match the index value based on the traversal direction with the search value to obtain a matching result;

[0036] When the matching result is a match to an index value consistent with the search value based on the traversal direction, the node data corresponding to the index value is determined as the target data, and the target data is output as the search result.

[0037] In one embodiment, the step of traversing from a first search starting point and a second search starting point of the index graph based on the search value, matching the search value with the index value of the index graph node, and determining the search direction also includes:

[0038] authenticating the search request and determining a privacy access right for the search request;

[0039] The node data with a privacy level greater than the privacy access permission is hidden, and the index value of the node data in the index map is shielded.

[0040] In addition, to achieve the above-mentioned purpose, the present application also proposes a data processing device, the data processing device comprising:

[0041] A data classification module is used to classify and grade the data in the source data set based on the data type, and determine the data type and data privacy level of each data in the source data set;

[0042] A retrieval tree generation module, used to construct a data index tree according to the data of the source data set based on the data type and the data privacy level, each data is a node in the data index tree, and the data index tree includes a classification index tree and a privacy index tree;

[0043] An index transmission module, used to determine the first node position and the second node position of each data in the classification index tree and the privacy index tree respectively, and generate a transmission index according to the first node position and the second node position;

[0044] An index map generation module, configured to generate a data index map by combining the classification index tree and the privacy index tree based on the transmission index;

[0045] The retrieval output module is used to generate a retrieval value based on the data retrieval request when receiving a data retrieval request, match the index value of the node in the data index graph according to the retrieval value to obtain a matching result, determine the target data according to the matching result, and output the target data as a retrieval result.

[0046] In addition, to achieve the above objectives, the present application also proposes a data processing device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the data processing method as described above.

[0047] In addition, to achieve the above objectives, the present invention also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the data processing method as described above are implemented.

[0048] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the data processing method as described above are implemented.

[0049] The present application provides a data processing method, which classifies and grades the source data set, determines the data type and data privacy level of each data in the source data set; constructs a data index tree based on the data of the source data set, each data is a node in the data index tree, determines the node position of each data in the classification index tree and the privacy index tree respectively, and generates a transmission index based on the node position; combines the classification index tree and the privacy index tree based on the transmission index to generate a data index graph; when receiving a data retrieval request, generates a retrieval value based on the data retrieval request, matches the index value of the node in the data index graph according to the retrieval value, obtains a matching result, determines the target data according to the matching result, and outputs the target data as a retrieval result. In the above manner, the data is constructed into a multidimensional data network graph from the two aspects of privacy and type, thereby improving the efficiency of data retrieval. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0051] In order to more clearly illustrate the embodiments of the present application 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, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0052] Figure 1 This is a flow chart of the first embodiment of the data processing method of the present application;

[0053] Figure 2 A schematic diagram of classification relationships in accordance with an embodiment of the data processing method of the present application;

[0054] Figure 3 A schematic diagram of generating a hash insertion value in accordance with an embodiment of a data processing method of the present application;

[0055] Figure 4 A schematic diagram of index map construction in an embodiment of the data processing method of the present application;

[0056] Figure 5 This is a schematic diagram of the module structure of the data processing device according to an embodiment of the present application;

[0057] Figure 6 Schematic diagram of the device structure of the hardware operating environment involved in the data processing method in the embodiment of the present application.

[0058] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0059] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0060] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0061] The main solution of the embodiment of the present application is: based on the data type and the data privacy level, a data index tree is constructed according to the data of the source data set, each data is a node in the data index tree, and the data index tree includes a classification index tree and a privacy index tree;

[0062] Determine a first node position and a second node position of each data in the classification index tree and the privacy index tree, respectively, and generate a transmission index according to the first node position and the second node position;

[0063] Combining the classification index tree and the privacy index tree based on the transmission index to generate a data index graph;

[0064] When a data retrieval request is received, a retrieval value is generated based on the data retrieval request, the index value of the node in the data index graph is matched according to the retrieval value to obtain a matching result, the target data is determined according to the matching result, and the target data is output as a retrieval result.

[0065] The current retrieval method uses the primary key as the retrieval target for independent data retrieval, which consumes a lot of waiting time. If the data is in an encrypted state, the retrieval time will increase again. Therefore, the current processing efficiency of data retrieval, especially privacy data retrieval, is seriously low, with low data processing efficiency.

[0066] The present application provides a solution that can form data into multi-dimensional data, locate the same data from multiple dimensions, and hide private data accordingly. When performing a request retrieval, the corresponding data information can be determined in an adapted index graph based on the request retrieval, thereby reducing the number of node traversals and improving retrieval efficiency.

[0067] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device, a data processing device, etc. that can realize the above functions, and this embodiment does not specifically limit this. The following takes a data processing device as an example to illustrate this embodiment and the following embodiments.

[0068] The present application embodiment provides a data processing method, referring to Figure 1 , Figure 1This is a flowchart of the first embodiment of the data processing method of the present application.

[0069] In this embodiment, the data processing method includes steps S10 to S50:

[0070] Step S10, classifying and grading the data in the source data set based on the data type, and determining the data type and data privacy level of each data in the source data set.

[0071] It should be noted that the source data set refers to the data collected through various channels, including data obtained from sensor devices, network acquisition, and user input. Various data are stored in independent or shared databases, and the collection containing this data is the source data set.

[0072] It can be understood that data types include various types of data such as text data, image data, audio data, etc. Similarly, the data privacy level is an important indicator used to describe the importance of data. For example, the user's ID number can be considered to be an important privacy level, while information that can be directly obtained on the Internet, such as the explanation of a certain word, belongs to the general privacy level.

[0073] In a specific implementation, when the source data set is obtained, each data in the source data set can be classified and graded. Before classification and grading, the deep learning model can be used to add data type tags and privacy level tags to each data in the source data set. The privacy level tag needs to be determined according to the data holder's degree of recognition of the data, which is related to each usage scenario, so this embodiment does not limit this. After labeling, the data can be classified through filters or classifiers. The classification relationship refers to Figure 2 . In the classification process, we can first determine two major categories, namely the data type classification set and the data privacy level classification set. Assuming that the current data type includes text data type, audio data type, audio data type, etc., the privacy level is divided into general, secret, confidential, top secret and other types. Among them, in the privacy level, general < secret < confidential < top secret. In other words, for a data Data, it will only appear once in the data type classification set and the privacy level classification set. For example, if the data Data is image-type secret data, then its classification result can be referred to Figure 1 Based on the same method, all data in the source data set can be classified to determine the data type and data privacy level of each data.

[0074] In a feasible implementation manner, the step of classifying and grading the data of the source data set based on the data type and determining the data type and data privacy level of each data in the source data set includes:

[0075] Performing data filtering on the source data set, filtering out abnormal data in the source data set, and obtaining an initial source data set;

[0076] Identify the data in the initial source data set and determine the type tag and privacy tag of the data;

[0077] The type tag and the privacy tag are classified to determine the data type and the data privacy level.

[0078] In the specific implementation, the source data set D is filtered to filter out abnormal data in the source data set, such as duplicate data, missing data, invalid data, etc., to obtain the initial source data set. The initial filtered set is the data set after the source data is filtered, which is represented by D init Assume that the type labeling function is T and the privacy labeling function is P. Then for the initial source data set D init Each data item d in i , whose type tag is t i and privacy markp i It can be expressed as:

[0079] t i =T(d i ) and p i =P(d i )

[0080] When performing classification processing, classification processing can be performed based on the classification function C. For the set of all type tags and privacy tags {t i , p i It can be classified into different data types and data privacy levels, and the classification result G(D) is obtained, which is expressed as:

[0081] G(D)=C{P(d i ),T(d i )|d i ∈F(D)}

[0082] Step S20, based on the data type and the data privacy level, construct a data index tree according to the data of the source data set, each data is a node in the data index tree, and the data index tree includes a classification index tree and a privacy index tree.

[0083] It should be noted that the data index tree is a data structure used to efficiently manage and retrieve data. In the specific selection, the B+ tree can be selected. In the B+ tree, all leaf nodes are connected by a link pointer, which makes range queries more efficient. The classification index tree and privacy index tree are index trees created based on data types and data privacy levels respectively.

[0084] It is understandable that when creating a classification index tree and a privacy index tree, it is first necessary to clarify the data type (such as numeric type, text type, etc.) and the corresponding privacy level (such as general, confidential, secret, etc.) of each record in the data set. This information will determine how to organize and index the data. When creating a classification index tree, the data category is used as the main classification method for classification. First, a root node can be created to represent the entire data set, and sub-root nodes can be created under the root node according to the data type. The sub-root nodes are all classified data sets. That is to say, in the image classification data set, they can all point to the sub-root nodes of the image data. Then, starting from the root node, the data set is recursively divided into smaller parts according to the selected classification criteria, and child nodes are created for each part. Considering the depth and width of the tree, the query efficiency is maximized, and the nodes in some positions can be merged to keep the tree balanced. The specific balance strategy can be determined by the amount of data in the leaf nodes to keep the number of leaf nodes close.

[0085] When building a privacy index tree, formulate corresponding privacy policies according to the privacy level of the data, such as which data can only be accessed by specific roles, or which data needs to be encrypted and stored. Apply access control lists (ACLs) on each node to ensure that only authorized users can access specific data. For sensitive data, desensitized information can be stored in the index tree instead of the original data. For example, the user's hash value can be stored instead of the real name. For highly sensitive data, encryption technology can be used to store it in the index tree to ensure that even if the data is leaked, it will not be easily interpreted. In this way, two index trees with the same database but different classification methods can be obtained.

[0086] In a feasible implementation manner, the step of constructing a data index tree based on the data type and the data privacy level includes:

[0087] Grouping the data in the source data set according to the data type and the data privacy level to obtain a plurality of grouped data sets;

[0088] Performing hash calculations on the data in the grouped data sets to obtain hash insertion values;

[0089] Mapping the hash insertion value to a corresponding position in the initial bit position to obtain an index value;

[0090] Performing binary tree encoding on the index value based on the data volume of the grouped data set to obtain an index code;

[0091] A data index tree is constructed according to the index code.

[0092] It should be noted that the hash insertion value is the hash value obtained by hashing the data. When the input data is different, the obtained hash insertion value is also different, so the hash insertion value can be regarded as another form of data. The index code refers to the position in the index tree. The search path in the index tree can be determined based on the index code. When generating the index code, it can be determined based on the amount of data in the data set corresponding to the current node.

[0093] It can be understood that, first, the data in the source data set is grouped according to the data type and privacy level to obtain multiple grouped data sets. The grouping criteria are defined according to the data type (such as numeric type, text type, etc.) and the privacy level (such as general, secret, confidential, etc.), the source data set is traversed, and the data is classified according to the defined grouping criteria to form multiple grouped data sets. The data in the grouped data sets are hashed to obtain hash insertion values. In the data insertion stage, the hash function is used to map the elements to the initial bit positions, and the corresponding bit positions are set to 1. In all stages, all elements are mapped using the same hash function. Refer to Figure 3 , Figure 3 Generate a schematic diagram for hash insertion value. Given a set of data sets A = {a1, a2, ..., a n}, and use k hash functions {h1,h2,...,h k} Map n elements to bits of length m. In the initial state, all elements in the bit vector are set to 0. When inserting an element, set the k vector bits corresponding to the hash function calculation to 1. This obtains the index value after the hash insertion value is mapped. Then determine the order of each group according to the data volume of the grouped data set, and perform binary tree encoding with the order information of the data volume and the index value to obtain the index code. Create the root node of the index tree and recursively construct child nodes according to the index code. Each child node represents the index value of a grouped data set.

[0094] Step S30, determining the first node position and the second node position of each data in the classification index tree and the privacy index tree respectively, and generating a transmission index according to the first node position and the second node position.

[0095] It should be noted that the first node position and the second node position refer to the node positions of the same data in the classification index tree and the privacy index tree, respectively. The first node position refers to the node position of the data in the classification index tree, and the second node position refers to the node position of the data in the privacy index tree. Transmission index breaks the barrier between the two trees and can realize the index from the classification index tree to the privacy index tree or the privacy index tree to the classification index tree.

[0096] The step of determining the first node position and the second node position of each data in the classification index tree and the privacy index tree respectively, and generating a transmission index according to the first node position and the second node position comprises:

[0097] Determine the first node position of each data in the classification index tree;

[0098] Determine the second node position of each data in the privacy index tree;

[0099] Generate a bidirectional node vector with the first node position and the second node position as starting and ending points respectively;

[0100] The bidirectional node vector is encoded to obtain a transmission index.

[0101] It should be noted that a bidirectional node vector refers to a vector that can point from the classification index tree to the privacy index tree, and also from the privacy index tree to the classification index tree, thereby achieving dimensional spanning of the two index trees.

[0102] In a specific implementation, after the classification index tree and the privacy index tree have been constructed, a transmission index can be created between the two index trees. When searching for data, it is possible to traverse from one index tree to another index tree. When traversing, 0 and 1 can be represented as starting from the root node, traversing to the left and right to reach the target node, respectively. For example, 010 means triggering from the root node, and traversing to the left, right, and left in sequence to reach the target node. When generating a transmission index, this operation can be performed on any data to determine the position of the data in the classification index tree and the privacy index tree. The node position can be determined by the index value, and the result is as follows: 分类 (D i )=p1p2p3...p n , where p j represents the jth direction on the path (0 means left, 1 means right). Similarly, P can be obtained based on the same operation in the privacy index tree. 隐私 (D i )=q1q2q3...q m , where q k represents the kth direction on the path (0 means left, 1 means right). Then the transmission index is generated according to the positions of the two nodes. When determining the transmission index, the transmission index can be obtained based on the hash combination path, specifically:

[0103] T(D i )=H(P 分类 (D i )⊕P 隐私 (D i ))

[0104] Here, H is a hash function, such as SHA-256.

[0105] Through the above method, we can determine the node position of each data item in the classification index tree and the privacy index tree, and generate a transmission index. The transmission index can be used to efficiently query and manage data while protecting the privacy of the data.

[0106] Step S40: Combining the classification index tree and the privacy index tree based on the transmission index to generate a data index graph.

[0107] It should be noted that a data index diagram is a graphical representation method used to show how data is organized in an index structure. Through a data index diagram, you can intuitively understand how data is indexed, stored, and retrieved.

[0108] In a feasible implementation manner, the step of combining the classification index tree and the privacy index tree to generate a data index graph based on the transmission index includes:

[0109] Determine the node position of the data in the classification index tree and the privacy index tree by using the transmission index;

[0110] Obtain an undirected edge according to the node position and the transmission index;

[0111] The nodes in the classification index tree and the privacy index tree are connected by the undirected edges to obtain a data index graph.

[0112] In a specific implementation, when determining the node positions of data in the classification index tree and the privacy index tree according to the transmission index, the positions of the data in the two index trees can be determined according to the endpoints of the transmission index. When the node positions of the data in the two index trees are obtained, the undirected edges can be determined according to these positions and the transmission index. The undirected edge represents the connection between two nodes and can be expressed by the following formula:

[0113] E(I c ,I p )={I c ,I p}

[0114] Where E represents the set of edges, (I c ,I p ) represents a node position pair in the classification index tree and the privacy index tree.

[0115] Finally, the nodes in the classification index tree and the privacy index tree are connected using the determined undirected edges to construct the data index graph. Since the classification index tree and the privacy classification tree already exist, the classification index tree and the privacy classification tree can be used as the main body of the index graph, and the undirected edges are used as bridges to connect the classification index tree and the privacy classification tree. Figure 4 , Figure 4 A schematic diagram of the index graph is constructed. The dashed lines represent undirected edges, which are used to connect the classification index tree and the privacy classification tree to form the index graph.

[0116] Step S50, when a data retrieval request is received, a retrieval value is generated based on the data retrieval request, the index value of the node in the data index graph is matched according to the retrieval value to obtain a matching result, the target data is determined according to the matching result, and the target data is output as a retrieval result.

[0117] It should be noted that the data retrieval request is a retrieval operation performed by the user, which includes the user's identity authority information and the target retrieval data content.

[0118] In a specific implementation, when a data retrieval request is received, retrieval request data is determined according to the data retrieval request; the retrieval request data is converted into a retrieval value; based on the retrieval value, the index graph is traversed from a first retrieval starting point and a second retrieval starting point, the retrieval value is matched with an index value of a node of the index graph, and a retrieval direction is determined; a traversal direction is determined according to the retrieval direction, and the index value based on the traversal direction is matched with the retrieval value to obtain a matching result; when the matching result is an index value consistent with the retrieval value based on the traversal direction, the node data corresponding to the index value is determined as the target data, and the target data is output as the retrieval result. Specifically, when a data retrieval request is received, a search value can be generated for the request content of the data retrieval request. Specifically, content analysis is performed based on the request content to determine the data type and data classification of the requested content. For example, when the request content is to retrieve the ID card number, it can be determined based on the request content that the data type of the request content is a numeric type or text type of data, and the privacy level is a confidential privacy level. Then, a search header can be generated based on the data type and privacy level determined by the request content, and a search tail can be generated for the content contained in the request content itself, and the search header and the search tail are combined to obtain a search value. Then, according to the search value, the data index graph is traversed, and the search value is matched with the index value of the node to obtain a matching result. There are two matching results, one is a successful match, and it can choose whether to display or hide data operations according to the user's privacy access rights, and the other is a match failure, and the matching information of the failed retrieval is output. When the matching result is to determine the target data, the target data is output as a search result.

[0119] In a feasible implementation manner, the step of traversing from the first retrieval starting point and the second retrieval starting point of the index graph based on the retrieval value, matching the retrieval value with the index value of the index graph node, and determining the retrieval direction also includes:

[0120] authenticating the search request and determining a privacy access right for the search request;

[0121] The node data with a privacy level greater than the privacy access permission is hidden, and the index value of the node data in the index map is shielded.

[0122] It should be noted that the privacy access permission refers to the data with the maximum privacy level that can be queried. When the privacy access permission is less than the privacy level of the data, it cannot be queried.

[0123] In a specific implementation, when making a retrieval request, the identity can be verified, the privacy access rights of the requester of the retrieval request can be determined, the node data with a privacy level greater than the privacy access rights can be hidden, and the index value of the node data in the index map can be shielded. At this time, it can be regarded as forming an index map dedicated to the current user, and the data in the index map is all the visible content of the user. Therefore, when the user makes a request, the search can be performed only within a limited range, and there will be no side-stepping of privacy data. On the other hand, it can also reduce a certain amount of data and improve the retrieval time.

[0124] This embodiment provides a data processing method, which classifies and grades the source data set, determines the data type and data privacy level of each data in the source data set; constructs a data index tree based on the data of the source data set, each data is a node in the data index tree, determines the node position of each data in the classification index tree and the privacy index tree respectively, and generates a transmission index based on the node position; combines the classification index tree and the privacy index tree based on the transmission index to generate a data index graph; when receiving a data retrieval request, generates a retrieval value based on the data retrieval request, matches the index value of the node in the data index graph according to the retrieval value, obtains a matching result, determines the target data according to the matching result, and outputs the target data as a retrieval result. In the above manner, the data is constructed into a multidimensional data network graph from the two aspects of privacy and type, thereby improving the efficiency of data retrieval.

[0125] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the data processing method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0126] This application also provides a data processing device, please refer to Figure 5 , the data processing device comprises:

[0127] A data classification module 10, used to classify and grade the data in the source data set based on the data type, and determine the data type and data privacy level of each data in the source data set;

[0128] A search tree generation module 20, configured to construct a data index tree according to the data of the source data set based on the data type and the data privacy level, wherein each data is a node in the data index tree, and the data index tree includes a classification index tree and a privacy index tree;

[0129] An index transmission module 30, used to determine the first node position and the second node position of each data in the classification index tree and the privacy index tree respectively, and generate a transmission index according to the first node position and the second node position;

[0130] An index map generation module 40, configured to generate a data index map by combining the classification index tree and the privacy index tree based on the transmission index;

[0131] The retrieval output module 50 is used to generate a retrieval value based on the data retrieval request when receiving a data retrieval request, match the index value of the node in the data index graph according to the retrieval value to obtain a matching result, determine the target data according to the matching result, and output the target data as a retrieval result.

[0132] In a feasible implementation, the retrieval tree generation module 20 is also used to group the data in the source data set according to the data type and the data privacy level to obtain multiple grouped data sets; perform hash calculations on the data in the grouped data sets to obtain hash insertion values; map the hash insertion values ​​to corresponding positions in the initial bits to obtain index values; perform binary tree encoding on the index values ​​based on the data volume of the grouped data sets to obtain index codes; and construct a data index tree based on the index codes.

[0133] In a feasible implementation, the index transmission module 30 is also used to determine the first node position of each data in the classification index tree; determine the second node position of each data in the privacy index tree; generate a bidirectional node vector with the first node position and the second node position as the starting and ending points respectively; encode the bidirectional node vector to obtain a transmission index.

[0134] In a feasible implementation, the index map generation module 40 is also used to determine the node position of the data in the classification index tree and the privacy index tree by using the transmission index; obtain an undirected edge based on the node position and the transmission index; and connect the nodes in the classification index tree and the privacy index tree with the undirected edge to obtain a data index map.

[0135] In a feasible implementation manner, the data classification module 10 is also used to filter the source data set, filter out abnormal data in the source data set, and obtain an initial source data set; identify the data in the initial source data set, and determine the type tag and privacy tag of the data; classify the type tag and the privacy tag to determine the data type and data privacy level.

[0136] In a feasible implementation, the retrieval output module 50 is also used to, upon receiving a data retrieval request, determine retrieval request data according to the data retrieval request; perform data conversion on the retrieval request data to convert the retrieval request data into a retrieval value; traverse from the first retrieval starting point and the second retrieval starting point of the index graph based on the retrieval value, match the retrieval value with the index value of the index graph node, and determine the retrieval direction; determine the traversal direction according to the retrieval direction, match the index value based on the traversal direction with the retrieval value, and obtain a matching result; when the matching result is an index value consistent with the retrieval value based on the traversal direction, determine the node data corresponding to the index value as the target data, and output the target data as the retrieval result.

[0137] In a feasible implementation, the retrieval output module 50 is also used to authenticate the retrieval request and determine the privacy access rights of the retrieval request; hide the node data whose privacy level is greater than the privacy access rights, and mask the index value of the node data in the index graph.

[0138] The data processing device provided by the present application adopts the data processing method in the above embodiment, which can solve the technical problem of low processing efficiency of privacy data retrieval. Compared with the prior art, the beneficial effects of the data processing device provided by the present application are the same as the beneficial effects of the data processing method provided by the above embodiment, and other technical features in the data processing device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0139] The present application provides a data processing device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the data processing method in the above-mentioned embodiment 1.

[0140] Reference below Figure 6, which shows a schematic diagram of the structure of a data processing device suitable for implementing an embodiment of the present application. The data processing device in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The data processing device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0141] like Figure 6 As shown, the data processing device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the data processing device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the data processing device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a data processing device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0142] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0143] The data processing device provided by the present application adopts the data processing method in the above embodiment to solve the technical problem of data processing. Compared with the prior art, the beneficial effects of the data processing device provided by the present application are the same as the beneficial effects of the data processing method provided by the above embodiment, and other technical features in the data processing device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0144] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0145] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0146] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, wherein the computer-readable program instructions are used to execute the data processing method in the above-mentioned embodiment.

[0147] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0148] The computer-readable storage medium may be included in a data processing device, or may exist independently without being incorporated into a data processing device.

[0149] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by a data processing device, the data processing device: classifies and grades the data of the source data set based on the data type, and determines the data type and data privacy level of each data in the source data set; constructs a data index tree according to the data of the source data set based on the data type and the data privacy level, each data is a node in the data index tree, and the data index tree includes a classification index tree and a privacy index tree; determines the first node position and the second node position of each data in the classification index tree and the privacy index tree respectively, and generates a transmission index according to the first node position and the second node position; combines the classification index tree and the privacy index tree to generate a data index graph based on the transmission index; when receiving a data retrieval request, generates a retrieval value based on the data retrieval request, matches the index value of the node in the data index graph according to the retrieval value, obtains a matching result, determines the target data according to the matching result, and outputs the target data as the retrieval result.

[0150] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0151] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0152] The modules involved in the embodiments described in the present application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0153] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned data processing method, and can solve the technical problems of data processing. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the data processing method provided in the above-mentioned embodiment, and will not be described in detail here.

[0154] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned data processing method when executed by a processor.

[0155] The computer program product provided by the present application can solve the technical problem of data processing. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the data processing method provided by the above embodiment, which will not be repeated here.

[0156] The above are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A data processing method, characterized in that: The data processing method comprises: Classify and grade the data in the source data set based on the data type, and determine the data type and data privacy level of each data in the source data set; Based on the data type and the data privacy level, construct a data index tree according to the data of the source data set, each data is a node in the data index tree, and the data index tree includes a classification index tree and a privacy index tree; Determine a first node position and a second node position of each data in the classification index tree and the privacy index tree, respectively, and generate a transmission index according to the first node position and the second node position; Combining the classification index tree and the privacy index tree based on the transmission index to generate a data index graph; When a data retrieval request is received, a retrieval value is generated based on the data retrieval request, the index value of the node in the data index graph is matched according to the retrieval value to obtain a matching result, the target data is determined according to the matching result, and the target data is output as a retrieval result.

2. The method according to claim 1, characterized in that The step of constructing a data index tree based on the data type and the data privacy level comprises: Grouping the data in the source data set according to the data type and the data privacy level to obtain a plurality of grouped data sets; Performing hash calculations on the data in the grouped data sets to obtain hash insertion values; Mapping the hash insertion value to a corresponding position in the initial bit position to obtain an index value; Performing binary tree encoding on the index value based on the data volume of the grouped data set to obtain an index code; A data index tree is constructed according to the index code.

3. The method according to claim 1, characterized in that The step of determining the first node position and the second node position of each data in the classification index tree and the privacy index tree respectively, and generating a transmission index according to the first node position and the second node position comprises: Determine the first node position of each data in the classification index tree; Determine the second node position of each data in the privacy index tree; Generate a bidirectional node vector with the first node position and the second node position as starting and ending points respectively; The bidirectional node vector is encoded to obtain a transmission index.

4. The method according to claim 1, characterized in that The step of combining the classification index tree and the privacy index tree to generate a data index graph based on the transmission index comprises: Determine the node position of the data in the classification index tree and the privacy index tree by using the transmission index; Obtain an undirected edge according to the node position and the transmission index; The nodes in the classification index tree and the privacy index tree are connected by the undirected edges to obtain a data index graph.

5. The method according to claim 1, characterized in that The step of classifying and grading the data of the source data set based on the data type and determining the data type and data privacy level of each data in the source data set comprises: Performing data filtering on the source data set, filtering out abnormal data in the source data set, and obtaining an initial source data set; Identify the data in the initial source data set and determine the type tag and privacy tag of the data; The type tag and the privacy tag are classified to determine the data type and the data privacy level.

6. The method according to claim 1, characterized in that The step of generating a search value based on the data search request upon receiving the data search request, matching the index value of the node in the data index graph according to the search value to obtain a matching result, determining the target data according to the matching result, and outputting the target data as the search result comprises: Upon receiving a data retrieval request, determining retrieval request data according to the data retrieval request; Performing data conversion on the search request data to convert the search request data into a search value; Traversing from a first search starting point and a second search starting point of the index graph based on the search value, matching the search value with an index value of a node of the index graph, and determining a search direction; Determine a traversal direction according to the search direction, and match the index value based on the traversal direction with the search value to obtain a matching result; When the matching result is a match to an index value consistent with the search value based on the traversal direction, the node data corresponding to the index value is determined as the target data, and the target data is output as the search result.

7. The method according to claim 6, characterized in that The step of traversing from the first search starting point and the second search starting point of the index graph based on the search value, matching the search value with the index value of the index graph node, and determining the search direction also includes: authenticating the search request and determining a privacy access right for the search request; The node data with a privacy level greater than the privacy access permission is hidden, and the index value of the node data in the index map is shielded.

8. A data processing device, characterized in that: The data processing device comprises: A data classification module is used to classify and grade the data in the source data set based on the data type, and determine the data type and data privacy level of each data in the source data set; A retrieval tree generation module, used to construct a data index tree according to the data of the source data set based on the data type and the data privacy level, each data is a node in the data index tree, and the data index tree includes a classification index tree and a privacy index tree; An index transmission module, used to determine the first node position and the second node position of each data in the classification index tree and the privacy index tree respectively, and generate a transmission index according to the first node position and the second node position; An index map generation module, configured to generate a data index map by combining the classification index tree and the privacy index tree based on the transmission index; The retrieval output module is used to generate a retrieval value based on the data retrieval request when receiving a data retrieval request, match the index value of the node in the data index graph according to the retrieval value to obtain a matching result, determine the target data according to the matching result, and output the target data as a retrieval result.

9. A data processing device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the data processing method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the data processing method according to any one of claims 1 to 7 are implemented.

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