Data processing method and device, equipment and storage medium

Abstract

This application discloses a data processing method, apparatus, device, and storage medium, relating to the field of data processing technology. The method includes: classifying and grading a source dataset to determine the data type and privacy level of each data point; constructing a data index tree based on the source dataset, with each data point as a node in the data index tree, determining the node position of each data point in both the classification index tree and the privacy index tree, and generating a transmission index based on the node positions; combining the classification index tree and the privacy index tree based on the transmission index to generate a data index graph; upon receiving a data retrieval request, generating a retrieval value based on the request, matching the retrieval value with the index value of a node in the data index graph to obtain a matching result, determining the target data based on the matching result, and outputting the target data as the retrieval result. Through this method, data is constructed into a multi-dimensional data network graph from both privacy and type perspectives, improving data retrieval efficiency.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to data processing methods, apparatus, devices and storage media. Background Technology

[0002] As the internet continues to operate, it accumulates massive amounts of data, leading to a continuous increase in the amount of information stored in databases and the variety of data types. This data includes a large amount of privacy-sensitive information with varying levels of privacy and different data types. Retrieving privacy-sensitive data from a database typically involves independent data retrieval based on the primary key, a method that consumes significant waiting time. If the data is encrypted, the retrieval time increases further. Therefore, current data retrieval, especially privacy-sensitive data retrieval, is severely inefficient, exhibiting low overall data processing efficiency.

[0003] Increasing the amount of data in the database can lead to significant waiting time during data extraction or retrieval. Current methods typically involve...

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

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

[0006] To achieve the above objectives, this application provides a data processing method, the method comprising:

[0007] The data in the source dataset is classified and graded based on data type to determine the data type and data privacy level of each data in the source dataset.

[0008] Based on the data type and the data privacy level, a data index tree is constructed according to the data in the source dataset, where 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 the first node position and the second node position of each data point in the classification index tree and the privacy index tree, and generate a transmission index based on the first node position and the second node position;

[0010] Based on the transmission index, the classification index tree and the privacy index tree are combined to generate a data index graph;

[0011] Upon receiving a data retrieval request, a retrieval value is generated based on the data retrieval request. The retrieval value is then matched against the index value of a node in the data index graph to obtain a matching result. The target data is determined based on the matching result and output as the 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] The data in the source dataset will be grouped according to the data type and the data privacy level to obtain multiple grouped datasets;

[0014] The hash insertion values ​​are obtained by performing hash calculations on the data in the grouped datasets respectively;

[0015] The hash insertion value is mapped to the corresponding position in the initial bit to obtain the index value;

[0016] The index value is encoded using a binary tree based on the data volume of the grouped dataset to obtain the index code.

[0017] Construct a data index tree based on the index encoding.

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

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

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

[0021] Generate bidirectional node vectors with the first node position and the second node position as the start and end points, respectively.

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

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

[0024] The transmission index determines the node position of the data in the classification index tree and the privacy index tree;

[0025] Undirected edges are obtained based on the node positions 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 classifying the source dataset based on data type to determine the data type and data privacy level of each piece of data in the source dataset includes:

[0028] The source dataset is filtered to remove abnormal data, thus obtaining the initial source dataset.

[0029] The data in the initial source dataset is identified to determine the data type label and privacy label;

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

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

[0032] Upon receiving a data retrieval request, the data to be retrieved is determined based on the data retrieval request.

[0033] The search request data is transformed into search values.

[0034] Based on the search value, the search is traversed from the first search starting point and the second search starting point of the index graph, and the search value is matched with the index value of the node in the index graph to determine the search direction;

[0035] The traversal direction is determined based on the retrieval direction, and the index value based on the traversal direction is matched with the retrieval value to obtain the matching result;

[0036] When the matching result is an index value that matches 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 the index graph from a first retrieval starting point and a second retrieval starting point based on the retrieval value, matching the retrieval value with the index values ​​of the nodes in the index graph, and determining the retrieval direction further includes:

[0038] The search request is authenticated to determine the privacy access permissions of the search request;

[0039] Node data with a privacy level greater than the privacy access permission level is hidden, and the index value of the node data in the index graph is blocked.

[0040] Furthermore, to achieve the above objectives, this application also proposes a data processing apparatus, which includes:

[0041] The data classification module is used to classify and classify the data in the source dataset based on the data type, and to determine the data type and data privacy level of each data in the source dataset.

[0042] The retrieval tree generation module is used to construct a data index tree based on the data type and the data privacy level of the source dataset, 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.

[0043] The index transmission module is 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 based on the first node position and the second node position;

[0044] An index graph generation module is used to combine the classification index tree and the privacy index tree based on the transmission index to generate a data index graph;

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

[0046] In addition, to achieve the above objectives, this application also proposes a data processing apparatus, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the data processing method 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, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the data processing method described above.

[0048] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the data processing method described above.

[0049] This application provides a data processing method. It classifies and grades a source dataset to determine the data type and privacy level of each data point. A data index tree is constructed based on the data in the source dataset, with each data point being a node in the data index tree. The node position of each data point in both the classification index tree and the privacy index tree is determined, and a transmission index is generated based on the node positions. The classification index tree and the privacy index tree are combined based on the transmission index to generate a data index graph. Upon receiving a data retrieval request, a retrieval value is generated based on the request. The retrieval value is matched against the index values ​​of the nodes in the data index graph to obtain matching results. The target data is then determined based on the matching results and output as the retrieval result. Through this method, data is constructed into a multi-dimensional data network graph from both privacy and type perspectives, improving data retrieval efficiency. Attached Figure Description

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

[0051] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a flowchart illustrating an embodiment of the data processing method of this application;

[0053] Figure 2 This is a schematic diagram of the classification relationship according to an embodiment of the data processing method of this application;

[0054] Figure 3 This is a schematic diagram illustrating the generation of hash insertion values ​​according to an embodiment of the data processing method of this application;

[0055] Figure 4 This is a schematic diagram of the index graph construction according to an embodiment of the data processing method of this application;

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

[0057] Figure 6 This is a schematic diagram of the device structure of the hardware operating environment involved in the data processing method in the embodiments of this application.

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

[0059] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0060] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0061] The main solution of this application embodiment is: based on the data type and the data privacy level, a data index tree is constructed according to the data of the source dataset, where 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 the first node position and the second node position of each data point in the classification index tree and the privacy index tree, and generate a transmission index based on the first node position and the second node position;

[0063] Based on the transmission index, the classification index tree and the privacy index tree are combined to generate a data index graph;

[0064] Upon receiving a data retrieval request, a retrieval value is generated based on the data retrieval request. The retrieval value is then matched against the index value of a node in the data index graph to obtain a matching result. The target data is determined based on the matching result and output as the retrieval result.

[0065] Current retrieval methods use primary keys as the search target for independent data searches, which consumes a significant amount of waiting time. If the data is encrypted, the retrieval time increases further. Therefore, current data retrieval, especially for private data, is severely inefficient, exhibiting low overall data processing efficiency.

[0066] This application provides a solution that can form multidimensional data, locate the same data from multiple dimensions, and hide privacy data accordingly. When performing a retrieval request, it can determine the corresponding data information in an appropriate index graph based on the retrieval request, reducing the number of node traversals and improving retrieval efficiency.

[0067] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or data processing device capable of performing the above functions. This embodiment does not specifically limit it in this regard. The following uses a data processing device as an example to describe this embodiment and the following embodiments.

[0068] This application provides a data processing method, which is described in the embodiments below. Figure 1 , Figure 1This is a flowchart illustrating the first embodiment of the data processing method of this application.

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

[0070] Step S10: Classify and grade the data in the source dataset based on data type to determine the data type and data privacy level of each data in the source dataset.

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

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

[0073] In practical implementation, upon obtaining the source dataset, each data point in the dataset can be classified and graded. Before classification and grading, a deep learning model can be used to add data type and privacy level labels to each data point in the source dataset. The privacy level labeling needs to be determined based on the data holder's level of acceptance of the data and is related to various use cases; therefore, this embodiment does not impose restrictions on it. After labeling, the data can be classified using filters or classifiers, with the classification relationships referring to... Figure 2 In the classification process, two main categories can be identified: the data type classification set and the data privacy level classification set. Assuming the current data types include text, audio, and other types, and the privacy levels are divided into General, Secret, Confidential, and Top Secret, with the privacy level ranking as General < Secret < Confidential < Top Secret. That is, for a given data type, it will only appear once in both the data type classification set and the privacy level classification set. For example, if data is classified as secret image data, its classification result can be referenced... Figure 1 As shown. Using the same method, it is possible to classify all data in the source dataset and determine the data type and privacy level of each data point.

[0074] In one feasible implementation, the step of classifying and classifying the source dataset based on data type to determine the data type and data privacy level of each piece of data in the source dataset includes:

[0075] The source dataset is filtered to remove abnormal data, thus obtaining the initial source dataset.

[0076] The data in the initial source dataset is identified to determine the data type label and privacy label;

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

[0078] In the specific implementation, the source dataset D is filtered to remove abnormal data, such as duplicate data, missing data, and invalid data, to obtain the initial source dataset. The initial filtered dataset is the dataset after filtering the source data, denoted as D. init Let the type labeling function be T and the privacy labeling function be P. Then, for the initial source dataset D... init Each data item d in i Its type marker t i and privacy tag p i It can be represented as:

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

[0080] During classification, it can be performed based on the classification function C, for the set {t} of all type tags and privacy tags. i p i} can be classified into different data types and data privacy levels, resulting in a classification result G(D), which is represented 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 in the source dataset. Each data point is a node in the data index tree, which includes a classification index tree and a privacy index tree.

[0083] It's important to note that a data index tree is a data structure used for efficient data management and retrieval. A B+ tree can be chosen as a specific option, where all leaf nodes are connected by a linked pointer, making range queries more efficient. Categorical index trees and privacy index trees are index trees created based on data type and data privacy level, respectively.

[0084] Understandably, when creating categorical and privacy index trees, it's crucial to first define the data type (e.g., numeric, text) and corresponding privacy level (e.g., general, confidential, secret) of each record in the dataset. This information determines how the data is organized and indexed. When creating a categorical index tree, the primary classification method is the data category. A root node is created to represent the entire dataset, and secondary root nodes are created under the root node based on data type. These secondary root nodes represent all categorized datasets; for example, in an image-based categorized dataset, all nodes point to the secondary root nodes for image data. Then, starting from the root node, the dataset is recursively divided into smaller parts according to the selected classification criteria, creating child nodes for each part. Considering the tree's depth and width, query efficiency is maximized, and nodes in certain positions can be merged to maintain tree balance. The specific balancing strategy can be determined by the size of the leaf nodes to keep the number of leaf nodes relatively close.

[0085] When constructing a privacy index tree, corresponding privacy policies are formulated based on the privacy level of the data. For example, which data can only be accessed by specific roles, or which data needs to be stored encrypted. Access Control Lists (ACLs) are applied to each node to ensure that only authorized users can access specific data. For sensitive data, anonymized information can be stored in the index tree instead of the original data. For example, a user's hash value can be stored instead of their real name. For highly sensitive data, encryption technology can be used to store it in the index tree, ensuring that even if the data is leaked, it cannot be easily interpreted. This allows for the creation of two index trees with the same database but different classification methods.

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

[0087] The data in the source dataset will be grouped according to the data type and the data privacy level to obtain multiple grouped datasets;

[0088] The hash insertion values ​​are obtained by performing hash calculations on the data in the grouped datasets respectively;

[0089] The hash insertion value is mapped to the corresponding position in the initial bit to obtain the index value;

[0090] The index value is encoded using a binary tree based on the data volume of the grouped dataset to obtain the index code.

[0091] Construct a data index tree based on the index encoding.

[0092] It should be noted that the hash insertion value is the hash value obtained by hashing the data. Different input data will result in different hash insertion values, thus the hash insertion value can be considered as another form of data representation. The index code refers to the position within the index tree. The index code determines the search path within the index tree, and its generation is determined based on the amount of data in the dataset corresponding to the current node.

[0093] Understandably, the process begins by grouping the data in the source dataset according to data type and privacy level, resulting in multiple grouped datasets. Grouping criteria are defined based on data type (e.g., numeric, text) and privacy level (e.g., general, secret, confidential). The source dataset is then traversed, and data is categorized according to these criteria, forming multiple grouped datasets. Hash calculations are then performed on the data in each grouped dataset to obtain hash insertion values. During the data insertion phase, a hash function is used to map elements to initial bits, and the corresponding bits are set to 1. In all phases, the same hash function is used to map all elements. (Refer to...) Figure 3 , Figure 3 To generate a diagram for hash insertion values, given a dataset A = {a1, a2, ..., a...} n}, and use k hash functions {h1,h2,...,h k The process maps n elements to bits of length m, initially setting all elements in the bit vector to 0. When inserting an element, the k corresponding bits calculated by the hash function are set to 1. This yields the index value after hash insertion. Then, the order of each group is determined based on the data volume of the grouped dataset, and a binary tree encoding is performed using the data volume order information and the index value to obtain the index code. The root node of the index tree is created, and child nodes are recursively constructed based on the index code. Each child node represents the index value of a grouped dataset.

[0094] Step S30: 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 based on 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 categorical index tree and the privacy index tree, respectively. The data at the first node position is located in the categorical index tree, and the data at the second node position is located in the privacy index tree. A transmissive index, on the other hand, breaks down the barrier between the two trees, enabling indexes to point from the categorical index tree to the privacy index tree or vice versa.

[0096] The step of determining the first node position and the second node position of each data point in the classification index tree and the privacy index tree, respectively, and generating a transmission index based on the first node position and the second node position includes:

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

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

[0099] Generate bidirectional node vectors with the first node position and the second node position as the start and end points, respectively.

[0100] The bidirectional node vector is encoded to obtain the 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 vice versa, thus enabling dimensional crossing between the two index trees.

[0102] In its implementation, after constructing the categorical index tree and the privacy index tree, a projection index can be created between the two index trees. During data searching, it's possible to traverse from one index tree to the other. During traversal, 0 and 1 can represent starting from the root node and traversing left and right to reach the target node, respectively. For example, 010 represents starting from the root node and traversing left, right, and then left to reach the target node. When generating the projection index, this operation can be performed on any data to determine its position in the categorical index tree and the privacy index tree. The node position can be determined by the index value, resulting in a value like P. 分类 (D i = p1p2p3...p n , where p j This represents the j-th direction on the path (0 for left, 1 for right). Similarly, P can be obtained in the privacy index tree based on the same operation. 隐私 (D i )=q1q2q3...q m , where q k This represents the k-th direction on the path (0 for left, 1 for right). Then, a transmission index is generated based on the positions of the two nodes. The transmission index can be determined by combining the hash path, specifically:

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

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

[0105] Using the methods described above, we can determine the node position of each data item in the categorical index tree and the privacy index tree, and generate a transparent index. The transparent index can be used to efficiently query and manage data while protecting data privacy.

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

[0107] It's important to note that a data index diagram is a graphical representation used to illustrate how data is organized within an index structure. Through a data index diagram, one can intuitively understand how data is indexed, stored, and retrieved.

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

[0109] The transmission index determines the node position of the data in the classification index tree and the privacy index tree;

[0110] Undirected edges are obtained based on the node positions 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 practical implementation, when determining the node position of data in the classification index tree and the privacy index tree based on the transmission index, the data's position in both index trees can be determined based on the endpoints of the transmission index. Once the node positions of the data in the two index trees are obtained, undirected edges can be determined based on these positions and the transmission index. An undirected edge represents a connection between two nodes and can be represented 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 pair of node positions in the categorical index tree and the privacy index tree.

[0115] Finally, nodes in the classification index tree and privacy index tree are connected using defined undirected edges to construct a data index graph. Since the classification index tree and privacy classification tree already exist, they can be used as the main body of the index graph, connected by undirected edges, as a bridge. Figure 4 , Figure 4 This diagram illustrates the construction of the index graph. The dashed lines represent undirected edges used to connect the classification index tree and the privacy classification tree to form the index graph.

[0116] Step S50: Upon receiving a data retrieval request, generate a retrieval value based on the data retrieval request, match the retrieval value with the index value of the node in the data index graph to obtain a matching result, determine the target data based on the matching result, and output the target data as the retrieval result.

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

[0118] In a specific implementation, upon receiving a data retrieval request, the following steps are taken: First, retrieval request data is determined based on the data retrieval request. Second, the retrieval request data is transformed into a retrieval value. Third, based on the retrieval value, the index graph is traversed from a first retrieval starting point and a second retrieval starting point, and the retrieval value is matched with the index values ​​of the nodes in the index graph to determine the retrieval direction. Fourth, based on the retrieval direction, a traversal direction is determined, and the index values ​​along the traversal direction are matched with the retrieval value to obtain a matching result. Fifth, when the matching result is an index value that matches 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, upon receiving a data retrieval request, the system can generate a retrieval value based on the request content. This involves content analysis to determine the data type and classification of the requested content. For example, when the request is for an ID card number, the system can determine whether the data type is numeric or text, and the privacy level is confidential. Based on this, a retrieval header is generated, and the content itself is used to generate a retrieval tail. The header and tail are then combined to obtain the retrieval value. The system then traverses the data index graph based on the retrieval value, matching it with the node's index value to obtain a matching result. There are two possible matching results: a successful match, in which case the system can choose to display or hide the data based on the user's privacy permissions; and a failed match, in which case the system outputs a failed match message. Finally, the system identifies the target data and outputs it as the retrieval result.

[0119] In one feasible implementation, the step of traversing the index graph from the first and second retrieval starting points based on the retrieval value, matching the retrieval value with the index values ​​of the nodes in the index graph, and determining the retrieval direction further includes:

[0120] The search request is authenticated to determine the privacy access permissions of the search request;

[0121] Node data with a privacy level greater than the privacy access permission level is hidden, and the index value of the node data in the index graph is blocked.

[0122] It should be noted that privacy access permissions refer to the maximum privacy level of data that can be queried. If the privacy access permissions are lower than the privacy level of the data, then it cannot be viewed.

[0123] In the specific implementation, the identity can be verified when a search request is made, the privacy access permissions of the requester can be determined, and the node data with a privacy level greater than the privacy access permission is hidden. The index value of the node data in the index graph is also hidden. At this time, it can be regarded as forming an index graph that belongs exclusively to the current user. The data in the index graph is all the content that the user can see. Therefore, when the user makes a request, the search can be carried out only within a limited scope, and there will be no situation where privacy data is questioned indirectly. On the other hand, it can also reduce the amount of data and improve the search time.

[0124] This embodiment provides a data processing method that classifies and grades a source dataset to determine the data type and privacy level of each data point. A data index tree is constructed based on the data in the source dataset, with each data point being a node in the data index tree. The node position of each data point in both the classification index tree and the privacy index tree is determined, and a transmission index is generated based on the node positions. The classification index tree and the privacy index tree are combined based on the transmission index to generate a data index graph. Upon receiving a data retrieval request, a retrieval value is generated based on the request. The retrieval value is matched against the index values ​​of the nodes in the data index graph to obtain matching results. The target data is then determined based on the matching results and output as the retrieval result. Through this method, data is constructed into a multi-dimensional data network graph from both privacy and type perspectives, improving data retrieval efficiency.

[0125] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the data processing method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0126] This application also provides a data processing apparatus, please refer to... Figure 5 The data processing device includes:

[0127] Data classification module 10 is used to classify and classify the data in the source dataset based on data type, and to determine the data type and data privacy level of each data in the source dataset.

[0128] The retrieval tree generation module 20 is used to construct a data index tree based on the data type and the data privacy level of the source dataset, 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] The index transmission module 30 is 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 based on the first node position and the second node position.

[0130] Index graph generation module 40 is used to combine the classification index tree and the privacy index tree based on the transmission index to generate a data index graph;

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

[0132] In one feasible implementation, the retrieval tree generation module 20 is further configured to: group the data in the source dataset according to the data type and the data privacy level to obtain multiple group datasets; perform hash calculations on the data in each group dataset to obtain hash insertion values; map the hash insertion values ​​to the 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 group datasets to obtain index encoding; and construct a data index tree based on the index encoding.

[0133] In one feasible implementation, the index transmission module 30 is further configured to determine the position of each data in the first node of the classification index tree; determine the position of each data in the second node of the privacy index tree; generate bidirectional node vectors with the first node position and the second node position as the start and end points respectively; and encode the bidirectional node vectors to obtain the transmission index.

[0134] In one feasible implementation, the index graph generation module 40 is further configured to determine the node position of the data in the classification index tree and the privacy index tree using the transmission index; obtain undirected edges based on the node positions and the transmission index; and connect the nodes in the classification index tree and the privacy index tree with the undirected edges to obtain a data index graph.

[0135] In one feasible implementation, the data classification module 10 is further configured to filter the source dataset, filter out abnormal data in the source dataset to obtain an initial source dataset; identify the data in the initial source dataset to determine the data type label and privacy label; classify the type label and privacy label to determine the data type and data privacy level.

[0136] In one feasible implementation, the retrieval output module 50 is further configured to, upon receiving a data retrieval request, determine retrieval request data based on the data retrieval request; perform data conversion on the retrieval request data to convert it into a retrieval value; traverse the index graph from the first retrieval starting point and the second retrieval starting point based on the retrieval value, match the retrieval value with the index value of the node in the index graph, and determine the retrieval direction; determine the traversal direction based on 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 that matches 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 one feasible implementation, the retrieval output module 50 is further configured to authenticate the retrieval request, determine the privacy access permissions of the retrieval request, hide node data with a privacy level greater than the privacy access permissions, and mask the index value of the node data in the index graph.

[0138] The data processing apparatus provided in this application, employing the data processing method described in the above embodiments, can solve the technical problem of low processing efficiency in privacy data retrieval. Compared with the prior art, the beneficial effects of the data processing apparatus provided in this application are the same as those of the data processing method described in the above embodiments, and other technical features in the data processing apparatus are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0139] This application provides a data processing apparatus, 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 to enable the at least one processor to perform the data processing method in Embodiment 1 above.

[0140] The following is for reference. Figure 6This document illustrates a structural diagram of a data processing device suitable for implementing embodiments of this application. The data processing device in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 6 The data processing device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0141] like Figure 6 As shown, the data processing device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the data processing device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the data processing device to communicate wirelessly or wiredly with other devices to exchange data. Although the figures show data processing devices with various systems, it should be understood that implementing or having all of the systems shown is not required. More or fewer systems may be implemented alternatively.

[0142] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0143] The data processing device provided in this application, employing the data processing method described in the above embodiments, can solve the technical problems of data processing. Compared with the prior art, the beneficial effects of the data processing device provided in this application are the same as those of the data processing method described in the above embodiments, and other technical features of the data processing device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0144] It should be understood that the various parts disclosed in this application can be implemented using 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 suitable manner in one or more embodiments or examples.

[0145] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0146] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the data processing method described in the above embodiments.

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

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

[0149] The aforementioned computer-readable storage medium carries one or more programs that, when executed by a data processing device, cause the data processing device to: classify and classify the data in the source dataset based on data type, and determine the data type and data privacy level of each data in the source dataset; construct a data index tree based on the data type and the data privacy level, where 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 the first node position and the second node position of each data in the classification index tree and the privacy index tree, and generate a transmission index based on the first node position and the second node position; combine the classification index tree and the privacy index tree based on the transmission index to generate a data index graph; and upon receiving a data retrieval request, generate a retrieval value based on the data retrieval request, match the retrieval value with the index value of a node in the data index graph to obtain a matching result, determine the target data based on the matching result, and output the target data as the retrieval result.

[0150] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0151] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0152] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0153] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for performing the above-described data processing method, and is capable of solving the technical problem of data processing. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the data processing method provided in the above embodiments, and will not be repeated here.

[0154] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the data processing method described above.

[0155] The computer program product provided in this application can solve the technical problems of data processing. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the data processing method provided in the above embodiments, and will not be repeated here.

[0156] The above are only some embodiments of this application and do not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the content of this application specification and drawings, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A data processing method, characterized by, The data processing method includes: The data in the source dataset is classified and graded based on data type to determine the data type and data privacy level of each data in the source dataset. Based on the data type and the data privacy level, a data index tree is constructed according to the data in the source dataset, where 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 the first node position and the second node position of each data point in the classification index tree and the privacy index tree, and generate a transmission index based on the first node position and the second node position; Based on the transmission index, the classification index tree and the privacy index tree are combined to generate a data index graph; Upon receiving a data retrieval request, a retrieval value is generated based on the data retrieval request. The retrieval value is then matched against the index value of a node in the data index graph to obtain a matching result. The target data is determined based on the matching result and output as the retrieval result.

2. The method of claim 1, wherein, The step of constructing a data index tree based on the data type and the data privacy level includes: The data in the source dataset will be grouped according to the data type and the data privacy level to obtain multiple grouped datasets; The hash insertion values ​​are obtained by performing hash calculations on the data in the grouped datasets respectively; The hash insertion value is mapped to the corresponding position in the initial bit to obtain the index value; The index value is encoded using a binary tree based on the data volume of the grouped dataset to obtain the index code. Construct a data index tree based on the index encoding.

3. The method as described in claim 1, characterized in that, The step of determining the first node position and the second node position of each data point in the classification index tree and the privacy index tree, respectively, and generating a transmission index based on the first node position and the second node position includes: Determine the position of each data point in the first node of the classification index tree; Determine the position of each piece of data in the second node of the privacy index tree; Generate bidirectional node vectors with the first node position and the second node position as the start and end points, respectively. The bidirectional node vector is encoded to obtain the transmission index.

4. The method as described in claim 1, characterized in that, The step of combining the classification index tree and the privacy index tree based on the transmission index to generate a data index graph includes: The transmission index determines the node position of the data in the classification index tree and the privacy index tree; Undirected edges are obtained based on the node positions 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 as described in claim 1, characterized in that, The steps of classifying and classifying the source dataset based on data type to determine the data type and data privacy level of each piece of data in the source dataset include: The source dataset is filtered to remove abnormal data, thus obtaining the initial source dataset. The data in the initial source dataset is identified to determine the data type label and privacy label; The type markers and privacy markers are classified to determine the data type and data privacy level.

6. The method as described in claim 1, characterized in that, The steps of generating a retrieval value based on the data retrieval request upon receiving the data retrieval request, matching the retrieval value with the index value of a node in the data index graph to obtain a matching result, determining the target data based on the matching result, and outputting the target data as the retrieval result include: Upon receiving a data retrieval request, the data to be retrieved is determined based on the data retrieval request. The search request data is transformed into search values. Based on the search value, the search is traversed from the first search starting point and the second search starting point of the index graph, and the search value is matched with the index value of the node in the index graph to determine the search direction; The traversal direction is determined based on the retrieval direction, and the index value based on the traversal direction is matched with the retrieval value to obtain the matching result; When the matching result is an index value that matches 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 as described in claim 6, characterized in that, Before the step of traversing the index graph from the first and second search starting points based on the search value, matching the search value with the index values ​​of the nodes in the index graph, and determining the search direction, the method further includes: The search request is authenticated to determine the privacy access permissions of the search request; Node data with a privacy level greater than the privacy access permission level is hidden, and the index value of the node data in the index graph is blocked.

8. A data processing apparatus, characterized in that, The data processing device includes: The data classification module is used to classify and classify the data in the source dataset based on the data type, and to determine the data type and data privacy level of each data in the source dataset. The retrieval tree generation module is used to construct a data index tree based on the data type and the data privacy level of the source dataset, 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. The index transmission module is 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 based on the first node position and the second node position; An index graph generation module is used to combine the classification index tree and the privacy index tree based on the transmission index to generate a data index graph; The retrieval output module is used to generate a retrieval value based on the data retrieval request when a data retrieval request is received, match the retrieval value with the index value of the node in the data index graph to obtain a matching result, determine the target data based on the matching result, and output the target data as the retrieval result.

9. A data processing device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the data processing method as described in 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, it implements the steps of the data processing method as described in any one of claims 1 to 7.

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