Semantic search method based on secret state knowledge graph

Abstract

The invention relates to a semantic search method based on a secret state knowledge graph, and belongs to the technical field of information security and privacy protection, and the method comprises the following steps: S1, constructing an Internet of Vehicles cloud edge collaborative security semantic search system; s2, the authorization center initializes the system; s3, the authorization center registers the user; s4, the edge server generates a ciphertext; and S5, the authorized user sends a query request to the nearest edge server, the edge server generates a query trap door for a legal user request and performs search matching, if the search is not successful, the edge server sends the query trap door to the cloud for search matching, and finally the edge server decrypts a search result and returns the decrypted search result to the user. According to the method, the comprehensiveness and accuracy of the search result are improved, the privacy and safety of the data are guaranteed, and the method is suitable for semantic search of encrypted data in an Internet of Vehicles scene.

Description

Technical Field

[0001] The present invention belongs to the technical field of information security and privacy protection, and relates to a semantic search method based on a secret knowledge graph. Background Art

[0002] With the rapid development of Internet of Things (IoT) technology, IoV technology has been widely used in the fields of vehicle monitoring, intelligent transportation, and driver assistance, providing users with a smarter and more convenient travel experience. However, with the increase in data volume, the resources of on-board terminal devices are limited, making it difficult to cope with powerful applications that require intensive computing and large-capacity caches, such as autonomous driving, artificial intelligence processing, and low-latency content delivery. Therefore, it is necessary to outsource data to cloud computing centers to utilize their powerful computing and storage capabilities. Since the amount of data generated by IoV is huge and extremely valuable, in order to achieve on-demand data acquisition, users can obtain IoV-related data through search. For example, users want to obtain sensor data on a road section through keyword search to analyze traffic flow. However, this type of data involves sensitive information such as specific geographic location and time. Once leaked, it may lead to problems such as the tracking of user whereabouts and activity trajectories and the exposure of personal privacy. Therefore, protecting the privacy of users and data during data search is of great research value. Existing methods encrypt data before users outsource data. However, the indistinguishability of ciphertext data poses severe challenges to retrieval and matching in an encrypted environment.

[0003] To further realize data sharing without leaking data privacy, searchable encryption technology enables users to use indexes under ciphertext to perform keyword searches. Currently, the ciphertext data search method in the Internet of Vehicles mainly supports precise keyword matching based on secret space. When data is stored in ciphertext on a cloud server, users can use the powerful computing power of the cloud server to perform keyword retrieval while ensuring that the server cannot obtain any user's private information, so as to achieve data sharing without leaking data privacy. However, due to the differences in the background knowledge of the search users, there are large deviations in their keyword descriptions of the search objects, which makes the keyword-based searchable encryption scheme have a low recall rate in the precise matching mode. Due to the large differences in the subjective cognition of the search users, searching and matching only by keywords will lead to severe limitations in recall performance. Existing data search methods do not consider semantic relevance search under user privacy protection. For example, when a user of the Internet of Vehicles searches for a "hotel" with good road conditions and easy parking near the current driving area, the user hopes to find the "hotel" and understand the traffic conditions and parking convenience around it. Traditional searchable encryption schemes usually rely on precise keyword matching and can only return results that fully match the query word "hotel", but cannot filter out potential needs that meet the user's conditions such as good road conditions and convenient parking. Therefore, the introduction of semantic search technology is of great significance in the field of Internet of Vehicles.

[0004] Semantic search has significantly improved the comprehensiveness of information retrieval tasks. Although relevant research on semantic search has achieved certain results in the past, most semantic search methods do not consider user and data privacy issues and cannot meet the privacy protection requirements and information needs of users in the Internet of Vehicles environment. Currently, there are few methods that meet the privacy data protection and semantic search needs of the Internet of Vehicles. Wang et al. proposed a method to implement encrypted data retrieval by fuzzy retrieval of misspelled keywords and ranking the results by relevance in "Encrypted data retrieval and sharing scheme in space–air–ground-integrated vehicular networks" [IEEE Internet of Things Journal, 2021, 9(8): 5957-5970]. However, this scheme mainly relies on word frequency-inverse document frequency to calculate the correlation between documents and queries, resulting in insufficient association capabilities and limited ability to search for context-related information.

[0005] Therefore, there are still many challenges in realizing secure retrieval of encrypted data in the Internet of Vehicles scenario. On the one hand, due to the complexity and uncertainty of the road environment, high requirements are placed on real-time performance and accuracy. On the other hand, due to the large differences in subjective cognition among search users, traditional keyword search methods based on dense space usually require precise keyword matching, and the returned results only contain matching content for the keyword, which severely limits the completeness of the search system. Summary of the invention

[0006] In view of this, the purpose of the present invention is to address the problem that the accuracy and comprehensiveness of system searches under encrypted data cannot be met in the Internet of Vehicles. Different from the traditional precise matching keyword search method based on encrypted space, the present invention designs a cloud-edge collaborative security semantic search architecture, which consists of a three-layer architecture of end-edge-cloud, integrating the advantages of the edge side being close to the data source for real-time response and the characteristics of the cloud side's powerful computing power and convenient data sharing. The edge directly returns local search results to reduce user search delays. Secondly, in view of the problem that existing encrypted search methods mainly focus on precise keyword matching and do not fully utilize the semantic relationship between data, the present invention uses encrypted knowledge graphs for semantic association search, constructs corresponding knowledge graphs through semantic relationships between data, and encrypts and protects sensitive information. When a user queries, the query is semantically parsed, and related nodes and edges are found in the knowledge graph, and results that meet the query intent are returned. This method supports semantic search of user encrypted data, improves the comprehensiveness and accuracy of search results, and ensures data privacy and security.

[0007] In order to achieve the above object, the present invention provides the following technical solutions:

[0008] A semantic search method based on a dense knowledge graph comprises the following steps:

[0009] S1: Build a secure semantic search system for cloud-edge collaboration in the Internet of Vehicles;

[0010] S2: The authorization center initializes the system;

[0011] S3: The authorization center registers the user;

[0012] S4: The edge server generates ciphertext;

[0013] S5: The authorized user sends the query request to the nearest edge server. The edge server generates a query trap for the legitimate user request and performs search matching. If the search is unsuccessful, the edge server sends the query trap to the cloud for search matching. Finally, the edge server decrypts the search results and returns them to the user.

[0014] Furthermore, the Internet of Vehicles cloud-edge collaborative security semantic search system includes:

[0015] Authorization Center: The authorization center is a trusted institution responsible for the generation and issuance of keys and user registration authorization;

[0016] Data entity: entity data collected by vehicle entities and surrounding facility sensors;

[0017] Edge server: responsible for the following tasks: 1) Organize data entities, extract key information to build corresponding attribute graphs and encrypt them, generate indexes for each node entity and edge in the attribute graph, and then cache the encrypted knowledge graph locally, and upload the backup to the cloud server for storage; 2) Perform natural language processing according to the query requirements in the natural language form entered by the user, extract core keywords to generate trapdoors for search matching; 3) Decrypt the results of successful search matching, and return the final results to the user in plain text;

[0018] Cloud server: used to organize the encrypted data uploaded by each edge server to the cloud into a global database storage, provide it to users for search, and return the matching results to the corresponding edge server;

[0019] User: Sends query request to the nearest edge server through a secure channel; receives decrypted plaintext result from the edge server.

[0020] Furthermore, in step S2, the authorization center performs global initialization and takes the security parameter k as input, outputs the key K={K1, K2, K3} and the system parameter S, where S∈Z qIt is used for user registration authorization. The length of K1 and K2 are both k-bit. The symmetric key K3 is generated by a symmetric encryption algorithm and is used to encrypt data.

[0021] Further, in step S3, the authorization center generates a user authorization certificate using the identity information sent by the user and S And send it to the edge server authorized user list list, so as to add a new authorized user u i ,in H1:{0,1} * →Z q .

[0023] Furthermore, in step S4, the edge server organizes the collected data into a knowledge graph and corresponding indexes, encrypts them, and backs them up to the cloud server, which specifically includes the following steps:

[0024] S41: Data processing: Integrate various types of data, extract entities, attributes and relationships between entities, and process the data into a key-value pair structured attribute graph for storage;

[0025] S42: Index generation: sort the key-value pair data by key and generate num values ​​in order. <k i ,v i ,num i >triplets, and using each <k i ,v i ,num i >k in i Generate partial index EInd i =MAC(Ind xi ,1); then define the random permutation π:[n]→[n], for each <k i ,v i ,num i > i generate Storage, forming the final index structure DInd i ={EInd i ,Enum i} for storage;

[0026] S43: Data encryption: key-value pair data node generated after data processing i =(k i ,v i ) is encrypted and stored to form a ciphertext c, and all elements are padded to the same length; where j∈J,J∈[1,|m|], m=(k1,k2,...k i ) is a key-value pair information library;

[0027]

[0028] Further, the step S5 specifically includes the following steps:

[0029] S51: Trapdoor generation: The authorized user sends an authorization credential and a query request to the nearest edge server; first, the edge server verifies the user based on the user's authorization credential Ac. Is it in the authorized user list list? If The edge server extracts the keywords in the user query request through natural language processing and generates the corresponding query trap as ET i ;

[0030] S52: Search matching: The edge server compares the query keyword set {w q1 ,...,w qn} by the keyword w q1 The generated trapdoor Eτ1=MAC(td x1 ,1) and the stored index EInd i If the two are equal, the index structure DInd i ={EInd i ,Enum i}Find the corresponding num i =π -1 (j) value as a pointer to locate the corresponding key-value pair (ck j ,cv j ); will (ck j ,cv j ) as the starting node to retrieve the one-hop subgraph to obtain the node corresponding to w q1 The set of directly related nodes and edges S1 = (V, R);

[0031] Then in the initial search result S1, according to w q2 Filter out the node and edge set S2 = (V', R') that matches it, and so on, filter out the nodes and edges that match the subsequent query keyword {w q3 ,...,w qn} matches the set of nodes and edges, if the qi ∈{w q2 ,...,w qn The corresponding ciphertext trapdoors generated by} all satisfy Eτ i ∈{Eτ2,...,Eτ n} and {Eτ2,...,Eτ n}∈EInd, then we finally get q1 ,...,w qn} Matching nodes and corresponding edge set S n =(V* ,R * );

[0032] V * ={(ck1,cv1),...,(ck n ,cv n )},n≤m

[0033] S53: Data decryption: The retrieved data S n =(V * ,R * ) is decrypted on the edge server, and a new information subgraph SubG is constructed based on the decrypted nodes and edges and returned to the user.

[0034] Furthermore, in the key-value mode, the one-hop subgraph query retrieves the attribute value corresponding to the entity through the entity identifier, and retrieves the edges and attribute values ​​of the edges related to the entity to return all related nodes:

[0035] V={(ck1,cv1),...,(ck m ,cv m )}.

[0036] The beneficial effects of the present invention are as follows: the present invention designs a semantic search method based on an encrypted knowledge graph under the Internet of Vehicles cloud-edge collaborative secure semantic search architecture, aiming to achieve that by associating different terms and contextual information, the knowledge graph can capture the user's potential information needs and provide more comprehensive and accurate search results. At the same time, it effectively improves the accuracy and efficiency of encrypted data retrieval and protects the privacy of Internet of Vehicles users. Symmetric encryption is used to encrypt and store key-value pair data and user query data in the knowledge graph, allowing users to search for data information while data is encrypted, and ciphertext information sharing is provided on the basis of protecting user privacy and data information. In the Internet of Vehicles scenario, the user's query information and the specific location, driving trajectory and other information of the vehicle are considered sensitive. The present invention realizes search and matching in a ciphertext state, so the actual data content will not be leaked, and sensitive information is protected.

[0037] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0039] Figure 1 This is a system model diagram of the edge-cloud collaborative efficient encrypted semantic search method described in the present invention;

[0040] Figure 2 Schematic diagram of the knowledge graph encryption method designed for the present invention;

[0041] Figure 3 Flowchart of the efficient encrypted semantic search method with edge-cloud collaboration designed for the present invention. DETAILED DESCRIPTION

[0042] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0043] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0044] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0045] Figure 1A system model diagram of an efficient encrypted semantic search method for edge-cloud collaboration provided by a specific embodiment of the present invention. The traditional encrypted search architecture relies on precise keyword matching in the cloud, which cannot accurately understand the user's query intention, resulting in low query efficiency and difficulty in meeting user needs. The architecture uses searchable encryption to protect the privacy of entity data and fully integrates the advantages of edge and cloud. The edge directly returns local search results to reduce user search delays. The architecture includes five types of participants: authorization center, data entity, edge server, cloud server and search user. Authorized users can search for required data on edge servers or clouds. In the Internet of Vehicles, since sensors and other entities have spatial location attributes, each edge server stores and processes data of nearby local entities respectively, thereby achieving efficient data processing and data access for user search requests. Data privacy protection can be achieved through encrypted data storage locally and in the cloud. All edge servers upload encrypted knowledge graph indexes of local data to cloud servers to meet users' complex search needs. Edge servers are close to search users and can respond to local search requests more promptly. At the same time, with the help of global indexes in the cloud, users can expand the search space and query non-local data or local historical data, thereby achieving a wider range of queries.

[0046] In the IoV cloud-edge collaborative secure semantic search architecture:

[0047] Authorization Center: The authorization center is a trusted organization that is mainly responsible for the generation and issuance of keys and user registration authorization.

[0048] Data Entity: Sensor entity data such as vehicle entities and surrounding facilities are uploaded to the nearest edge server through a secure channel.

[0049] Edge server: Mainly responsible for the following tasks: 1) Organize the uploaded data to extract key information to build the corresponding attribute graph and encrypt it, generate indexes for each node entity and edge in the attribute graph, and then cache the encrypted knowledge graph locally so that local users can query information more quickly, and upload the backup to the cloud server for storage. 2) According to the natural language query requirements entered by the user, such as the Internet of Vehicles user searching for "nearby hotels with good road conditions and easy parking" during the current driving, natural language processing is performed to extract core keywords to generate trapdoors for search matching. 3) Decrypt the results of successful search matching, and the final results are returned to the user in plain text.

[0050] Cloud Server: The cloud organizes the encrypted data uploaded by each edge server to the cloud into a global database storage, provides it to users for search, and returns data that matches the user's query through cooperation with the edge server.

[0051] User: Sends query requests to the nearest edge server through a secure channel. The edge server generates a trapdoor and searches for data on the edge server or in the cloud. Finally, the user receives the decrypted plaintext result from the edge server.

[0052] The collaborative process of this method is:

[0053] System initialization, the authorization center performs global initialization and issues keys, which are securely transmitted to other parts. The key generation algorithm is run by the authorization center, which outputs the key K and system parameter S by inputting the security parameter k.

[0054] User registration, performed by the authorization center, adds a new authorized user u i The authorization center uses the identity information sent by the user to generate a user authorization credential and sends it to the edge server's authorized user list.

[0055] Data encryption is performed by the edge server. The uploaded data is constructed into a key-value pair attribute graph G and then the encryption algorithm is run, with the key K and attribute graph G as input, and the output is the encrypted knowledge graph Θ.

[0056] Index Generation,The algorithm is run by the edge server.,By taking the keyword set W and the key K as input, a data index EInd is generated.

[0057] Trapdoor Generation,The algorithm is executed by the edge server to generate the trapdoor ET by inputting the query keyword set and the key.

[0058] Secure semantic search, the algorithm is executed by the edge server or cloud server. The user's query trapdoor is matched with the stored data index EInd. If the search is successful, the corresponding data entity node position J is returned. According to J, the location of the entity node in the attribute graph is located, and the node is used as the starting node for searching to obtain node data adjacent to and related to this entity.

[0059] S17: Data decryption. When the search results are returned to the edge server, the edge server uses the corresponding key to decrypt the data and returns it to the user via a secure channel.

[0060] like Figure 2 This is a schematic diagram of the knowledge graph encryption method in efficient and secure semantic search. The encrypted knowledge graph is used for semantic association search. The corresponding knowledge graph is constructed through the semantic relationship between data, and sensitive information is encrypted to protect it. When a user queries, the query is semantically parsed, and the related nodes and edges are found in the knowledge graph, and the results that meet the query intent are returned. Figure 3 As shown, the method specifically comprises the following steps:

[0061] S1: System initialization. GenKey(1k )→(K,S) The authorization center calls the algorithm of this stage. Specifically, it takes the security parameter k as input and outputs the key K={K1,K2,K3} and the system parameter S, where S∈Z q It is used for user registration authorization. The length of K1 and K2 are both k-bit. The symmetric key K3 is generated by the secure symmetric encryption algorithm π and is used to encrypt data.

[0062] S2: User registration. Executed by the authorization center to add a new authorized user u i The authorization center uses the identity information sent by the user and S to generate a user authorization certificate. And send to the edge server authorized user list list, where H1:{0,1} * →Z q .

[0063] S3: Ciphertext generation. EncData(K,G)→Θ: This stage is performed by the edge server, which organizes the collected data into a knowledge graph and the corresponding index and encrypts it, and then backs it up to the cloud server.

[0064] (1) Data processing: Integrate various types of data, extract entities, attributes, and relationships between entities, and process the data into a key-value pair structured attribute graph for storage.

[0065] (2) Index generation: Sort the key-value pair data by key and generate num values ​​in sequence. <k i ,v i ,num i >triplets, and using each <k i ,v i ,num i >k in i Generate partial index EInd i =MAC(Ind xi ,1). Then define the random permutation π:[n]→[n], for each <k i ,v i ,num i > i generate Storage, forming the final index structure DInd i ={EInd i ,Enum i} for storage.

[0066] (3) Data encryption: The key-value pair data node generated after data processing i =(k i ,vi ) is encrypted and stored to form ciphertext c, and all elements are padded to the same length. Among them, j∈J,J∈[1,|m|], m=(k1,k2,...k i ) is a key-value pair information library.

[0067]

[0068] S4: Matching search and decryption. This process consists of two stages: the search matching stage and the decryption stage. In the search matching stage, the authorized user sends the query request to the nearest edge server. First, the edge server verifies whether the user is a legitimate user through the user credential Ac. If the user is a registered authorized user, the edge server generates a corresponding query trapdoor ET for the user request and searches. If the search is unsuccessful, the edge server sends ET to the cloud for further search and matching. If the match is still unsuccessful, the user's search request is rejected. In the decryption stage, the edge server decrypts the encrypted search results and returns them to the user.

[0069] (1) Trapdoor generation: The edge server executes the algorithm, and the authorized user sends the authorization credential and query request to the nearest edge server. First, the edge server verifies the user based on the user's authorization credential Ac. Is it in the authorized user list list? If Then the subsequent operations are performed. The edge server extracts the keywords in the user query request through natural language processing to generate the corresponding query trap door ET i .

[0070] (2) Search and match: This step is used to find relevant information in the encrypted database for the query request submitted by the user. q1 ,...,w qn} by the keyword w q1 The generated trapdoor Eτ1=MAC(td x1 ,1) and the stored index EInd i If the two are equal, the index structure DInd i ={EInd i ,Enum i}Find the corresponding num i =π -1 (j) value is used as a pointer to locate the corresponding key-value pair (ck j ,cv j ). j ,cv j ) as the starting node to retrieve the one-hop subgraph to obtain the node corresponding to w q1The set of directly related nodes and edges S1 = (V, R). In the key-value mode, a one-hop subgraph query retrieves the attribute value corresponding to the entity through the entity identifier, and retrieves the edges and attribute values ​​of the edges related to the entity to return all related nodes.

[0071] V={(ck1,cv1),…,(ck m ,cv m )}

[0072] Then in the initial search result S1, according to w q2 Filter out the nodes and edges set S2 = (V', R') that match it, and so on, filter out the nodes and edges that match the subsequent query keyword {w q3 ,...,w qn} matches the set of nodes and edges, if the qi ∈{w q2 ,...,w qn The corresponding ciphertext trapdoors generated by} all satisfy Eτ i ∈{Eτ2,...,Eτ n} and {Eτ2,...,Eτ n}∈EInd, and finally get q1 ,…,w qn} Matching nodes and corresponding edge set S n =(V * ,R * ).

[0073] V * ={(ck1,cv1),...,(ck n ,cv n )},n≤m

[0074] (3) Data decryption: The retrieved data S n =(V * ,R * ) is decrypted on the edge server, and a new information subgraph SubG is constructed based on the decrypted nodes and edges and returned to the user.

[0075] In the above embodiments, the description's reference to "this embodiment" indicates that a particular feature, structure, or characteristic described in conjunction with the embodiment is included in at least some embodiments, but not necessarily all embodiments. Multiple occurrences of "this embodiment" do not necessarily all refer to the same embodiment.

[0076] In the above-described embodiments, although the invention has been described in conjunction with specific embodiments of the invention, many substitutions, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. For example, other storage structures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed. Embodiments of the invention are intended to encompass all such substitutions, modifications, and variations that fall within the broad scope of the appended claims.

[0077] This embodiment further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, any one of the methods in this embodiment is implemented.

[0078] This embodiment also provides an electronic terminal, including: a processor and a memory;

[0079] The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes any one of the methods in this embodiment.

[0080] The computer-readable storage medium in this embodiment can be understood by ordinary technicians in this field: all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

[0081] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication with each other. The memory is used to store computer programs, the communication interface is used to communicate, and the processor and the transceiver are used to run computer programs so that the electronic terminal executes each step of the above method.

[0082] In this embodiment, the memory may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0083] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0084] The present invention can be used in many general or special computing system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like.

[0085] The present invention may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A semantic search method based on a dense knowledge graph, characterized in that: The following steps are involved: S1: Build a secure semantic search system for cloud-edge collaboration in the Internet of Vehicles; S2: The authorization center initializes the system; S3: The authorization center registers the user; S4: The edge server generates ciphertext; S5: The authorized user sends the query request to the nearest edge server. The edge server generates a query trap for the legitimate user request and performs search matching. If the search is unsuccessful, the edge server sends the query trap to the cloud for search matching. Finally, the edge server decrypts the search results and returns them to the user.

2. The semantic search method based on dense knowledge graph according to claim 1, characterized in that: The Internet of Vehicles cloud-edge collaborative security semantic search system includes: Authorization Center: The authorization center is a trusted institution responsible for the generation and issuance of keys and user registration authorization; Data entity: entity data collected by vehicle entities and surrounding facility sensors; Edge server: responsible for the following tasks: 1) Organize data entities, extract key information to build corresponding attribute graphs and encrypt them, generate indexes for each node entity and edge in the attribute graph, and then cache the encrypted knowledge graph locally, and upload the backup to the cloud server for storage; 2) Perform natural language processing according to the query requirements in the natural language form entered by the user, extract core keywords to generate trapdoors for search matching; 3) Decrypt the results of successful search matching, and return the final results to the user in plain text; Cloud server: used to organize the encrypted data uploaded by each edge server to the cloud into a global database storage, provide it to users for search, and return the matching results to the corresponding edge server; User: Sends query request to the nearest edge server through a secure channel; receives decrypted plaintext result from the edge server.

3. The semantic search method based on dense knowledge graph according to claim 1, characterized in that: In step S2, the authorization center performs global initialization and takes the security parameter k as input, outputs the key K={K1, K2, K3} and the system parameter S, where S∈Z q It is used for user registration authorization. The length of K1 and K2 are both k-bit. The symmetric key K3 is generated by a symmetric encryption algorithm and is used to encrypt data.

4. The semantic search method based on dense knowledge graph according to claim 1, characterized in that: In step S3, the authorization center generates a user authorization certificate using the identity information sent by the user and S And send it to the edge server authorized user list list, so as to add a new authorized user u i ,in H1:{0,1} * →Z q .

5. The semantic search method based on dense knowledge graph according to claim 1, characterized in that: In step S4, the edge server organizes the collected data into a knowledge graph and corresponding indexes, encrypts them, and backs them up to the cloud server, which specifically includes the following steps: S41: Data processing: Integrate various types of data, extract entities, attributes and relationships between entities, and process the data into a key-value pair structured attribute graph for storage; S42: Index generation: sort the key-value pair data by key and generate num values ​​in order. <k i ,v i ,num i >triplets, and using each <k i ,v i ,num i >k in i Generate partial index EInd i =MAC(Ind xi ,1); then define the random permutation π:[n]→[n], for each <k i ,v i ,num i > i generate Storage, forming the final index structure DInd i ={EInd i ,Enum i } for storage; S43: Data encryption: key-value pair data node generated after data processing i =(k i ,v i ) is encrypted and stored to form a ciphertext c, and all elements are padded to the same length; where j∈J,J∈[1,|m|], m=(k1,k2,...k i ) is a key-value pair information library; 6. The semantic search method based on dense knowledge graph according to claim 1, characterized in that: The step S5 specifically comprises the following steps: S51: Trapdoor generation: The authorized user sends an authorization credential and a query request to the nearest edge server; first, the edge server verifies the user based on the user's authorization credential Ac. Is it in the authorized user list list? If The edge server extracts the keywords in the user query request through natural language processing and generates the corresponding query trap as ET i ; S52: Search matching: The edge server compares the query keyword set {w q1 ,...,w qn } by the keyword w q1 The generated trapdoor Eτ1=MAC(td x1 ,1) and the stored index EInd i If the two are equal, the index structure DInd i ={EInd i ,Enum i }Find the corresponding num i =π -1 (j) value as a pointer to locate the corresponding key-value pair (ck j ,cv j ); will (ck j ,cv j ) as the starting node to retrieve the one-hop subgraph to obtain the node corresponding to w q1 The set of directly related nodes and edges S1 = (V, R); Then in the initial search result S1, according to w q2 Filter out the node and edge set S2 = (V', R') that matches it, and so on, filter out the nodes and edges that match the subsequent query keyword {w q3 ,...,w qn } matches the set of nodes and edges, if the qi ∈{w q2 ,...,w qn The corresponding ciphertext trapdoors generated by} all satisfy Eτ i ∈{Eτ2,...,Eτ n } and {Eτ2,...,Eτ n }∈EInd, then we finally get q1 ,…,w qn } Matching nodes and corresponding edge set S n =(V * ,R * ); V * ={(ck1,cv1),…,(ck n ,cv n )},n≤m S53: Data decryption: The retrieved data S n =(V * ,R * ) is decrypted on the edge server, and a new information subgraph SubG is constructed based on the decrypted nodes and edges and returned to the user.

7. The semantic search method based on dense knowledge graph according to claim 6 is characterized in that: In the key-value mode, the one-hop subgraph query retrieves the attribute value corresponding to the entity through the entity identifier, and retrieves the edges and attribute values ​​of the edges related to the entity to return all related nodes: V={(ck1,cv1),...,(ck m ,cv m )}。

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