CN113254743B - Security semantic perception searching method for dynamic spatial data in Internet of vehicles - Google Patents
Security semantic perception searching method for dynamic spatial data in Internet of vehicles Download PDFInfo
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Abstract
The invention discloses a security semantic perception searching method for dynamic spatial data in a vehicle networking, which comprises the steps of establishing an encryption position index structure for a standard data set, encrypting the data set, sending the encryption index structure and a ciphertext data set to a public cloud server, carrying out main body training on a legal vehicle user given query keyword set w by a vehicle by adopting a theme model to obtain a query theme vector, generating a position search token and a theme probability search token by the vehicle by utilizing an encryption key and query information, searching the encryption position index structure through the position search token and the theme probability search token, carrying out matching calculation on search results to obtain a matching set, obtaining a result set after sequencing, and sending plaintext information of the result set to the vehicle by the private cloud server.
Description
Technical Field
The invention relates to the field of information security, in particular to a secure semantic perception searching method for dynamic spatial data in a vehicle networking.
Background
Location-based services, such as Spatial Key Search (SKS), are becoming increasingly common in many different applications, from social media to vehicle ad hoc networks (VANET). In VANET, communication between high speed vehicles relies on board units (OBUs; complex modules including wireless communication, embedded systems, sensors and positioning systems) and roadside units (RSUs). The dynamic nature of VANET (e.g., fast-moving vehicles) exacerbates the challenges of designing and implementing a secure and efficient SKS scheme, e.g., due to frequent communications, data explosion, and insufficient coverage between high-speed moving vehicles and other devices/systems in the infrastructure, and one widely used approach to addressing constraints in cellular networks is to send search queries and retrieve content from RSUs. Although the network link between the RSU and the vehicle has a relatively high bandwidth and low latency, it requires the vehicle to continuously transmit messages through the RSU. Since RSUs are physically exposed, they are vulnerable to a wide range of attacks.
The RSU and SKS service providers may not be fully trusted by the vehicle user. Since the search query from the vehicle user contains private information such as real-time coordinates and search preferences, any privacy disclosure of the query may have undesirable consequences. For example, a user is searching for a place that offers accommodation, rather than a restaurant, and the leakage of such a search may affect the privacy of the user. Forward privacy is also a basic requirement in SKS schemes that support dynamic updates. For example, a file injection attack is a typical attack that is applicable to dynamic schemes. In such an attack, an adversary may forge some data and send the forged data to the data owner. After being encrypted by the data owner, the ciphertext of the forged data is outsourced to a service provider for storage. By analyzing previous queries in conjunction with newly inserted data, an adversary may be able to recover some of the outsourced data and queries. This requires that the SKS scheme for VANET must be designed with both search efficiency and privacy protection in mind.
Searchable Encryption (SE) may be used to implement a privacy preserving spatial key search in VANET. However, many existing SE schemes focus only on security key searches on outsourced documents, without regard to scope limitations. In addition, the mobility of the vehicle user requires a faster and more efficient search process because the vehicle may be out of coverage in minutes when the vehicle is traveling at high speeds (e.g., 80 miles per hour). The public key cryptosystem scheme is too time consuming to provide real-time search results. Therefore, it remains challenging how to return top-level results that satisfy conditions such as the vehicle user's search intent and query scope constraints in a persistent, private manner.
Disclosure of Invention
Aiming at the defects of the searchable encryption technology in the vehicle-mounted ad hoc network, the invention provides a secure semantic perception searching method for dynamic spatial data in the vehicle-mounted ad hoc network, which solves the contradiction between semantic perception searching efficiency and privacy protection in the prior art and increases the limit of considering the geographical position range.
The invention is realized by the following technical scheme:
a secure semantic perception search method for dynamic spatial data in the Internet of vehicles comprises the following steps:
step 1, a private cloud server generates an encryption key for a data set D;
step 2, performing theme training on the data set D0 by adopting the trained LDA theme model to obtain a theme deflection vector of a shop corresponding to each record, and converting merchant descriptions in the data set D0 into theme deflection vectors to obtain a standard data set D;
step 3, the private cloud server establishes an encrypted position index structure for the standard data set D, encrypts the data set D at the same time, and sends the encrypted position index structure and the ciphertext data set to the public cloud server;
step 4, the vehicle adopts an LDA topic model to carry out main body training on a legal vehicle user given query keyword set w to obtain a query topic vector, and the vehicle generates a position search token and a topic probability search token by using an encryption key and query information;
step 5, the public cloud server searches the encrypted position index structure through the position search token to obtain a suspected area set of suspected search results, then screens the suspected area set through the position search token and the theme probability search token, calculates the matching degree of the screened results to obtain a matching set P, sorts the matching set P according to the matching degree scores, and forms the first k results into a result set;
and 6, the private cloud server sends the plaintext information of the result set to the vehicle.
Preferably, in step 2, the private cloud server performs theme training on the keyword array od in each record o 'in the D0 set according to the trained LDA theme model to obtain a theme bias vector of a shop corresponding to o', and updates the keyword array in each record in the data set D0 to obtain an updated data set D.
Preferably, the private cloud server in step 3 adopts an R tree structure, and establishes a position index structure for the data set D according to the geographic information ol
Position indexing structure
The node in the system is encrypted to obtain an encrypted position index structure
Preferably, the encryption method of the position index structure is as follows:
the private cloud server vectorizes each node to obtain a vector set, and encrypts the vector set by adopting an asymmetric scalar product order-preserving encryption algorithm to obtain an encrypted position index structure
Preferably, in step 4, legal vehicle users give a query keyword set w and a query range v l The vehicle requests the trained LDA theme model and the encryption key K from the private server;
vehicle query key pair using LDA topic modelPerforming topic training on the word set w to obtain a query topic vector v ψ Vehicle-use Key K, query Range v l And query topic vector v ψ Generating a vehicle to location lookup token T L And topic probability lookup token T N And sending the data to a public cloud server for searching.
Preferably, the method for generating the location finding token is as follows:
vehicle user pair query range v l And disturbing, carrying out vectorization processing to obtain a vector set, and encrypting the vector set by adopting an asymmetric scalar product order-preserving encryption algorithm ASPE to obtain the position search token.
Preferably, the method for generating the topic probability lookup token is as follows:
query subject vector v by adopting asymmetric scalar product order-preserving encryption algorithm ASPE ψ And encrypting to obtain the subject probability search token.
Preferably, the method for searching the suspected area set in step 5 is as follows:
and starting searching from a root node of the encryption position index structure, comparing the inner product of the current node and the area search token, determining whether the node comprises a suspected area, and traversing child nodes of the node if the node comprises the suspected area to obtain a suspected area set.
Preferably, the method for obtaining the matching set P is as follows:
and judging the geographic position of each node in the suspected area set by using the position search token, if the node meets the searched geographic requirement, calculating the matching degree, performing matching degree score calculation on the theme deviation vector in the node by using the theme probability search token, then sequencing the id of the nodes according to the matching degree score, selecting the id of k nodes before ranking, forming a result set, and returning the result set to the vehicle.
Preferably, after the vehicle obtains the result set in step 6, the plaintext information of the result set is requested to the private cloud through the encryption channel, and after the private cloud server receives the result set, the plaintext information corresponding to id in the result set is searched from the data set D to form a plaintext result set G which is sent to the vehicle.
Compared with the prior art, the invention has the following beneficial technical effects:
the invention provides a security semantic perception searching method of dynamic spatial data in the internet of vehicles,
according to the method for searching dynamic spatial data in the internet of vehicles through secure semantic perception, the R tree index is established for the data set through the geographic position of the object, and the process of searching suspected objects in the searching process is accelerated. Potential semantic features are extracted from keywords of the object description and the query based on the LDA topic model, and text similarity is measured through the extracted semantic features. Semantic awareness functionality of the query is provided. The nodes and topic bias vectors in the R-tree are encrypted using the ASPE encryption algorithm. The encryption algorithm ensures that the operation between the encrypted vectors has the same size characteristics as the operation between the plaintext vectors. The encryption algorithm can ensure the security of data on one hand and realize the functions of searching and matching information on the basis of encrypting information on the other hand.
And constructing a data index structure by using the R tree. The R tree structure facilitates the storage of the object index and simultaneously makes the search on the geographic position more efficient; corresponding algorithms are also designed to prune non-leaf nodes and speed up the process of searching potential leaf nodes.
Abandoning the encryption keyword matching method and adopting an LDA model. The textual description of the object and the query keywords are converted into topic bias vectors using an LDA model. The similarity between objects and queries can be measured by calculating the similarity of their topic bias vectors. The semantic search capability is fully improved, the search process is changed from a text matching process to a vector calculation process, and the search speed is greatly increased.
And encrypting the geographic position vector and the theme deviation vector by adopting an ASPE (asymmetric scalar product order preserving encryption algorithm) method, so that the characteristics of a calculation result after the vector is encrypted are not influenced. On one hand, confidentiality of plaintext data is guaranteed through encryption, and on the other hand, operational characteristics of the encrypted vector are effectively maintained, so that effective searching can be conducted on encrypted data. Therefore, data safety of index structure information and a data set is effectively protected, privacy leakage caused by vehicle searching is reduced to the maximum extent, and data safety under the outsourced data searching scene is effectively solved.
Drawings
FIG. 1 is a schematic diagram of a vehicle locating merchant system for use with the present invention;
FIG. 2 is a flowchart of the secure semantic perception search method of dynamic spatial data in the Internet of vehicles according to the present invention.
Detailed Description
The present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which the invention is shown by way of illustration and not by way of limitation.
Referring to fig. 1, the system of the invention takes a vehicle lookup merchant as an example, and comprises three entities, a trusted private cloud server, a public cloud server and a vehicle user.
The private cloud server trains a model, generates a theme vector and an auxiliary vector, and establishes an R tree index, encrypted data and an index structure; the public cloud server provides storage and retrieval service for the ciphertext data; the vehicle user is responsible for initiating the query request and generating the query token.
Referring to fig. 2, a method for searching dynamic spatial data in the internet of vehicles through secure semantic perception comprises the following steps:
step 1: the private cloud server generates an encryption key K for the data set D by adopting an ASPE encryption algorithm;
wherein, K = { M1, M2}, M1, M2 are random n × n reversible matrices, and n is the number of topics trained by the LDA topic model.
And 2, performing theme training on the data set D0 by adopting the trained LDA theme model to obtain a theme deflection vector of a store corresponding to each record, and converting merchant descriptions in the data set D0 into the theme deflection vector to obtain a standard data set D.
The specific implementation of this step is as follows:
2.1 Given a data set D0 containing a plurality of records o ', each record o' = { id, o = {, o l ,o d Is composed of 3Dimension composition, id is the identity number of the store corresponding to each record, o l Geographical location information, o, indicating the store to which the record corresponds l ＝{o x ，o y In which o is x Indicates the longitude, o, of the store y Representing the latitude; o. o d Is a set of arrays describing the store keywords, for example: fruits, food, etc., of variable length.
2.2 ) the private cloud server sets a keyword array o in each record o' in the D0 set according to the trained LDA topic model d Subject training is carried out to obtain a subject deviation vector o of a shop corresponding to the subject deviation vector o ψ ＝{p 1 ，…，p n N is the number of topics trained by the LDA model, e.g. 10, which is a fixed value.
O' = { id, o in each record in D0 l ,o d Update to o = { id, o = l ,o ψ }. Record o in all d d After the replacement, the new data set is obtained as D.
Step 3, the private cloud server establishes an encrypted index structure for the data set D
And encrypts the data set and constructs an encryption index
And sending the ciphertext data set C to a public cloud server;
the steps are realized as follows:
3.1 ) the private cloud server establishes a position index structure for the data set D, and adopts an R tree structure according to the geographic information o l Building an R-Tree index for dataset D
Namely a position index structure, and each leaf node stores all store information id contained in the area;
3.2 ) the private cloud server encrypts the nodes in the R-tree index to obtain an encrypted R-tree index
The specific steps for encrypting the R tree index are as follows:
the encryption of the R tree structure is to encrypt each node information mi in the R tree,
in order for mi to be in the longitudinal range,
is the range of mi in latitude, i is more than or equal to 1 and less than or equal to R, and R is the number of nodes in the whole R tree. The mi is encrypted as follows:
3.2.1.1 ) private cloud server vectorizes mi to obtain vector set
Wherein
W1, \8230;, (j = n-4), which is a random number between (0, 1), is an extended portion of the vector with a vector length of n.
3.2.1.2 ASPE pairs using asymmetric scalar product order-preserving encryption algorithm
And (3) encryption:
get the encryption(Vector)
The vector length is n. Encrypting mi as
3.2.2 ) all nodes of the R-tree are encrypted to obtain an encrypted R-tree index
I.e. the encryption position index structure.
3.3 The private cloud server encrypts data in the data set D to obtain an encrypted data set C;
the specific steps for encrypting the data set D are as follows:
3.3.1 Take out every record in the data set, proceed to a single record o = { id, o = } l ,o ψ And (5) encrypting. The specific steps for encrypting a single record are as follows:
3.3.1.1 To a single record of o in o l ＝{o x ，o y Encrypting, specifically comprising the following steps:
3.3.1.1.1 ) private cloud server pair o l Vectorizing to obtain a vector set
3.3.1.1.2 ASPE pairs using asymmetric scalar product order-preserving encryption algorithm
And (3) encryption:
obtaining an encrypted vector
The vector length is n. Encryptiono l Is composed of
3.3.1.2 O to a single record o ψ ＝{p 1 ，…，p n It is encrypted. ASPE (asynchronous sequence preserving encryption) pair o adopting asymmetric scalar product order preserving encryption algorithm ψ And (3) encryption:
obtaining an encrypted vector
The vector length is n.
3.3.2 O in each record for dataset D Total l ,o ψ After being encrypted, obtain
And
replacing o in data set D l ,o ψ Obtaining an encrypted data set C, in which the recorded data is
3.4 ) private cloud server encryption index
And sending the ciphertext data set C to a public cloud server;
and 4, step 4: legal vehicle user given query keyword set w and query range v l The vehicle requests the trained LDA topic model and the encryption key K from the private server, and the vehicle utilizes the key K and the query information { v l ，v ψ } generate a vehicle to location lookup token T L And topic probability lookup token T N Vehicle will find the location token T L And topic probability lookup token T N And sending the data to a public cloud server for searching.
The specific implementation of this step is as follows:
4.1 Legal vehicle users give a set of query keywords w = { q1, q 2.. Qt } and a query range v l ＝{[x l ，x r ]，[y l ，y r ]}. Wherein q1, q2,. Qt are keywords, for example: spicy soup, cate food, night, etc. [ x ] l ，x r ]Is the range of the longitude of the looking merchant, [ y l ，y r ]Is to find a range of the merchant's latitude.
4.2 Vehicle requests trained LDA topic model and encryption key K = { M) from private server 1 ,M 2 }；
4.3 Vehicle carries out topic training on a query keyword set w by utilizing an LDA topic model to obtain a query topic vector v ψ ＝{p 1 ，…,p n }；
4.4 Vehicle utilization key K and query information { v } l ，v ψ } generate a location lookup token T L And topic probability lookup token T N 。
The method comprises the following specific steps:
4.2.1 Generate location lookup token T L ：
The method comprises the following specific steps:
4.2.1.1 Vehicle user to query range v l Disturbing and vectorizing to obtain a vector set
Where δ and δ' are large positive numbers of randomly generated perturbations.
4.2.1.2 ASPE pairs using asymmetric scalar product order-preserving encryption algorithm
And (3) encryption:
obtaining an encrypted vector
Having a vector length of n, wherein
Is M 1 ，M 2 The inverse matrix of (c). Obtaining location lookup tokens
4.2.2 Generate topic probability lookup token T N . ASPE pair v adopting asymmetric scalar product order-preserving encryption algorithm ψ And (3) encryption:
obtaining an encrypted vector
The vector length is n. Finding a topic probability lookup token
4.2.3 Vehicle will location lookup token T L And topic probability lookup token T N And sending the data to a public cloud server for searching.
And 5: public cloud server searches token T through position L Slave encryption index structure
The suspected area set e _ M of the possible suspected result is obtained through searching, and the public cloud server searches the token T through the position L And topic probability lookup token T N And screening merchants contained in the suspected area set e _ M, and calculating the matching degree to obtain a matching set P. And sequencing the P by the public cloud server according to the matching degree score to obtain a TopK (first K) result set Pk and sending the TopK result set Pk to the vehicle.
The method comprises the following specific steps:
5.1 Public cloud server looks up token T by location L Slave encryption index structure
The steps of searching to obtain a suspected area set e _ M are as follows, 5.1.1) -5.1.4):
5.1.1 ) encryption index structure
The root node of (a) starts the lookup. Enqueue the root node into compare queue B.
5.1.2 Take out the head node e _ m from the comparison queue B, calculate the current node
And a region search token
The inner products of the corresponding vectors of (a) and (b) are compared,
inner product of corresponding vectors:
5.1.3 If r) is x ＜0and r y < 0: and (4) proving that the current node area contains the suspected area, and if the current node is a leaf node, adding the leaf node into the suspected area set. Otherwise, the child nodes of the current node are all enqueued in the comparison queue B.
If r is not satisfied x ＜0and r y If < 0, the node is discarded.
Execution continues at step 5.1.2. Until the comparison queue B is empty
5.1.4 Comparison queue B is empty and all regions are searched. A set of areas e _ M = { e _ mj, \8230; } containing suspect results is obtained, where j represents the index of the suspect area.
5.2 Public cloud server looks up token T by location L And topic probability lookup token T N From being suspected to beAnd screening the region set e _ M, and calculating the matching degree to obtain a matching set P.
The method comprises the following specific steps:
5.2.1 Id of all shops in the suspected area in the set e _ M is extracted to form a suspected target set e _ ids = { i, j, \8230; }, i, j \8230, which is the id value of a possible result.
5.2.2 Extract the store information corresponding to id in e _ ids from the encrypted data set
Carrying out geographic position judgment and matching degree score calculation on the obtained data;
the method comprises the following specific steps:
5.2.2.1 Location determination, using location lookup tokens
For shop
Performing a geographic location
And (6) judging. Calculating the inner product of the corresponding vectors:
if r is x ＜0and r y If the number is less than 0, the node is proved to meet the geographical requirement of searching, and matching degree calculation is carried out. If not, discarding and continuing to search the next.
5.2.2.2 Match score calculation. Finding tokens by topic probabilities
To store
Subject probability vector in (1)
And (3) carrying out matching degree score calculation:
5.2.3 And) after screening and matching degree score calculation are carried out on the objects o in the suspected target set e _ ids, the ids are sorted according to the matching degree score.
5.2.4 TopK interception is carried out on the sorted results, id of k before score ranking is selected to form a query result set RES, and the query result set RES is returned to the vehicle.
And 6: after the vehicle obtains the TopK result set RES, plaintext information of the result set RES is requested to the private cloud server through the encryption channel, and after the private cloud server receives the result set, the plaintext information of each element id of the set is searched from the data set D to form a plaintext result set which is sent to the vehicle.
The method not only considers the text matching degree between the vehicle query and the merchant description, but also adds the limitation to the geographical range. Efficient and safe searching of merchants is realized from the aspects of geographic position and semantic matching.
Second, a data index structure is constructed using the R-tree. The R tree structure facilitates the storage of the object index and simultaneously makes the search on the geographic position more efficient; corresponding algorithms are designed to prune non-leaf nodes and accelerate the process of searching potential leaf nodes.
In addition, the method of matching the encryption keywords is abandoned, and text description and query keywords of the object are converted into topic deviation vectors by using an LDA model. The similarity between objects and queries can be measured by calculating the similarity of their topic bias vectors. The semantic search capability is fully improved, the search process is changed from a text matching process to a vector calculation process, and the search speed is greatly increased.
And finally, encrypting the geographic position vector and the theme deviation vector by adopting an ASPE (asymmetric scalar product order preserving encryption algorithm) method, so that the characteristics of a calculation result after the vector is encrypted are not influenced. On one hand, confidentiality of plaintext data is guaranteed through encryption, and on the other hand, operational characteristics of the encrypted vector are effectively maintained, so that effective searching can be conducted on the encrypted data. Therefore, the data security of the index structure information and the data set is effectively protected, privacy disclosure brought by vehicle search is reduced to the maximum extent, and the data security under the outsourcing data search scene is effectively solved.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (7)
1. A secure semantic perception search method for dynamic spatial data in the Internet of vehicles is characterized by comprising the following steps:
step 1, a private cloud server generates an encryption key for a standard data set D;
step 2, performing theme training on the data set D0 by adopting the trained LDA theme model to obtain a theme deviation vector of a shop corresponding to each record, and converting merchant descriptions in the data set D0 into the theme deviation vector to obtain a standard data set D;
step 3, the private cloud server establishes an encrypted position index structure for the standard data set D, encrypts the standard data set D at the same time, and sends the encrypted position index structure and the ciphertext data set to the public cloud server;
step 4, the vehicle adopts an LDA topic model to carry out main body training on a given query keyword set w of a legal vehicle user to obtain a query topic vector, and the vehicle generates a position search token and a topic probability search token by using an encryption key and query information;
the legal vehicle user gives a query keyword set w and a query range v l The vehicle requests the trained LDA theme model and the encryption key K from the private cloud server;
the vehicle carries out theme training on the query keyword set w by utilizing the LDA theme model to obtain a query theme vector v ψ Vehicle using key K, inquiry range v l And query topic vector v ψ Generating vehicleVehicle location finding token T L And topic probability lookup token T N And sending the data to a public cloud server for searching;
the position search token is generated as follows:
vehicle user pair query range v l Disturbing, vectorizing to obtain a vector set, and encrypting the vector set by adopting an asymmetric scalar product order-preserving encryption Algorithm (ASPE) to obtain a position search token;
the generation method of the theme probability search token comprises the following steps:
query subject vector v by adopting asymmetric scalar product order-preserving encryption algorithm ASPE ψ Encrypting to obtain a theme probability search token;
step 5, the public cloud server searches the encrypted position index structure through the position search token to obtain a suspected area set of suspected search results, then screens the suspected area set through the position search token and the theme probability search token, calculates the matching degree of the screened results to obtain a matching set P, sorts the matching set P according to the matching degree scores, and forms the first k results into a result set;
and 6, the private cloud server sends the plaintext information of the result set to the vehicle.
2. The method for secure semantic-aware searching of dynamic spatial data in the internet of vehicles according to claim 1, wherein in step 2, the private cloud server performs keyword array o in each record o' in the data set D0 set according to the trained LDA topic model d And performing theme training to obtain theme deviation vectors of the stores corresponding to the o', updating the theme deviation vectors by the keyword groups in each record in the data set D0, and obtaining an updated standard data set D.
3. The method for secure semantic perception search of dynamic spatial data in the internet of vehicles according to claim 1, wherein the private cloud server in step 3 adopts an R tree structure and searches according to geographic information o l Establishment for a standard data set DPosition index structure
Position indexing structure
The node in (1) is encrypted to obtain an encrypted position index structure
4. The method for searching the dynamic space data in the internet of vehicles through secure semantic awareness according to claim 3, wherein the position index structure is encrypted as follows:
the private cloud server vectorizes each node to obtain a vector set, and encrypts the vector set by adopting an asymmetric scalar product order-preserving encryption algorithm to obtain an encrypted position index structure
5. The method for searching for dynamic spatial data in the internet of vehicles according to claim 1, wherein the method for searching for the suspected region set in step 5 is as follows:
and starting searching from a root node of the encryption position index structure, comparing the current node with the inner product of the area search token, determining whether the node comprises a suspected area, and traversing child nodes of the node if the node comprises the suspected area to obtain a suspected area set.
6. The method for searching dynamic spatial data in the internet of vehicles according to claim 1, wherein the method for obtaining the matching set P is as follows:
and judging the geographic position of each node in the suspected area set by using the position search token, if the node meets the searched geographic requirement, calculating the matching degree, performing matching degree score calculation on the theme deviation vector in the node by using the theme probability search token, then sequencing the id of the nodes according to the matching degree score, selecting the id of k nodes before ranking, forming a result set, and returning the result set to the vehicle.
7. The method for searching for dynamic spatial data in the internet of vehicles according to claim 6, wherein in step 6, after the vehicle obtains the result set, the vehicle requests the private cloud server for plaintext information of the result set through an encrypted channel, and after receiving the result set, the private cloud server searches for plaintext information corresponding to id in the result set from the data set D to form a plaintext result set G and sends the plaintext result set G to the vehicle.
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