CN117216781A - Searchable encryption method and system for semantic keywords, electronic equipment and medium - Google Patents
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Abstract
The invention discloses a searchable encryption method, a searchable encryption system, a searchable encryption electronic device and a searchable encryption medium for semantic keywords, wherein the method comprises the following steps: acquiring document data to be encrypted at a data owner side, and encrypting the document data to obtain an encryption index and a ciphertext file which are in one-to-one correspondence; acquiring a search request instruction sent by a data user terminal; matching the encryption index according to the search request instruction; transmitting the ciphertext file corresponding to the matched encryption index to a data user side; and at the data user end, decrypting the ciphertext file to obtain the document data. The technical scheme provided by the invention can solve the technical problem that the searchable encryption scheme in the prior art needs a larger storage space.
Description
Technical Field
The invention relates to the technical field of data encryption, in particular to a semantic multi-keyword search encryption method, a semantic multi-keyword search encryption system, electronic equipment and a semantic multi-keyword search encryption medium.
Background
With rapid development of cloud computing and cloud storage, data security issues are becoming more important.
In recent years, researchers have commonly used the TF-IDF model to generate document vectors and security indexes in the ciphertext state to construct searchable encryption schemes that support various forms.
However, the existing TF-IDF model is based on extracting keywords from all files, and then constructing a security index according to the extracted keywords, which results in a huge number of keywords in the dictionary set and requires a large storage space.
There is therefore a need for a searchable encryption scheme to reduce the use of storage space.
Disclosure of Invention
The invention provides a semantic multi-keyword search encryption method, a semantic multi-keyword search encryption system, electronic equipment and a semantic multi-keyword search encryption medium, and aims to effectively solve the technical problem that a searchable encryption scheme in the prior art needs a large storage space.
According to a first aspect of the present invention, the present invention provides a semantic keyword search encryption method, including: acquiring document data to be encrypted at a data owner side, and encrypting the document data to obtain an encryption index and a ciphertext file which are in one-to-one correspondence; acquiring a search request instruction sent by a data user terminal; matching the encryption index according to the search request instruction; transmitting the ciphertext file corresponding to the matched encryption index to a data user side; and decrypting the ciphertext file at the data user side to obtain document data.
Further, encrypting the document data includes: encrypting the document data by using the pre-acquired key to obtain an encrypted document set; extracting topics of the document data to obtain a topic set, wherein topics in the topic set correspond to documents in the encrypted document set one by one; and encrypting the theme set to obtain an encryption index.
Further, the step of extracting the topics of the document to obtain a topic set includes: performing topic extraction on the data document by using a pre-trained BTM topic model; processing the extracted topics by using a Gibbs sampling method to obtain a topic-keyword relevance distribution matrix and a document-topic relevance distribution matrix; respectively calculating confusion degree and consistency parameters of the topic-keyword relevance distribution matrix and the document-topic relevance distribution matrix; extracting the optimal topic-keyword probability distribution matrix from all topics of the data document by using the topic-keyword correlation distribution matrix, the confusion degree and the consistency parameter of the document-topic correlation distribution matrix; designating the number of topics in the corpus according to the confusion degree and consistency parameters; and extracting topics from the optimal topic-keyword relevance distribution matrix according to the number of topics in the specified library to obtain a topic set.
Further, the step of encrypting the theme set includes: extracting keyword probability distribution vectors of each topic from the topic-keyword relevance distribution matrix by utilizing a pre-trained BTM topic model, constructing t nodes corresponding to t topics, and setting the t nodes as leaf nodes; constructing a balanced tree index D by using t leaf nodes; expanding the dimension of all vectors in the balance tree index D to (m+j+2) -bit, and calculating the index vector of the balance tree index Index vector->The calculation method of (1) comprises the following steps: index vector +.>(m+j+1) th The bit dimension is set to a random numberObtain->Wherein d represents the number of virtual words added, m is the size of the array, (m+j+2) th -bit is set to 1; index vector +.>Split into two vectors->The segmentation method comprises the following steps: judging whether the value of each dimension of the 0/1 split vector S is 1, if so, the value is 1> Otherwise will D' i ＝D″ i Wherein r represents a randomly selected random number; using { M ] 1 ，M 2 Encrypting the sub-vectors in the balanced tree index to obtainWherein I is 1，i For encrypting index { M 1 ，M 2 And two (m+d+2) x (m+d+2) invertible matrices.
Further, the step of encrypting the theme set includes: obtaining a topic probability distribution vector of an ith document from a document-topic relevance distribution matrix; constructing a theme-document inverted index; and encrypting the inverted index by using a symmetric key to generate a theme document security index.
Further, the search request instruction sent by the data acquisition user includes: after the data user side sends a search request, extracting keywords in the search request; encrypting the keywords and generating a search trapdoor; the search trapdoor is sent to a data user side; using the data user terminal to reinitiate a search request by using a search trapdoor; and receiving a search trapdoor sent by the data user as the search request instruction.
Further, the said pair of keyThe step of encrypting the word and generating a search trapdoor comprises the steps of: pseudo-randomly transforming keywords to generate virtual keywordsAnd at->Insert set->Generating a set Q i The method comprises the steps of carrying out a first treatment on the surface of the Set Q by pre-trained BTM topic model i Generating an m-bit query topic vector; random at->V keywords are selected from the d virtual keywords, the corresponding positions of the v keywords are set to be 1, and the v keywords are scaled by using a random number, so that the dimension of the v keywords is expanded to be (m+j+2); calculating a pre-acquired CL j Average with NWhere n= { n=1, 2, … } represents the number of the keywords, CL j Is a confusion parameter, which is the security level of the document collection stored in the CS; by CL j N and μ, calculate variance +.>Using the variance sigma pair ++>Dividing to obtain->The segmentation method comprises the following steps: inquiry->Each element of (2)If the value of each dimension of the 0/1 split vector S is 0, let +.> No-> And->Searching trapdoors; using { M ] 1 ，M 2 Encrypting the search trapdoor to obtain an encrypted trapdoor, wherein the encryption method comprises the following steps: />{M 1 ，M 2 And two (m+d+2) x (m+d+2) invertible matrices.
Further, the step of matching the encryption index includes: calculating the similarity between the encryption index and the encryption trapdoor by utilizing a pre-constructed cosine function; and if the similarity reaches a preset threshold, sending the ciphertext file corresponding to the encryption index and the theme label corresponding to the encryption index to a data user side.
Further, the step of decrypting the ciphertext file includes: analyzing the theme label to obtain gamma i And MAC K (W i ，v i ) Wherein, gamma i And MAC K (W i ，v i ) The tandem value of (a) represents the tag domain, gamma i ＝Enc sk (v i )，v i A vector value indicating whether or not it appears in each file, enc indicating an encryption algorithm; computing Dec for each tag using Dec function sk (γ i ) Obtaining the productTo v i Calculation using MAC function againWill calculate +.>And MAC K (W i ，v i ) Comparing, if the two are the same, performing the next verification, otherwise directly refusing the decryption operation; the step of next verification includes: calculation of v=v l ^v 2 ^v 3 ^…^v N Wherein, a represents a placeholder modifier character; and (3) checking whether the corresponding positions in the v are all 1, if yes, verifying successfully, and executing decryption operation on the ciphertext file, otherwise, directly refusing the decryption operation.
According to a second aspect of the present invention, the present invention also provides a semantic keyword search encryption system, including: the data encryption module is used for acquiring document data which needs to be encrypted by a data owner side and encrypting the document data to obtain an encryption index and a ciphertext file which are in one-to-one correspondence; the instruction acquisition module is used for acquiring a search request instruction sent by the data user terminal; the index matching module is used for matching the encryption index according to the search request instruction; the file transmission module is used for transmitting the ciphertext file corresponding to the matched encryption index to the data user side; and the data decryption module is used for decrypting the ciphertext file at the data user end to obtain document data.
According to a third aspect of the present invention, there is also provided an electronic device comprising: the system comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, and is characterized in that the semantic keyword search encryption method of any one of the above is realized when the processor executes the computer program.
According to another aspect of the present invention, there is further provided a computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the semantic keyword search encryption method of any one of the above.
Through one or more of the above embodiments of the present invention, at least the following technical effects can be achieved:
in the technical scheme disclosed by the invention, the document data which needs to be encrypted at the data owner side are encrypted, and all the document data do not need to be encrypted, so that the extraction quantity of keywords is reduced, and the storage space is saved.
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The technical solution and other advantageous effects of the present invention will be made apparent by the following detailed description of the specific embodiments of the present invention with reference to the accompanying drawings.
FIG. 1 is a flowchart of a semantic keyword search encryption method provided by an embodiment of the present invention;
FIG. 2 is a frame diagram of a semantic keyword search encryption system provided by an embodiment of the present invention;
fig. 3 is a schematic block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to fall within the scope of the invention.
In the description of the present invention, it should be noted that, unless explicitly specified and defined otherwise, the term "and/or" herein is merely an association relationship describing associated objects, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. The character "/" herein generally indicates that the associated object is an "or" relationship unless otherwise specified.
With the rapid development of cloud computing and cloud storage, more and more enterprises or users often choose to outsource their private data documents to the cloud in order to save high maintenance costs of software and hardware, but how to protect data security becomes a difficult problem. In order to guarantee data privacy, before outsourcing, the data needs to be outsourced in an encrypted form, but how to efficiently retrieve ciphertext data becomes a new problem. In response to this difficulty, a searchable encryption technique has evolved, which is a technique that allows users to efficiently retrieve ciphertext data stored on a cloud server without decryption.
In recent years, researchers have commonly used TF-IDF models based on vector space models and secure KNN to generate document vectors and secure indexes in ciphertext states to construct searchable encryption schemes that support various forms, such as: single keyword searches, multi-keyword searches, fuzzy keyword searches, and connective keyword searches, among others. The TF-IDF model is built based on a Bow model, uses term frequency and inverse document frequency to measure the relevance between keywords and documents, and uses relevance scores to represent inner products between vectors, however, the scheme built based on the TF-IDF ignores the semantics of the keywords in different files, so that a difference exists between a search result and a semantic intention request of a user. In addition, most of the existing searchable encryption schemes based on TF-IDF use extracting keywords from all files, and then construct a security index according to the extracted keywords, resulting in a huge number of keywords in a dictionary set and extremely high dimensionality and sparsity of generated vectors, so that the existing searchable encryption schemes are not ideal in terms of search time and space costs.
Based on the method, the system, the electronic equipment and the medium, the application provides a semantic keyword searchable encryption method, a semantic keyword searchable encryption system and an electronic equipment and medium, and at least the occupation of space can be reduced.
Fig. 1 shows a semantic keyword search encryption method provided by an embodiment of the present application, including:
s101, acquiring document data to be encrypted at a data owner side, and encrypting the document data to obtain an encryption index and a ciphertext file which are in one-to-one correspondence;
s102, acquiring a search request instruction sent by a data user terminal;
s103, matching encryption indexes according to the search request instruction;
s104, transmitting the ciphertext file corresponding to the matched encryption index to a data user side;
s105, decrypting the ciphertext file at the data user side to obtain the document data.
In this embodiment, five entities may be scheduled to implement the above steps, where the five entities are respectively: the data service center is responsible for processing and encrypting, namely, the data service center executes step S101, and then the data service center sends the generated encryption index and ciphertext file to the cloud server and shares the generated security key with the cloud server. After the cloud server monitors that the data user terminal initiates the search request, that is, after the cloud server executes step S102, the cloud server continues to execute step S103 and step S104, and sends the ciphertext file to the data user terminal, the data user terminal executes the decryption operation locally, that is, executes step S105 at the data user terminal.
Because the semantic keyword searching and encrypting method provided by the embodiment encrypts the document data which needs to be encrypted at the data owner side in the searching and encrypting process, all the document data do not need to be encrypted, the keyword extraction quantity is reduced, and the storage space is saved.
In one embodiment, encrypting the document data includes:
encrypting the document data by using the pre-acquired key to obtain an encrypted document set;
extracting topics of the document data to obtain a topic set, wherein topics in the topic set correspond to documents in the encrypted document set one by one;
and encrypting the theme set to obtain an encryption index.
In this embodiment, the data owner and the trust center together generate a five-tuple security Key key= { K, S, CL j ，m 1 ，m 2 }. Wherein K is a symmetric key of an encryption subject document and is generated by a data owner side; s is a (m+d+2) -bit 0/1 partition vector, and the value of each dimension can only be 0 or 1; m is m 1 ，m 2 Is two (m+d+2) x (m+d+2) invertible matrices; CL (CL) j Is a confusion parameter, which is a document set security level stored in CS (cloud server), expressed as: CL (CL) j ＝{Level j |Level j ＝1，2，…，5}；{S，m 1 ，m 2 The key word vector is encrypted and the trapdoor vector is retrieved.
In one embodiment, the step of extracting topics from the document to obtain a set of topics comprises:
performing topic extraction on the data document by utilizing a pre-trained BTM topic model;
processing the extracted topics by using a Gibbs sampling method to obtain a topic-keyword relevance distribution matrix and a document-topic relevance distribution matrix;
respectively calculating confusion and consistency parameters of the topic-keyword relevance distribution matrix and the document-topic relevance distribution matrix;
extracting the optimal topic-keyword probability distribution matrix from all topics of the data document by using the topic-keyword correlation distribution matrix, the confusion degree and consistency parameters of the document-topic correlation distribution matrix;
designating the number of topics in the corpus according to the confusion degree and consistency parameters;
and extracting topics from the optimal topic-keyword relevance distribution matrix according to the number of topics in the specified library to obtain a topic set.
In this embodiment, the data service center will first train the model of the corpus under different topics by using the BTM topic model based on deep learning and perform the document topic T i Is an extraction of (2). Then using Gipps sampling (Gipps sample)ing) obtaining a topic-keyword relevance distribution Matrix (TKD-Matrix) omega and a document-topic relevance distribution Matrix (DTRD-Matrix) theta, then respectively calculating confusion and consistency parameters of the topic-keyword relevance distribution Matrix (TKD-Matrix) omega and the document-topic relevance distribution Matrix (DTRD-Matrix) theta, and extracting the optimal topic-keyword probability distribution Matrix of each topic. Finally, the topic number T of the corpus is specified according to the two parameters, and a topic set T= { T is extracted from the optimal topic-keyword relevance distribution matrix 1 ，T 2 ，…，T t }。
In one embodiment, the step of encrypting the theme set includes:
extracting keyword probability distribution vectors of each topic from a topic-keyword relevance distribution matrix by utilizing a pre-trained BTM topic model, constructing t nodes corresponding to t topics, and setting the t nodes as leaf nodes;
constructing a balanced tree index D by using t leaf nodes;
expanding the dimension of all vectors in the balance tree index D to (m+j+2) -bit, and calculating the index vector of the balance tree index
Will index the vectorSplit into two vectors->
Using { M ] 1 ，M 2 Encryption of sub-vectors in balanced tree index
In the present embodiment, the index vectorThe calculation method of (1) comprises the following steps: index vector +. >(m+j+1) th The bit dimension is set to a random number { epsilon } (j )|j∈[1，d][ MEANS FOR SOLVING PROBLEMS ]>Wherein d represents the number of virtual words added, m is the size of the array, (m+j+2) th -bit is set to 1;
index vectorThe segmentation method comprises the following steps: judging whether the value of each dimension of the 0/1 split vector S is 1, if so, determining thatOtherwise will D' i ＝D″ i Wherein r represents a randomly selected random number.
Wherein I is 1，i For encrypting index { M 1 ，M 2 And two (m+d+2) x (m+d+2) invertible matrices.
In the present embodiment, each leaf node stores a topic ID and a array INFO xy The array size is m, and the array value is vector T [ i ]]Vector T [ i ]]The probability distribution of all keywords in the ith theme is corresponding, and x and y respectively represent the y node of the x layer;
wherein the non-leaf node r xy Storing larger INFO xy When two nodes form a new balanced tree index, the INFO of two child nodes is used xy One-to-one comparison, large stored in corresponding parent node INFO xy The location then gets a balanced tree index D, otherwise known as the primary index.
In one embodiment, the step of encrypting the theme set includes:
obtaining a topic probability distribution vector of an ith document from a document-topic relevance distribution matrix;
Constructing a theme-document inverted index;
and encrypting the inverted index by using the symmetric key to generate the security index of the subject document.
In this embodiment, since each document contains a topic vector of potential semantic features, it is easy to construct a document-topic distribution Matrix, and then obtain the topic probability distribution vector of the ith document from the trained DTRD-Matrix θ. In order to improve the retrieval rate, constructing a topic-document inverted index, realizing quick retrieval of documents, and then using a symmetric key K 1 Encrypting the document to generate a subject document security index I 2 May also be referred to as a secondary index.
In this embodiment, the step of encrypting the theme set further includes: a look-up table is constructed.
Specifically, the form of the lookup table is shown in table 1:
| keyword(s) | Label (Label) |
| ξ sk (W 1 ) | Tag 1 ＝<γ 1 |MAC K (W 1 ，v 1 )> |
| ξ sk (W 2 ) | Tag 2 ＝<γ 2 |MAC K (W 2 ，v 2 )> |
| ξ sk (W 3 ) | Tag 3 ＝<γ 3 |MAC K (W 3 ，v 3 )> |
| …… | …… |
Table 1 look-up table
In Table 1, ζ sk (W 1 )、ξ sk (W 2 )、ξ sk (W 3 ) … … the pseudo-random transformation value under the action of the key sk, gamma i And MAC K (W i ，v i ) Represents a tag field, wherein γ i ＝Enc sk (v i )，v i A vector value indicating whether it appears in each file. If v i，j =1, it indicates that the j-th file contains the keyword, and otherwise, it does not.
The data owner uses the symmetric key K for the file that needs to be uploaded 1 Encrypting and then obtaining an encrypted document set The security index and the lookup Table are then sent to the cloud server.
And after the search operation of the cloud server is completed, searching in a lookup Table according to each pseudo-random transformation value, returning a corresponding Tag, and then returning ciphertext and tags of the first k files to the user.
In the construction scheme of the second-level index proposed in the present embodiment and the first-level index proposed in the above embodiment, one of the two-level indexes may be used in the semantic keyword search encryption method, or may be used simultaneously in the semantic keyword search encryption method.
In one embodiment, the step of matching the encryption index according to the search request instruction includes:
the search request instruction sent by the data user comprises the following steps:
after a data user side sends a search request, extracting keywords in the search request;
encrypting the keywords and generating a search trapdoor;
sending the search trapdoor to a data user terminal;
using the data user terminal to re-initiate the search request by using the search trapdoor;
and receiving a search trapdoor sent by the data user as a search request instruction.
In the embodiment, the search data of the data user can also be protected by extracting the keywords and encrypting the keywords into trapdoors, so that the safety of the data user is improved.
In one embodiment, the step of encrypting the keyword and generating a search trapdoor comprises:
pseudo-randomly transforming keywords to generate virtual keywordsAnd at->Insertion setGenerating a set Q i ；
Set Q by pre-trained BTM topic model i Generating an m-bit query topic vector;
random atV keywords are selected from the d virtual keywords, the corresponding positions of the v keywords are set to be 1, and the v keywords are scaled by using a random number, so that the dimension of the v keywords is expanded to be (m+j+2);
calculating a pre-acquired CL j Average with N
By CL j Variance is calculated for N and mu
Using variance sigma pairsDividing to obtain->
Using { M ] 1 ，M 2 And encrypting the search trapdoor to obtain the encrypted trapdoor.
The method for encrypting the search trapdoor comprises the following steps:{M 1 ，M 2 and two (m+d+2) x (m+d+2) invertible matrices.
In the present embodiment, n= { n=1, 2, … }, representing the number of keywords, CL j Is a confusion parameter, which is the security level of the collection of documents stored in the CS.
Using variance sigma pairsThe segmentation method comprises the following steps: inquiry->Is +.>If the value of each dimension of the 0/1 split vector S is 0, let +.> No-> And- >To search for trapdoors.
In one embodiment, the step of matching the encryption index includes:
calculating the similarity between the encryption index and the encryption trapdoor by utilizing a pre-constructed cosine function;
and if the similarity reaches a preset threshold, sending the ciphertext file corresponding to the encryption index and the theme label corresponding to the encryption index to the data user side.
In this embodiment, the cloud server calculates the encryption security index I by a cosine method 1 Encryption trapdoorSimilarity scores between to obtain the topic ID. Because each node of the balanced tree index is a vector representing the topic-keyword probability distribution, and the element in the parent node takes the maximum of the two child nodes, when matching the query trapdoor to the vector of tree nodes, the node with the highest probability is obtained, indicating that it is closer to the query vector. After obtaining the topic ID, matching the topic ID with the secondary index to obtain a document index, and finally returning the ciphertext set R to the user according to the document index, wherein the user uses the key K 1 And decrypting to obtain the required file.
However, since most KNN algorithms based on security improve the encryption strength of the algorithms by introducing random numbers, the algorithms still suffer from the threat of ratio attack; the application expands the dimension of the encryption triplet and matrix, and increases the safety strength by adding the dummy random number in the expansion dimension, but due to the random variable selected in each retrieval Is fixed, resulting in a regular distribution, so that when faced with big data analysis there is still a considerable hidden danger, so that to further eliminate this fixed attribute, the application also uses the improved idea of "confusion parameters" to perturb the number of inserted keywords, according to each queryThe security level and the keyword number of the query file are dynamically expanded in dimension for the encryption triples and the matrix, namely the query keyword number is +.>The final correlation is thus calculated as follows:
in one embodiment, the step of decrypting the ciphertext file comprises:
analyzing the theme label to obtain gamma i And MAC K (W i ，v i ) Wherein, gamma i And MAC K (W i ，v i ) The tandem value of (a) represents the tag domain, gamma i ＝Enc sk (v i )，v i A vector value indicating whether or not it appears in each file, enc indicating an encryption algorithm;
computing Dec for each tag using Dec function sk (γ i ) Obtain v i Calculation using MAC function again
To be calculatedAnd MAC K (W i ，v i ) Comparing, if the two are the same, performing the next verification, otherwise directly refusing the decryption operation;
the next step of verification includes: calculation of v=v 1 ^v 2 ^v 3 ^…^v N Wherein, a represents a placeholder modifier character; and (3) checking whether the corresponding positions in the v are all 1, if yes, verifying successfully, and executing decryption operation on the ciphertext file, otherwise, directly refusing the decryption operation.
In this embodiment, the data client receives the above embodimentsAfter the ciphertext and Tag of the described k files, executing the steps, namely: for topic tags { Tag i I=1, 2, …, N } to obtain γ by analysis i And MAC K (W i ，v i ). Dec is then calculated for each tag sk (γ i ) Obtain v i RecalculatingThen with MAC K (W i ，v i ) And comparing, if the comparison result is the same, performing the next verification, otherwise, directly rejecting.
Subsequently calculate v=v 1 ^v 2 ^v 3 ^…^v N Checking the returned ciphertext set, checking whether the corresponding positions in v are all 1, if yes, verifying successfully, then executing decryption operation, otherwise, directly rejecting.
In summary, according to the semantic keyword search encryption method provided by the application, potential semantic relation modeling between a topic model and text and extracted keywords is utilized, probability distribution of keywords to be queried is generated by utilizing a BTM topic model based on deep learning as a search evidence, then matching operation is carried out on the probability distribution and a security index to obtain a topic ID, and then the topic ID and a secondary index are matched to return the most relevant document. The irrelevance between the extracted keywords and the document identifier is ensured by replacing the specific keywords with the topics based on the semantics, meanwhile, by constructing a double-layer index structure, on one hand, the trained topic model reduces the dimension of nodes in the security index, improves the retrieval efficiency, and on the other hand, the inverted index constructed based on the balance tree further reduces the time complexity.
Referring to fig. 2, the embodiment of the application further provides a semantic keyword search encryption system, which includes: the system comprises a data encryption module 1, an instruction acquisition module 2, an index matching module 3, a file transmission module 4 and a data decryption module 5.
The data encryption module 1 is used for acquiring document data which needs to be encrypted at a data owner side and encrypting the document data to obtain an encryption index and a ciphertext file which are in one-to-one correspondence; the instruction acquisition module 2 is used for acquiring a search request instruction sent by a data user terminal; the index matching module 3 is used for matching the encryption index according to the search request instruction; the file transmission module 4 is used for transmitting the ciphertext file corresponding to the matched encryption index to the data user side; the data decryption module 5 is used for decrypting the ciphertext file at the data user end to obtain document data.
In the semantic keyword search encryption system provided by the embodiment, in the process of search encryption, document data needing to be encrypted at the data owner side are encrypted, and all document data are not required to be encrypted, so that the extraction quantity of keywords is reduced, and the storage space is saved.
In one embodiment, the data encryption module 1 includes: the system comprises a document encryption unit, a theme set extraction unit and a theme set encryption unit; the document encryption unit is used for encrypting the document data by utilizing the pre-acquired secret key to obtain an encrypted document set; the topic set extraction unit is used for extracting topics of the document data to obtain a topic set, and topics in the topic set correspond to documents in the encrypted document set one by one; the topic set encryption unit is used for encrypting the topic set to obtain an encryption index.
In one embodiment, the topic set extraction unit includes: the system comprises a data extraction subunit, a matrix acquisition subunit, a parameter calculation subunit, an optimal theme extraction subunit, a theme quantity assignment subunit and a theme acquisition subunit;
the data extraction subunit is used for extracting the topics of the data document by utilizing the pre-trained BTM topic model; the matrix acquisition subunit is used for processing the extracted topics by using a Gibbs sampling method to obtain a topic-keyword relevance distribution matrix and a document-topic relevance distribution matrix; the parameter calculation subunit is used for respectively calculating confusion and consistency parameters of the topic-keyword relevance distribution matrix and the document-topic relevance distribution matrix; the optimal topic extraction subunit is used for extracting an optimal topic-keyword probability distribution matrix from all topics of the data document by utilizing the topic-keyword correlation distribution matrix, the confusion degree and consistency parameters of the document-topic correlation distribution matrix; the topic number designating subunit is used for designating the number of topics in the corpus according to the confusion degree and the consistency parameter; the topic acquisition subunit is used for extracting topics from the optimal topic-keyword relevance distribution matrix according to the number of topics in the specified library to obtain a topic set.
In one embodiment, the topic set encryption unit includes: a sub-node constructing sub-unit, a balance tree index constructing sub-unit, a first dimension expanding sub-unit, an index vector dividing sub-unit and a sub-vector encrypting sub-unit;
the child node construction subunit is used for extracting keyword probability distribution vectors of each topic from the topic-keyword relevance distribution matrix by utilizing a pre-trained BTM topic model, constructing t nodes corresponding to t topics, and setting the t nodes as leaf nodes;
the balanced tree index constructing subunit is used for constructing a balanced tree index D by using t leaf nodes;
the first dimension expansion subunit is configured to expand dimensions of all vectors in the balanced tree index D to (m+j+2) -bit, and calculate an index vector of the balanced tree indexIndex vector->The calculation method of (1) comprises the following steps: index vector +.>(m+j+1) th The bit dimension is set to a random number { epsilon } (j) |j∈[1，d][ MEANS FOR SOLVING PROBLEMS ]>Wherein d represents the number of virtual words added, m is the size of the array, (m+j+2) th -bit is set to 1;
the index vector dividing subunit is used for dividing the index vectorSplit into two vectors->The segmentation method comprises the following steps: judging whether the value of each dimension of the 0/1 split vector S is 1, if so, the value is 1 >Otherwise will D' i ＝D″ i Wherein r represents a randomly selected random number;
the sub-vector encryption subunit is used for utilizing { M ] 1 ，M 2 Encryption of sub-vectors in balanced tree indexWherein I is 1，i For encrypting index { M 1 ，M 2 And two (m+d+2) x (m+d+2) invertible matrices.
In one embodiment, the topic set encryption unit includes: the system comprises a theme probability distribution vector acquisition subunit, an inverted index construction subunit and an inverted index encryption subunit.
The topic probability distribution vector acquisition subunit is used for acquiring topic probability distribution vectors of the ith document from the document-topic relevance distribution matrix; the inverted index constructing subunit is used for constructing a theme-document inverted index; the inverted index encryption subunit is configured to encrypt the inverted index using the symmetric key to generate a security index of the subject document.
In one embodiment, the instruction fetch module 2 includes: the device comprises a keyword extraction unit, a keyword encryption unit, a search trapdoor transmission unit, a search trapdoor search unit and a search trapdoor receiving unit;
the keyword extraction unit is used for extracting keywords in the search request after the data user terminal sends the search request;
the keyword encryption unit is used for encrypting keywords and generating a search trapdoor;
The searching trapdoor transmission unit is used for transmitting the searching trapdoor to the data user terminal;
the searching trapdoor searching unit is used for restarting a searching request by using the searching trapdoor by utilizing the data user terminal;
the search trapdoor receiving unit is used for receiving a search trapdoor sent by a data user as a search request instruction
In one embodiment, the keyword encryption unit includes: the system comprises a pseudo-random transformation subunit, a query subject vector generation subunit, a second dimension expansion subunit, an average value calculation subunit, a variance calculation subunit, a vector sub-tracking subunit and a search trapdoor encryption subunit;
pseudo-random transformation subunit for pseudo-randomly transforming keywords to generate virtual keywordsAnd atInsert set->Generating a set Q i ；
The query topic vector generation subunit is used for generating a set Q by a pre-trained BTM topic model i Generating an m-bit query topic vector;
the second dimension expansion subunit is used for random presenceV keywords are selected from the d virtual keywords, the corresponding positions of the v keywords are set to be 1, and the v keywords are scaled by using a random number, so that the dimension of the v keywords is expanded to be (m+j+2);
the average value calculation subunit is used for calculating the pre-acquired CL j Average with NWhere n= { n=1, 2, … }, represents the number of keywords, CL j Is a confusion parameter, for storing inDocument collection security level in CS;
variance calculation subunit for using CL j Variance is calculated for N and mu
The vector sub-unit is used for utilizing the variance sigma pairDividing to obtain->The segmentation method comprises the following steps: inquiry->Each element->If the value of each dimension of the 0/1 split vector S is 0, then letNo->And->Searching trapdoors;
search trapdoor encryption subunit for utilizing { M 1 ，M 2 Encrypting the search trapdoor to obtain an encrypted trapdoor, wherein the encryption method comprises the following steps:{M 1 ，M 2 and two (m+d+2) x (m+d+2) invertible matrices.
In one embodiment, the index matching module 3 includes: a similarity calculation unit and a data transmission unit;
the similarity calculation unit is used for calculating the similarity between the encryption index and the encryption trapdoor by utilizing a previously constructed cosine function;
and the data sending unit is used for sending the ciphertext file corresponding to the encryption index and the theme label corresponding to the encryption index to the data user terminal if the similarity reaches a preset threshold value.
In one embodiment, the data decryption module 5 comprises: the system comprises a theme tag analysis unit, a theme tag calculation unit and a verification unit;
The topic label analysis unit is used for analyzing topic labels to obtain gamma i And MAC K (W i ，v i ) Wherein, gamma i And MAC K (W i ，v i ) The tandem value of (a) represents the tag domain, gamma i ＝Enc sk (v i )，v i A vector value indicating whether or not it appears in each file, enc indicating an encryption algorithm;
the topic tag calculation unit is used for calculating Dec for each tag by using Dec function sk (γ i ) Obtain v i Calculation using MAC function again
For combining calculatedAnd MAC K (W i ，v i ) Comparing, if the two are the same, performing the next verification, otherwise directly refusing the decryption operation;
the step of the verification unit for the next verification includes: calculation of v=v 1 ^v 2 ^v 3 ^…^v N Wherein, a represents a placeholder modifier character; and (3) checking whether the corresponding positions in the v are all 1, if yes, verifying successfully, and executing decryption operation on the ciphertext file, otherwise, directly refusing the decryption operation.
An embodiment of the present application further provides an electronic device, referring to fig. 3, including: the memory 601, the processor 602, and a computer program stored on the memory 601 and executable on the processor 602, the processor 602 implements the semantic keyword search encryption method described in the foregoing when executing the computer program.
Further, the electronic device further includes: at least one input device 603 and at least one output device 604.
The memory 601, the processor 602, the input device 603, and the output device 604 are connected via a bus 605.
The input device 603 may be a camera, a touch panel, a physical key, a mouse, or the like. The output device 604 may be, in particular, a display screen.
The memory 601 may be a high-speed random access memory (RAM, random Access Memory) memory or a non-volatile memory (non-volatile memory), such as a disk memory. The memory 601 is used for storing a set of executable program codes and the processor 602 is coupled to the memory 601.
Further, the embodiment of the present application also provides a computer readable storage medium, which may be provided in the electronic device in each of the above embodiments, and the computer readable storage medium may be the memory 601 in the above embodiments. The computer-readable storage medium has stored thereon a computer program which, when executed by the processor 602, implements the semantic keyword search encryption method described in the foregoing embodiments.
Further, the computer-readable medium may be any medium capable of storing a program code, such as a usb (universal serial bus), a removable hard disk, a Read-Only Memory 601 (ROM), a RAM, a magnetic disk, or an optical disk.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in software functional modules.
The integrated modules, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention.
It should be noted that, for the sake of simplicity of description, the foregoing method embodiments are all expressed as a series of combinations of actions, but it should be understood by those skilled in the art that the present invention is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily all required for the present invention.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
In summary, although the present invention has been described in terms of the preferred embodiments, the preferred embodiments are not limited to the above embodiments, and various modifications and changes can be made by one skilled in the art without departing from the spirit and scope of the invention, and the scope of the invention is defined by the appended claims.
Claims (10)
1. A semantic keyword search encryption method, comprising:
acquiring document data to be encrypted at a data owner side, and encrypting the document data to obtain an encryption index and a ciphertext file which are in one-to-one correspondence;
acquiring a search request instruction sent by a data user terminal;
matching the encryption index according to the search request instruction;
transmitting the ciphertext file corresponding to the matched encryption index to a data user side;
and decrypting the ciphertext file at the data user side to obtain document data.
2. The semantic keyword search encryption method according to claim 1,
The encrypting the document data includes:
encrypting the document data by using the pre-acquired key to obtain an encrypted document set;
extracting topics of the document data to obtain a topic set, wherein topics in the topic set correspond to documents in the encrypted document set one by one;
and encrypting the theme set to obtain an encryption index.
3. The semantic keyword search encryption method according to claim 2,
the step of extracting the topics of the document to obtain a topic set comprises the following steps:
performing topic extraction on the data document by using a pre-trained BTM topic model;
processing the extracted topics by using a Gibbs sampling method to obtain a topic-keyword relevance distribution matrix and a document-topic relevance distribution matrix;
respectively calculating confusion degree and consistency parameters of the topic-keyword relevance distribution matrix and the document-topic relevance distribution matrix;
extracting the optimal topic-keyword probability distribution matrix from all topics of the data document by using the topic-keyword correlation distribution matrix, the confusion degree and the consistency parameter of the document-topic correlation distribution matrix;
Designating the number of topics in the corpus according to the confusion degree and consistency parameters;
and extracting topics from the optimal topic-keyword relevance distribution matrix according to the number of topics in the specified library to obtain a topic set.
4. The semantic keyword search encryption method according to claim 3,
the step of encrypting the theme set includes:
extracting keyword probability distribution vectors of each topic from the topic-keyword relevance distribution matrix by utilizing a pre-trained BTM topic model, constructing t nodes corresponding to t topics, and setting the t nodes as leaf nodes;
constructing a balanced tree index D by using t leaf nodes;
expanding the dimension of all vectors in the balance tree index D to (m+j+2) -bit, and calculating the index vector of the balance tree indexIndex vector->The calculation method of (1) comprises the following steps: index vector +.>(m+j+1) th The bit dimension is set to a random number { epsilon } (j) |j∈[1，d][ MEANS FOR SOLVING PROBLEMS ]>Wherein d represents the number of virtual words added, m is the size of the array, (m+j+2) th -bit is set to 1;
will index the vectorSplit into two vectors->The segmentation method comprises the following steps: judging whether the value of each dimension of the 0/1 split vector S is 1, if so, the value is 1 > Otherwise will D' i ＝D＂ i Wherein r represents a randomly selected random number;
using { M ] 1 ，M 2 Encrypting the sub-vectors in the balanced tree index to obtainWherein I is 1，i For encrypting index { M 1 ，M 2 And two (m+d+2) x (m+d+2) invertible matrices.
5. The semantic keyword search encryption method according to claim 3,
the step of encrypting the theme set includes:
obtaining a topic probability distribution vector of an ith document from a document-topic relevance distribution matrix;
constructing a theme-document inverted index;
and encrypting the inverted index by using a symmetric key to generate a theme document security index.
6. The semantic keyword search encryption method according to claim 1,
the step of obtaining the search request instruction sent by the data user comprises the following steps:
after the data user side sends a search request, extracting keywords in the search request;
encrypting the keywords and generating a search trapdoor;
the search trapdoor is sent to a data user side;
using the data user terminal to reinitiate a search request by using a search trapdoor;
receiving a search trapdoor sent by the data user as the search request instruction;
The step of encrypting the keyword and generating a search trapdoor comprises the following steps:
pseudo-randomly transforming keywords to generate virtual keywordsAnd at->Insertion setGenerating a set Q i ；
Set Q by pre-trained BTM topic model i Generating an m-bit query topic vector;
random atV keywords are selected from the d virtual keywords, the corresponding positions of the v keywords are set to be 1, and the v keywords are scaled by using a random number, so that the dimension of the v keywords is expanded to be (m+j+2);
calculating a pre-acquired CL j Average with NWhere n= { n=1, 2, … }, representing the number of keywords, CL j Is a confusion parameter, which is the security level of the document collection stored in the CS;
by CL j Variance is calculated for N and mu
Using the variance sigma pairDividing to obtain->The segmentation method comprises the following steps: inquiry->Is +.>If the value of each dimension of the 0/1 split vector S is 0, let +.>No-> And->Searching trapdoors;
using { M ] 1 ，M 2 Encrypting the search trapdoor to obtain an encrypted trapdoor, wherein the encryption method comprises the following steps:{M 1 ，M 2 and two (m+d+2) x (m+d+2) invertible matrices.
7. The semantic keyword search encryption method of claim 6,
The step of matching the encryption index includes:
calculating the similarity between the encryption index and the search trapdoor;
if the similarity reaches a preset threshold, sending a ciphertext file corresponding to the encryption index and a theme label corresponding to the encryption index to a data user side;
the step of decrypting the ciphertext file comprises:
analyzing the theme label to obtain gamma i And MAC K (W i ，v i ) Wherein, gamma i And MAC K (W i ，v i ) The tandem value of (a) represents the tag domain, gamma i ＝Enc sk (v i )，v i A vector value indicating whether or not it appears in each file, enc indicating an encryption algorithm;
computing Dec for each tag using Dec function sk (γ i ) Obtain v i Calculation using MAC function again
To be calculatedAnd MAC K (W i ，v i ) Comparing, if the two are the same, performing the next verification, otherwise directly refusing the decryption operation;
the step of next verification includes: calculation of v=v 1 ∧ v 2 ∧ v 3 ∧ … ∧ v N Wherein, Λ represents a space-occupying decoration character; and (3) checking whether the corresponding positions in the v are all 1, if yes, verifying successfully, and executing decryption operation on the ciphertext file, otherwise, directly refusing the decryption operation.
8. A semantic keyword search encryption system comprising:
the data encryption module is used for acquiring document data which needs to be encrypted by a data owner side and encrypting the document data to obtain an encryption index and a ciphertext file which are in one-to-one correspondence;
The instruction acquisition module is used for acquiring a search request instruction sent by the data user terminal;
the index matching module is used for matching the encryption index according to the search request instruction;
the file transmission module is used for transmitting the ciphertext file corresponding to the matched encryption index to the data user side;
and the data decryption module is used for decrypting the ciphertext file at the data user end to obtain document data.
9. An electronic device, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method of any of claims 1 to 7.
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