CN103927340B - A kind of cipher text retrieval method - Google Patents

A kind of cipher text retrieval method Download PDF

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CN103927340B
CN103927340B CN201410119505.XA CN201410119505A CN103927340B CN 103927340 B CN103927340 B CN 103927340B CN 201410119505 A CN201410119505 A CN 201410119505A CN 103927340 B CN103927340 B CN 103927340B
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document
vector
cluster
ciphertext
server
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CN103927340A (en
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陈驰
朱晓杰
申培松
田雪
于晶
王朝刚
杨腾飞
李丹
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Institute of Information Engineering of CAS
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/3331—Query processing
    • G06F16/334—Query execution
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/35—Clustering; Classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
    • G06F16/374—Thesaurus
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/93—Document management systems

Abstract

The invention discloses a kind of cipher text retrieval method, in the establishment and the encryption of index of client implementation level cluster, server receives cryptogram search vector, relevance score between each document vector sum query vector can be calculated by the ciphertext index based on hierarchical clustering, and document ordering function is realized according to the relevance score, due to vectorial without all of ciphertext document of inquiry in query process, but search for the document vector of specific intra-cluster, so query time greatly reduces, searching ciphertext efficiency is improve.The present invention supports the searching ciphertext that multiple key can sort, and supports the searching ciphertext under big data quantity, meanwhile, effectively protect data-privacy.

Description

A kind of cipher text retrieval method
Technical field
The present invention relates to information security field, specifically, it is related to a kind of cipher text retrieval method.
Background technology
Along with the popularization and development of cloud computing, cloud security also increasingly obtains the concern of people.Due to cloud computing system It is in large scale, many privacy of user data and enterprise's private data, and unprecedented open and complexity are carried, make Its security facing stern challenge.In order to protect the security of data, increasing enterprises and individuals have selected first logarithm According to encryption, Cloud Server is then uploaded to again.This causes that the ciphertext data in public cloud are faced with volatile growth, how to examine Data after Suo Jiami become a problem.The fast development of searching ciphertext technology, has promoted the solution of cloud security problem.
Existing scheme currently for ciphertext text retrieval is broadly divided into two classes：Ordered retrieval and the retrieval based on index. Ordered retrieval is mainly compared one by one by all keywords in searching keyword and document, realizes matching retrieval.Based on index Retrieval mainly by search index, realizes the positioning searching of document.But, because ordered retrieval is needed to all of word in document It is scanned, it is necessary to the keyword of scanning also can explosively increase when number of documents exponentially increases, so should Scheme is not suitable for being applied under the very big environment of data volume.And the search method for indexing is based on, during by shorter query execution Between and be widely used.But the foundation of index has to safeguard the safety of data, it is impossible to reveal letter because of index Breath.So, how to set up a kind of safe Indexing Mechanism is a full of challenges problem.
Three below patent all refers to searching ciphertext technology：
Chinese invention patent application, publication number：One kind is disclosed in CN200410070113.5 in PKI and full-text search skill On the basis of art, the data retrieval under conditions of non-decrypting is realized.The present invention is the transformation to global search technology, Here almost remain most of technology of full-text search, encryption only carried out to the index terms of index file, be easy to be System is realized.The encryption of searching ciphertext system, decryption occur in client, effectively reduce data safety to server and network The dependence of Environmental security.
Chinese invention patent application, publication number：A kind of ciphertext full-text search system is disclosed in CN201010187384.4, Include urtext processing module, word-dividing mode, encrypting module, document ciphertext memory module, ciphertext index module, ciphertext inspection Rope module, retrieval result processing module, system management module.The system as a result of ciphertext dynamic descendence tree index structure, The ciphertext dynamic descendence tree index updating method of participle group technology, document local level, achievable safely and efficiently index creation, Full-text search and substring query under the dynamic renewal of index and ciphertext state.
Chinese invention patent application, publication number：Disclose a kind of for rapidly searching ciphertext in CN200810145083.8 Methods, devices and systems.Data owner is to file encryption and by ciphertext storage to server.Data owner is according to text The keyword generation encrypted indexes of part, and by encrypted indexes storage to server.Index is made up of keyword entry set, often Individual keyword entry set is identified by a keyword entry set locator, and including at least associated with corresponding keyword File one or more file retainers.Ciphertext of each file retainer comprising the information for obtaining encryption file, And correct file retainer decruption key is only utilized, the ciphertext could be decrypted.Data owner authorizes to searcher Keyword entry set locator and file retainer decruption key, to enable that searcher retrieves to encrypted indexes And obtain the file relevant with certain keyword.
However, these technical schemes are also faced with some challenges, there are some defects：
1）Cryptogram search efficiency is low.Because the current multiple key searching ciphertext scheme that can sort is required for sequential scan close Text index, can increase the query processing time when data volume is very big, significantly reduce search efficiency.
2）Multiple key word retrieval cannot be carried out.Needs carry out repeatedly list key search and can be only achieved retrieval purpose, so Data transfer and server burden can be increased, while reducing Consumer's Experience.
The content of the invention
Regarding to the issue above, the present invention proposes a kind of new cipher text retrieval method, provides the user more complete close Literary retrieval scheme.
The present invention proposes a kind of multiple key cipher text retrieval method, and client is connected with server, and the client is to clothes Business device uploads, downloads or updates ciphertext data；Its step includes：
1）Vectorization is carried out to the plain text document that data owner uploads in client, according to similar between document vector Degree cluster, and document vector in cluster is layered, the index based on hierarchical clustering, encrypting plaintext document and index are set up, Generate ciphertext document and ciphertext index and upload onto the server；
2）Data consumer's input inquiry sentence, client carries out vectorization to query statement and encrypts, and generation ciphertext is looked into Ask vector and be dealt into server；
3）Server is successively searched the best match most close with data consumer's query statement and is clustered by ciphertext index, And calculate the relevance score between the vector of the ciphertext document vector sum cryptogram search under the cluster；
4）Server according to ciphertext document vector sum cryptogram search vector between relevance score to ciphertext document ordering, According to data consumer's demand, ciphertext document and its document code in the top is returned.
Further, described data owner can be with authorization data user（The user for being retrieved）In the clothes Business device on search its upload onto the server on document.
Further, the vectorization method for expressing of described plain text document is as follows：
1）Syntactic analysis and morphological analysis are carried out according to the plain text document that data owner uploads, keyword set is obtained, Keyword in all documents constitutes dictionary；
2）According to above-mentioned dictionary, each document is searched, if the keyword in the dictionary can be found in a document, The correspondence position of the document vector is 1, is otherwise 0.
Also the method similar with plain text document vectorization is used during query statement vectorization.
Further, after hierarchical clustering is formed, each cluster includes a center vector, for example, it may be each is literary The average of shelves vector；The described index based on hierarchical clustering include cluster, the membership of document, cluster centre vector and Document vector.
Further, when described foundation is based on the index of hierarchical clustering, following clustering convergence condition is preset：
1）The degree of correlation is satisfied by certain constraints between document number of vectors and the document vector of intra-cluster；
2）Using Euclidean space distance metric or corner dimension as the degree of correlation foundation.
Further, vector is indicated after described cluster centre vector encryption as ciphertext cluster.
Further, the index AES for being used during described encrypted indexes is comprised the following steps：
1）In client generation index encryption key { u, S, M1,M2}；
2）Document vector D, cluster centre vector C are extended for the vector that length is u, random number is inserted in the position having more, Then the document vector sum cluster centre vector after expansion is respectively decomposed into two vector D according to vectorial S1、D2With C1、C2, then Cipher key matrix M1 ‐1,M2 ‐1D is multiplied by respectively1,D2With C1、C2, by decomposition after vectorial D1And D2It is encrypted as M‐1 1*D1, M‐1 2*D2, C1With C2It is encrypted as M‐1 1*C1, M‐1 2*C2And on uploading onto the server, so as to obtain ciphertext index.
Further, described ciphertext index includes cluster, the membership of document, and ciphertext cluster indicates vectorial and close Document vector.
Further, the server successively searches the best match cluster most close with data consumer's query statement When, the relevance score between cryptogram search vector ciphertext cluster instruction vector corresponding with each cluster in this layer is calculated, point Number highest for matching cluster, then to next layer continue inquire about the matching cluster all sub- cluster, find out its correlation Gamete cluster set, the like, the matching cluster of the bottom is best match cluster.
Compared with prior art, the present invention has following advantage：
1）The searching ciphertext for supporting multiple key to sort.Server receives cryptogram search vector, by poly- based on level The ciphertext index of class can calculate the relevance score between each document vector sum query vector, and according to the degree of correlation point Number realizes document ordering function.
2）Support the searching ciphertext under big data quantity.The program inquires about maximally related matching by searching with data consumer Cluster, reduces the amount of calculation of relevance score between document and inquiry, the significant increase efficiency of searching ciphertext.
3）The work of clustering and index encryption is completed in client, data-privacy is effectively protected.
Brief description of the drawings
Fig. 1 is the application scenario diagram in multiple key cipher text retrieval method of the present invention, and wherein Top k represent data consumer Specify and return to file number；
Fig. 2 is the overall framework figure in multiple key cipher text retrieval method of the present invention, whereinCryptogram search vector is represented, Top k represent that data consumer specifies and return to file number；
Fig. 3 is the flow chart of the cryptogram search in multiple key cipher text retrieval method of the present invention, wherein（W1…Wn）Represent bright Query text vector,（T1…Tn）Cryptogram search vector is represented,（C1,1…C1,n）、（C2,1…C2,n）、（C3,1…C3,n）Represent that ciphertext is gathered Class center vector,（d1,1…d1,n）、（d2,1…d2,n）、（d3,1…d3,n）、（d9,1…d9,n）Represent ciphertext document vector.
Specific embodiment
The present invention will be further described in detail with specific implementation below in conjunction with the accompanying drawings, but limits this never in any form The scope of invention.
The present embodiment uses the application scenarios based on data outsourcing model as shown in Figure 1, wherein, have three in initialization system Individual user, respectively admin, tom and jerry, wherein admin are data owners, and jerry is cloud service provider, and tom is Data consumer.
The present embodiment shows that admin uploads to document on the Cloud Server of jerry offers, and authorizes tom in cloud service The query process of these documents and tom is searched on device, whole process is as shown in Figure 2.
It is assumed that admin has 7 documents, as shown in table 1, wherein keyword is extracted from content for its content and keyword Come.
Table 1：Document content and keyword
Document code Document content Keyword
1 There is basketball, be exactly perfect life Basketball, perfect, life
2 Basketball is round Basketball is justified
3 This child likes playing basketball Child, likes, and beats, basketball
4 I can go to play basketball the time Time, meeting is beaten, basketball
5 Football is my hobby Football, hobby
6 With the people that football is dreamed of Band, football, dream
7 The comparing of football and basketball Football, basketball compares
Table 2：The dictionary that all documents of admin are constituted
Numbering Keyword Numbering Keyword
1 Basketball 8 Time
2 It is perfect 9 Meeting
3 Life 10 Football
4 Circle 11 Hobby
5 Child 12 Band
6 Like 13 Dream
7 Beat 14 Compare
Word in table 2 comes from the keyword in document, and the keyword in all documents constitutes a dictionary.According to this Individual dictionary, admin is by each document vectorization, if the word occurs in document, the corresponding position of vector is 1, is otherwise 0, knot Fruit is as follows：
Table 3：The vectorization of document is represented
Admin carries out hierarchical clustering in client according to above-mentioned document vector, wherein, set in the present embodiment following Clustering convergence condition：
(1) the similarity between an intra-cluster document is greater than equal to 1, otherwise increases a cluster newly.
(2) a cluster interior element is otherwise layered no more than 3.
The specific execution step of hierarchical clustering algorithm is as follows：
(1) it is 2 that admin sets initial cluster centre number；
(2) random two vectors of selection from document vector are used as initial cluster center, it is assumed that be 1 and 6；
(3) partition clustering is carried out to document, finally obtain ground floor two documents vector set { 1,2,3,4 } and 5,6, 7}。
To document vector set { 1,2,3,4 } continue divide, obtain the second layer two documents vector set { 1,2 } and {3,4}。
After cluster is formed, the center vector of each cluster is the average of each document vector.After cluster centre vector encryption Vector can be indicated as ciphertext cluster.
Before encryption is indexed, admin firstly generates key { u, S, M1,M2}。
When encryption is indexed, 14 random values are all added to every document vector sum cluster centre vector first（This value Can sets itself as required）, it is assumed that random value is all 0.1.Admin produces random vector S with random value generator, its length Spend is that document vector length adds random number digit, that is, 28.
Table 4：Random vector S
1 2 3 4 5 6 7 8 9 10 11 12 13 14
1 1 1 1 1 1 1 0 0 0 0 0 0 0
15 16 17 18 19 20 21 22 23 24 25 26 27 28
0 0 0 0 0 0 0 1 1 1 1 1 1 1
Each document vector sum cluster centre vector, is divided into D by 28 place values in S1And D2, as S [i]=1, D1[i] is random value, D2[i]=originally value-D1[i].As S [i]=0, D2[i]=D1[i].Assuming that all of random value is all 0.5, then D1Such as table 5, D2Such as table 6.
Table 5：D1Vector set
Table 6：D2Vector set
Assuming that document { 1,2,3,4 } and { 5,6,7 } form the two of ground floor larger clusters, wherein cluster 1, 2,3,4 } be divided into two clusters of the second layer { 1,2 } and { 3,4 } again, following four figure illustrate these corresponding centers of cluster to The segmentation situation of amount：
Table 7：{ 1,2,3,4 } center vector of document vector set and its segmentation after vector
Table 8：{ 5,6,7 } center vector of document vector set and its segmentation after vector
Table 9：{ 1,2 } center vector of document vector set and its segmentation after vector
Table 10：{ 3,4 } center vector of document vector set and its segmentation after vector
Two matrix M in the key that Admin is produced1And M2It is reversible unit matrix.Use M1And M2After segmentation Vectorial D1And D2It is M after encryption‐1 1*D1, M‐1 2*D2, and on uploading onto the server, while to the plain text document of user using symmetrical On AES encryption is uploaded onto the server, the document vector after encryption is：
Table 11：Encryption M‐1 1*D1Set
Table 12：Encryption M‐1 2*D2Set
Detailed process during inquiry is as shown in figure 3, setting data user tom wants to search and keyword " perfect life " most phase 1 document for closing, " perfect life " is issued admin by him, and admin firstly generates query vector Q, such as table 13, wherein first 14 Dictionary construction is depended on, 14 is random value afterwards, it is assumed that be all 0.01.Then using the S in key to the query vector Q that generates Carry out segmentation and produce Q1And Q2, such as table 14.
Table 13：Query vector Q
Q 1 2 3 4 5 6 7 8 9 10 11 12 13 14
0 1 1 0 0 0 0 0 0 0 0 0 0 0
15 16 17 18 19 20 21 22 23 24 25 26 27 28
0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Table 14：The split vector of Q
Q1 1 2 3 4 5 6 7 8 9 10 11 12 13 14
0 1 1 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 0.5
15 16 17 18 19 20 21 22 23 24 25 26 27 28
0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Q2 1 2 3 4 5 6 7 8 9 10 11 12 13 14
0 1 1 0 0 0 0 ‐0.5 ‐0.5 ‐0.5 ‐0.5 ‐0.5 ‐0.5 ‐0.5
15 16 17 18 19 20 21 22 23 24 25 26 27 28
‐0.49 ‐0.49 ‐0.49 ‐0.49 ‐0.49 ‐0.49 ‐0.49 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Afterwards, admin uses key M1, M2To the Q for producing1And Q2Encryption, i.e. MT 1*Q1, MT 2* Q2, is sent to server. Server is first by MT 1*Q1、MT 2* Q2 and ciphertext cluster centre vector 1 and 2 carry out Euclidean distance computing, as a result such as table 15, according to Result show that cluster { 1,2,3,4 } is matching cluster, MT 1*Q1、MT 2* Q2 again with ciphertext cluster centre vector 11 and center vector 12 Compare, it is { 1,2 } to calculate best match cluster, such as table 16, last MT 1*Q1、MT 2* Q2 and ciphertext document vector 1,2 are distinguished Calculate relevance score, such as table 17, finally, numbering be 1 document be most matching inquiry document, its ciphertext document returns to number According to user tom, tom can be issued admin and be obtained document cleartext information decrypting.
Table 15：Calculate ground floor matching cluster
M-11* centers 1 M-11* centers 2
MT1*Q1 2.43 2.34
M-12* centers 1 M-12* centers 2
MT2*Q2 2.85 3.52
Amount to 5.28 5.86
Table 16：Calculate second layer matching cluster
M-11* centers 11 M-11* centers 12
MT1*Q1 2.51 2.41
M-12* centers 11 M-12* centers 12
MT2*Q2 2.50 3.12
Amount to 5.01 5.43
Table 17：Calculate most relevant documentation
M-11* documents vector 1 M-11* documents vector 2
MT1*Q1 2.51 2.51
M-12* documents vector 1 M-12* documents vector 2
MT2*Q2 2.24 2.96
Amount to 4.75 5.47
From the present embodiment as can be seen that this method realizes multiple key can sort searching ciphertext, wherein, do not obtaining The disabled user of data owner's proper authorization is the cleartext information that cannot know document, it is ensured that the content safety of document.It is right In server, because document and index are all encryptions, and different cryptogram search can also be generated for same key word of the inquiry, Server is difficult with the clear content that statistical analysis means guess document.From search efficiency, due in query process Without all of ciphertext document vector of inquiry, but the document for searching for specific intra-cluster is vectorial, so query time subtracts significantly It is few, improve searching ciphertext efficiency.

Claims (8)

1. a kind of cipher text retrieval method, client is connected with server, and the user end to server is uploaded, downloaded or more Xinmi City Literary data；Its step includes：
1) vectorization is carried out to the plain text document that data owner uploads in client, is gathered according to the similarity between document vector Class, and document vector in cluster is layered, set up the index based on hierarchical clustering, encrypting plaintext document and index, generation Ciphertext document and ciphertext index are simultaneously uploaded onto the server, wherein, when setting up the index based on hierarchical clustering, preset following Clustering convergence condition：
1-1) degree of correlation is satisfied by certain constraints between the document number of vectors of intra-cluster and document vector；
Euclidean space distance metric or corner dimension 1-2) are used as the foundation of the degree of correlation；
2) data consumer's input inquiry sentence, client carries out vectorization to query statement and encrypts, generation cryptogram search to Measure and be dealt into server；
3) server is successively searched the best match most close with data consumer's query statement and is clustered by ciphertext index, and counts Calculate the relevance score between the ciphertext document vector sum cryptogram search vector under the cluster；
4) server according to the relevance score between ciphertext document vector sum cryptogram search vector to ciphertext document ordering, according to Data consumer's demand, returns to ciphertext document and its document code in the top.
2. cipher text retrieval method as claimed in claim 1, it is characterised in that described data owner authorization data user Searched on the server its upload onto the server on document.
3. cipher text retrieval method as claimed in claim 1, it is characterised in that the vectorization method for expressing of described plain text document It is as follows：
1) plain text document uploaded according to data owner carries out syntactic analysis and morphological analysis, obtains keyword set, owns Keyword in document constitutes dictionary；
2) according to above-mentioned dictionary, each document is searched, if the keyword in the dictionary, this article can be found in a document The correspondence position of shelves vector is 1, is otherwise 0.
4. cipher text retrieval method as claimed in claim 1, it is characterised in that after hierarchical clustering is formed, each cluster is included One center vector；The described index based on hierarchical clustering include cluster, the membership of document, cluster centre vector and Document vector.
5. cipher text retrieval method as claimed in claim 4, it is characterised in that as close after described cluster centre vector encryption Text cluster indicates vector.
6. cipher text retrieval method as claimed in claim 4, it is characterised in that the index used during described encrypted indexes is encrypted Algorithm is comprised the following steps：
1) in client generation index encryption key { u, S, M1,M2}；
2) document vector D, cluster centre vector C are extended for the vector that length is u, random number is inserted in the position having more, then Document vector sum cluster centre vector after expansion is respectively decomposed into two vector D according to vectorial S1、D2With C1、C2, then key Matrix M1 ‐1,M2 ‐1D is multiplied by respectively1,D2With C1、C2, by decomposition after vectorial D1And D2It is encrypted as M‐1 1*D1, M‐1 2*D2, C1And C2Plus Close is M‐1 1*C1, M‐1 2*C2And on uploading onto the server, so as to obtain ciphertext index.
7. cipher text retrieval method as claimed in claim 5, it is characterised in that described ciphertext index includes cluster, document Membership, ciphertext cluster indicates vector and ciphertext document vector.
8. the cipher text retrieval method as described in claim 5-7 is any, it is characterised in that the server is successively searched and data When the most close best match of user's query statement is clustered, cryptogram search vector is calculated corresponding close with each cluster in the layer Text cluster indicates the relevance score between vector, fraction highest to be clustered for matching, then continues to inquire about this to next layer All sub- cluster with cluster, finds out the matching son cluster set of its correlation, the like, the matching cluster of the bottom is most Good matching cluster.
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CN109885650B (en) * 2019-01-08 2021-05-11 南京邮电大学 Outsourcing cloud environment privacy protection ciphertext sorting retrieval method

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