CN104036050A - Complex query method for encrypted cloud data - Google Patents
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- CN104036050A CN104036050A CN201410316970.2A CN201410316970A CN104036050A CN 104036050 A CN104036050 A CN 104036050A CN 201410316970 A CN201410316970 A CN 201410316970A CN 104036050 A CN104036050 A CN 104036050A
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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
- G06F16/10—File systems; File servers
- G06F16/14—Details of searching files based on file metadata
- G06F16/148—File search processing
Abstract
The invention discloses a complex query method for encrypted cloud data. The method includes the steps that a data owner establishes binary vector indexes for a file set, uses a symmetrical password mechanism for encrypting the file set and sends encrypted files to a cloud terminal; when requesting to get access to the files containing some keywords, one user applies to the data owner for a query token, and the query token includes a keyword set and the binary vector indexes of all the files; the user establishes a query binary vector according to query keywords and the keyword set, inner product calculation is performed on the query binary vector and index binary vectors of all the files, and whether the files include the query keywords of the user or not is judged; if the files include the query keywords, new index binary vectors corresponding to the query keywords are further established; the user makes the query keywords generate an LSSS matrix according to a logic expression, and inner production calculation is performed on the new index binary vectors and the LSSS matrix so that whether the files meet the query logic expression or not can be judged. Precise complex queries can be achieved, and the query efficiency can be higher than the query efficiency of inverted indexes which are widely used at present.
Description
Technical field
The invention belongs to cloud storage and information retrieval field, be specifically related to a kind of ciphertext cloud data complex query method.
Background technology
Under cloud storage environment, protect user data confidentiality and privacy, encryption is a kind of conventional method, but after data encryption, encrypt data search problem is urgently to be resolved hurrily.
For solving ciphertext cloud data retrieval problem, mainly contain at present two kinds of typical methods: one is directly ciphertext to be carried out to linear search, word in ciphertext is compared one by one, confirm the number of times whether keyword exists and occur; Second method, based on Security Index, is first set up keyword index to document, then will after document and index encryption, be uploaded to high in the clouds, and when search, from index, whether searching keyword is present in certain document.Directly the method shortcoming of ciphertext linear search is to search efficiency is not high, and cannot tackle the search scene of mass data.Searching ciphertext method based on index is current research main flow, and reason is that its search efficiency is better, and security performance is higher, is suitable for large-scale cloud storage searching ciphertext system.
In existing research work, all schemes are all to adopt inverted index mechanism, also do not use the scheme of binary vector index.And at present fewer about the scheme of complex query, and the accuracy of Query Result is in urgent need to be improved especially.
Adopt binary vector index only need to retain less information at data owner's end, just can realize the encrypt data retrieval of highly effective and safe.Adopt LSSS matrix can realize accurate complex query.
Ciphertext cloud data query is to ensure data confidentiality in cloud storage and gordian technique that can accessibility, has important theory significance and practical value for the fast development of propelling cloud storage.
Summary of the invention
For the defect of prior art, the object of the present invention is to provide a kind of ciphertext cloud data complex query method, be intended to improve data query accuracy, search efficiency and security.
For achieving the above object, the invention provides a kind of ciphertext cloud data complex query method, comprise the following steps:
Step 1. data owner, to its file set index building, uses binary vector index, and in index, each represents a keyword, represents with 0 and 1 whether corresponding keyword is present in this file;
Step 2. data owner uses Symmetric Cryptography encrypt file collection based on Single document or data block;
Encrypt file collection is sent to high in the clouds by step 3. data owner;
When step 4. user requires to access the file that comprises some keyword, apply for query token to data owner, in query token, include the binary vector index of keyword set and All Files;
Step 5. user builds inquiry binary vector according to searching keyword and keyword set, and inquiry binary vector and the index binary vector of each file are carried out to inner product calculating judges whether this file comprises user's searching keyword;
If this file of step 6. includes searching keyword, further build the new index binary vector corresponding with searching keyword;
Step 7. user generates LSSS (Linear Secret Sharing Scheme by searching keyword according to logical expression, linear secret sharing scheme) matrix, and new index binary vector and LSSS matrix are carried out to inner product calculating further to judge whether this file meets query logic expression formula.
Step 1 specifically comprises following sub-step:
1.1 data owners use existing point of word algorithm to extract keyword to its file set, build keyword set;
1.2 data owner builds binary vector index according to the corresponding keyword whether comprising in each file in keyword set, represents that with 1 corresponding keyword is present in this file, represents that with 0 corresponding keyword is not present in this file.
In step 2, if encrypt based on Single document, data owner, according to quantity of documents in file set, utilizes Symmetric Cryptography to generate at random the symmetric key of corresponding number, and utilizing symmetric key to be encrypted generating ciphertext to file, the encryption key of each file is all different; If based on encryption of blocks of data, data owner carries out piecemeal according to setting data block size by file centralized documentation, utilize Symmetric Cryptography to generate at random the symmetric key of corresponding number, and utilizing symmetric key to be encrypted generating ciphertext to data block, the encryption key of each data block is all different.
Step 4 specifically comprises following sub-step:
4.1 users send inquiry authorized application to data owner, data owner determines whether issue authorization token to user and for which file set according to its security strategy, includes the binary vector index of keyword set and the authority of authority collection in token;
4.2 data owners use general secure transport mechanism that token is sent to user.
Step 5 specifically comprises following sub-step:
First 5.1 build inquiry binary vector, its method is as follows: whether user builds inquiry binary vector according to searching keyword in keyword set, represent that with 1 corresponding keyword is present in keyword set, represent that with 0 corresponding keyword is not present in keyword set.
5.2 the index binary vector of inquiry binary vector and each file is carried out to inner product calculating, when inner product result of calculation is while being non-zero, show this file including searching keyword, in the time that inner product result of calculation is 0, show that this file does not comprise searching keyword.And the value of inner product result of calculation is larger, show that the keyword comprising is more.
Suppose r
idocument F
iscale-of-two index vector, wherein r
i{ 0,1} represents keyword w to [j] ∈
iwhether in document, exist; Q is a query vector, its Cao Q[j] { 0,1} represents keyword w to ∈
jwhether in searching keyword set W.Document F
icalculate i.e. rQ by inner product mode with the similarity score of searching keyword set W.
In step 6, build the new index binary vector method corresponding with searching keyword as follows: in the index binary vector of file, the binary digit of searching keyword correspondence position is retained, corresponding other non-searching keyword position is removed.
Step 7 specifically comprises following sub-step:
7.1 first build LSSS matrix according to query logic expression formula, its method is as follows: first root node vector is made as to (1), its vector length is 1, and variable c is initialized as to 1, father node uses vector v mark.If father node is OR door, child nodes is by v mark; If father node is AND door, left child nodes is v||1, right child nodes be (0 ... 0) ||-1,0 number is c, and c=c+1.Complete after the mark of whole tree, the row of leaf node composition LSSS matrix M, if length not etc., does not fill 0.
New index binary vector and LSSS matrix are carried out inner product calculating by 7.2, result of calculation that and if only if be (1,0,0 ..., 0) time, show that file meets querying condition, otherwise do not meet querying condition.
A kind of ciphertext cloud data complex query method, comprises data owner, user and high in the clouds, and data owner is used for using existing point word algorithm to extract keyword to its file set, and builds the binary vector index of All Files; Data owner also, for using Symmetric Cryptography to be encrypted to file, if based on data block, also will carry out piecemeal by setting data block size by file, then uses Symmetric Cryptography to be encrypted, and then the file of encryption is sent to high in the clouds; User is used for to the mandate of data owner's requesting query; Data owner is also for providing authorization token according to appointment security strategy to user; User is also for using token information to build inquiry binary vector; User also carries out inner product calculating to judge whether file comprises searching keyword for the index binary vector that uses inquiry binary vector and All Files; User is also for building the new index binary vector corresponding with searching keyword; User is also for searching keyword is generated to LSSS matrix according to logical expression, and new index binary vector and LSSS matrix are carried out to inner product calculating; User is the file cipher text for comprising searching keyword to high in the clouds request also, and uses the file secret key decryption file comprising in token; High in the clouds is used for store data, and responds user's read-write requests.
The above technical scheme of conceiving by the present invention, compared with prior art, the present invention has following advantage:
1. query accuracy is high, uses query logic expression formula can represent complicated querying condition, uses LSSS matrix can obtain the Query Result conforming to completely with query logic expression formula.
2. Data Update is convenient, the process of setting up index is completed by data owner, keyword set information is by data owner's keeping, in the time having file to upgrade, data owner only needs the binary vector index of updating file, and encrypt file again, then the file of encryption is sent to high in the clouds.
3. use binary vector inner product to calculate very efficient, only need to increase a small amount of storage at user side just can realize efficient retrieval.
Brief description of the drawings
Fig. 1 is each entity relationship diagram involved in the present invention.
Fig. 2 is the inventive method process flow diagram.
Fig. 3 is binary vector key map of the present invention.
Fig. 4 is LSSS matrix construction figure of the present invention.
Embodiment
In order to make object of the present invention, technical scheme and advantage clearer, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein, only in order to explain the present invention, is not intended to limit the present invention.
Below be first explained and illustrated with regard to technical term of the present invention:
Data owner: refer to the owner of file, file need to be stored in cloud, and formulate the access control policy of file;
User: the file that needs reading out data owner to issue;
High in the clouds or cloud storage: storage data owner's file, the operation requests that the loyal executing data owner of meeting and validated user send, but can peep file content in the time of conditions permit;
File: data owner need to be uploaded to the data in high in the clouds;
Blocks of files: file block, data owner adopts different encryption keys to the different piecemeals of identical file;
Symmetric Cryptography: be a kind of conventional cipher mechanism, encryption and decryption adopt same key, and efficiency is higher, adopts in the present invention this encryption mechanism file or blocks of files;
Symmetric key: the random binary data generating in Symmetric Cryptography;
LSSS: linear secret sharing scheme is the abbreviation of its English full name Linear Secret Sharing Scheme.
Below in conjunction with embodiment and accompanying drawing, the present invention will be further described.
As shown in Figure 1, ciphertext cloud data complex query method of the present invention is to be applied in to encrypt in cloud storage system, and this system comprises data owner, user and high in the clouds.
In the present embodiment, data owner is certain secretary of R&D institution, reaches the scientific research project file of the data Shi Gai unit in high in the clouds, and in the unit of being mainly used in, personnel include the data sharing of the personnel that travel outside in project application and performance history.
As shown in Figure 2, ciphertext cloud data complex query method of the present invention comprises the following steps:
Step 1. data owner, to its file set index building, uses binary vector index, and in index, each represents a keyword, represents with 0 and 1 whether corresponding keyword is present in this file, as shown in Figure 3.This step specifically comprises following sub-step:
1.1 data owners use existing point of word algorithm to extract keyword to its file set, build keyword set; For example, as shown in Figure 3, keyword set { data retrieval, binary vector are encrypted in cloud computing, cloud storage }.
1.2 data owner builds binary vector index according to the corresponding keyword whether comprising in each file in keyword set, represents that with 1 corresponding keyword is present in this file, represents that with 0 corresponding keyword is not present in this file.
For example, as shown in Figure 3, file 1 comprises keyword { cloud computing is encrypted }, and its index binary vector is f
1=(1,0,1,0,0), file 2 comprises keyword { data retrieval, binary vector are encrypted in cloud storage }, and its index binary vector is f
2=(0,1,1,1,1).
Step 2. data owner uses Symmetric Cryptography encrypt file collection (can based on Single document or data block);
Encrypt file collection is sent to high in the clouds by step 3. data owner;
When step 4. user requires to access the file that comprises some keyword, apply for query token to data owner, in query token, include the binary vector index of keyword set and All Files.This step specifically comprises following sub-step:
4.1 users send inquiry authorized application to data owner, data owner determines whether issue authorization token to user and for which file set according to its security strategy, includes the binary vector index of keyword set and the authority of authority collection in token;
4.2 data owners use general secure transport mechanism that token is sent to user.
Step 5. user builds inquiry binary vector according to searching keyword and keyword set, and inquiry binary vector and the index binary vector of each file are carried out to inner product calculating judges whether this file comprises user's searching keyword.This step specifically comprises following sub-step:
First 5.1 build inquiry binary vector, its method is as follows: whether user builds inquiry binary vector according to searching keyword in keyword set, represent that with 1 corresponding keyword is present in keyword set, represent that with 0 corresponding keyword is not present in keyword set.
5.2 the index binary vector of inquiry binary vector and each file is carried out to inner product calculating, when inner product result of calculation is while being non-zero, show this file including searching keyword, in the time that inner product result of calculation is 0, show that this file does not comprise searching keyword.And the value of inner product result of calculation is larger, show that the keyword comprising is more.
For example, establishing searching keyword is: w
1=" cloud computing, w
2=" cloud storage ", w
3=" encryption ", w
4=" data retrieval ", query expression is: (w
1or w
2) and w
3and w
4, inquiring about binary vector is q=(1,1,1,1,0).
For example, as shown in Figure 3, file 1 comprises keyword { cloud computing is encrypted }, and its index binary vector is f
1=(1,0,1,0,0), file 2 comprises keyword { data retrieval, binary vector are encrypted in cloud storage }, and its index binary vector is f
2=(0,1,1,1,1).The index vector of query vector and file 1 is carried out to inner product calculating: qf
1=(1,1,1,1,0) (1,0,1,0,0)
-1=2, the index vector of query vector and file 2 is carried out to inner product calculating: qf
2=(1,1,1,1,0) (0,1,1,1,1)
-1=3.
If this file of step 6. includes searching keyword, further build the new index binary vector corresponding with searching keyword;
In step 6, build the new index binary vector method corresponding with searching keyword as follows: in the index binary vector of file, the binary digit of searching keyword correspondence position is retained, corresponding other non-searching keyword position is removed.
For example, the new index binary vector of file 1 is f
1'=(1,0,1,0), the new index binary vector of file 2 is f
2'=(0,1,1,1).
Step 7. user generates LSSS matrix by searching keyword according to logical expression, and new index binary vector and LSSS matrix are carried out to inner product calculating further to judge whether this file meets query logic expression formula.This step specifically comprises following sub-step:
7.1 first build LSSS matrix according to query logic expression formula, its method is as follows: first root node vector is made as to (1), its vector length is 1, and variable c is initialized as to 1, father node uses vector v mark.If father node is OR door, child nodes is by v mark; If father node is AND door, left child nodes is v||1, right child nodes be (0 ... 0) ||-1,0 number is c, and c=c+1.Complete after the mark of whole tree, the row of leaf node composition LSSS matrix M, if length not etc., does not fill 0.
New index binary vector and LSSS matrix are carried out inner product calculating by 7.2, result of calculation that and if only if be (1,0,0 ..., 0) time, show that file meets querying condition, otherwise do not meet querying condition.
For example, find the file that meets querying condition, first construct LSSS matrix, see Fig. 4.Building method is as follows: first root node vector is made as to (1), its vector length is 1, and variable c is initialized as to 1, and father node uses vector v mark.If father node is OR door, child nodes is by v mark; If father node is AND door, left child nodes is v||1, right child nodes be (0 ... 0) ||-1,0 number is c, and c=c+1.Complete after the mark of whole tree, the row of leaf node composition LSSS matrix M, if length not etc., does not fill 0.
After matrix M construction complete, inquire about one by one the index vector of each file, the new index binary vector of file 1 is f
1'=(1,0,1,0), calculate f
1' M=(1,0,1), therefore file 1 does not meet querying condition.The new index binary vector of file 2 is f
2'=(0,1,1,1), calculate f
2' M=(1,0,0), therefore file 2 meets querying condition.
If a Chinese character accounts for 2 bytes, a keyword is made as maximum 5 Chinese characters, accounts for 10 bytes, supposes to have 1000 keywords, and storage keyword set only needs the storage space of 10K byte.The binary vector index size of each file is 1000, approximately 12 bytes, and 1000 files, only need the index stores space of 12K byte.
Those skilled in the art will readily understand; the foregoing is only preferred embodiment of the present invention and oneself; not in order to limit the present invention, all any amendments of doing within the spirit and principles in the present invention, be equal to and replace and improvement etc., within all should being included in protection scope of the present invention.
Claims (8)
1. a ciphertext cloud data complex query method, is characterized in that, comprises the following steps:
Step 1. data owner, to its file set index building, uses binary vector index, and in index, each represents a keyword, represents with 0 and 1 whether corresponding keyword is present in this file;
Step 2. data owner uses Symmetric Cryptography encrypt file collection based on Single document or data block;
Encrypt file collection is sent to high in the clouds by step 3. data owner;
When step 4. user requires to access the file that comprises some keyword, apply for query token to data owner, in query token, include the binary vector index of keyword set and All Files;
Step 5. user builds inquiry binary vector according to searching keyword and keyword set, and inquiry binary vector and the index binary vector of each file are carried out to inner product calculating judges whether this file comprises user's searching keyword;
If this file of step 6. includes searching keyword, further build the new index binary vector corresponding with searching keyword;
Step 7. user generates LSSS matrix by searching keyword according to logical expression, and new index binary vector and LSSS matrix are carried out to inner product calculating further to judge whether this file meets query logic expression formula.
2. ciphertext cloud data complex query method according to claim 1, is characterized in that, step 1 specifically comprises following sub-step:
1.1 data owners use existing point of word algorithm to extract keyword to its file set, build keyword set;
1.2 data owner builds binary vector index according to the corresponding keyword whether comprising in each file in keyword set, represents that with 1 corresponding keyword is present in this file, represents that with 0 corresponding keyword is not present in this file.
3. ciphertext cloud data complex query method according to claim 1, it is characterized in that, in step 2, if encrypt based on Single document, data owner is according to quantity of documents in file set, utilize Symmetric Cryptography to generate at random the symmetric key of corresponding number, and utilize symmetric key to be encrypted generating ciphertext to file, the encryption key of each file is all different; If based on encryption of blocks of data, data owner carries out piecemeal according to setting data block size by file centralized documentation, utilize Symmetric Cryptography to generate at random the symmetric key of corresponding number, and utilizing symmetric key to be encrypted generating ciphertext to data block, the encryption key of each data block is all different.
4. ciphertext cloud data complex query method according to claim 1, is characterized in that, step 4 specifically comprises following sub-step:
1.1 users send inquiry authorized application to data owner, data owner determines whether issue authorization token to user and for which file set according to its security strategy, includes the binary vector index of keyword set and the authority of authority collection in token;
1.2 data owners use general secure transport mechanism that token is sent to user.
5. ciphertext cloud data complex query method according to claim 1, is characterized in that, step 5 specifically comprises following sub-step:
First 1.1 build inquiry binary vector, its method is as follows: whether user builds inquiry binary vector according to searching keyword in keyword set, represent that with 1 corresponding keyword is present in keyword set, represent that with 0 corresponding keyword is not present in keyword set;
The index binary vector of inquiry binary vector and each file is carried out inner product calculating by 1.2, when inner product result of calculation is while being non-zero, show this file including searching keyword, in the time that inner product result of calculation is 0, show that this file does not comprise searching keyword, and the value of inner product result of calculation is larger, show that the keyword comprising is more.
6. ciphertext cloud data complex query method according to claim 1, it is characterized in that, in step 6, build the new index binary vector method corresponding with searching keyword as follows: in the index binary vector of file, the binary digit of searching keyword correspondence position is retained, corresponding other non-searching keyword position is removed.
7. ciphertext cloud data complex query method according to claim 1, is characterized in that, step 7 specifically comprises following sub-step:
First 1.1 build LSSS matrix according to query logic expression formula, and its method is as follows: first root node vector is made as
(1), its vector length is 1, and by variable
cbe initialized as 1, father node uses vector
v mark; If father node is OR door, child nodes by
v mark; If father node is AND door, left child nodes is
v || 1, right child nodes is
(0 ... 0)||
-1, 0 number is
c, and
c=
c+ 1; Complete after the mark of whole tree leaf node composition LSSS matrix
m row, if length not etc., does not fill 0;
New index binary vector and LSSS matrix are carried out inner product calculating by 1.2, and result of calculation that and if only if is
(1,0,0 ..., 0)time, show that file meets querying condition, otherwise do not meet querying condition.
8. a ciphertext cloud data complex query method, comprises data owner, user and high in the clouds, it is characterized in that,
Data owner is used for using existing point word algorithm to extract keyword to its file set, and builds the binary vector index of All Files;
Data owner also, for using Symmetric Cryptography to be encrypted to file, if based on data block, also will carry out piecemeal by setting data block size by file, then uses Symmetric Cryptography to be encrypted, and then the file of encryption is sent to high in the clouds;
User is used for to the mandate of data owner's requesting query;
Data owner is also for providing authorization token according to appointment security strategy to user;
User is also for using token information to build inquiry binary vector;
User also carries out inner product calculating to judge whether file comprises searching keyword for the index binary vector that uses inquiry binary vector and All Files;
User is also for building the new index binary vector corresponding with searching keyword;
User is also for searching keyword is generated to LSSS matrix according to logical expression, and new index binary vector and LSSS matrix are carried out to inner product calculating;
User is the file cipher text for comprising searching keyword to high in the clouds request also, and uses the file secret key decryption file comprising in token;
High in the clouds is used for store data, and responds user's read-write requests.
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| CN110069604A (en) * | 2019-04-23 | 2019-07-30 | 北京字节跳动网络技术有限公司 | Text search method, apparatus and computer readable storage medium |
| CN110727835A (en) * | 2019-10-17 | 2020-01-24 | 浙江中智达科技有限公司 | Data query method, device and system |
| CN110727835B (en) * | 2019-10-17 | 2021-03-12 | 浙江中智达科技有限公司 | Data query method, device and system |
| CN112256839A (en) * | 2020-11-11 | 2021-01-22 | 深圳技术大学 | Ciphertext searching method, device and system and computer readable storage medium |
| WO2022099496A1 (en) * | 2020-11-11 | 2022-05-19 | 深圳技术大学 | Ciphertext search method, apparatus and system, and computer-readable storage medium |
| CN112256839B (en) * | 2020-11-11 | 2023-07-07 | 深圳技术大学 | Ciphertext search method, ciphertext search device, ciphertext search system and computer-readable storage medium |
| CN115098649A (en) * | 2022-08-25 | 2022-09-23 | 北京融数联智科技有限公司 | Keyword search method and system based on double-key accidental pseudorandom function |
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