CN107622212A - A kind of mixing cipher text retrieval method based on double trapdoors - Google Patents
A kind of mixing cipher text retrieval method based on double trapdoors Download PDFInfo
- Publication number
- CN107622212A CN107622212A CN201710954119.6A CN201710954119A CN107622212A CN 107622212 A CN107622212 A CN 107622212A CN 201710954119 A CN201710954119 A CN 201710954119A CN 107622212 A CN107622212 A CN 107622212A
- Authority
- CN
- China
- Prior art keywords
- double
- trapdoors
- mixing
- method based
- retrieval
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Landscapes
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a kind of mixing cipher text retrieval method based on double trapdoors, comprise the steps of：S1, data file pretreatment；S2, data file encryption；S3, index construction and encryption；S4, retrieval request is generated to trapdoor vector；S5, user download retrieval file.The present invention supports multi-key word and fuzzy word searching ciphertext, adds the security of retrieving, and search can be encrypted by realizing the efficient keyword for supporting sequence, improve effectiveness of retrieval and precision ratio.
Description
Technical field
The present invention relates to database retrieval field, and in particular to a kind of mixing cipher text retrieval method based on double trapdoors.
Background technology
Searching ciphertext is the significant embodiment of availability of data in cloud computing.At present, the multi-key word in cloud computing obscures
The problems such as efficiency is low, security is poor be present in retrieval scheme.A kind of this mixing searching ciphertext based on double trapdoor technologies proposed
Scheme in performance advantageously：On the one hand the double index structures introduced can be used to support keywords-based retrieval；Secondly, introducing
(Dictionary based on Fuzzy Set Con-struction, are based on by Huffman (Huffman) code tree and DFSC
The fuzzy structure set of dictionary) index structure is improved, improve recall precision and reduce index memory space；Finally, pass through
TF-IDF (Term Frequency-Inverse Document Frequency) rule conceals keyword word frequency, increase inspection
The security of rope process.
The comprehensive advantage of this method, which is embodied in improve effectiveness of retrieval and reduce, indexes storage overhead.However, with
The continuous development of cloud computing technology, many are related to the data of user privacy information and are uploaded in cloud so that user loses pair
The absolute control of these private datas, these private datas cause user to depositing by the safety problem of malicious sabotage and leakage
Data in Chu Yun represent greatly worry.As cloud service provider Saleforce.com is caused largely by security attack
Cloud tenant private data be compromised and lose, two employees of Google successfully invade the Google of tenant
The accounts such as Voice, Gtalk obtain the account password leakage thing of privacy information, apple iCloud leakage door event and Dropbox
Part etc..These private data leakage events make it that protection of the cloud service provider to private data is also self-doubt.
In order to protect privacy of user, effective retrieval service is provided the user with.Data owner is when uploading data, originally
Scheme generates double trapdoors to adapt to keywords-based retrieval and fuzzy search, takes double indexes to generate one to each keyword suddenly
Fu Man (Huffman) value, data owner to data file and index after encrypting and uploading cloud server end, by the credible 3rd
Direction cloud server end returns changed according to the retrieval request of data consumer after the trapdoor that generates, last Cloud Server is to data
User, which returns, meets search condition and degree of correlation highest file, and Cloud Server whole process can not all know file and rope during being somebody's turn to do
The content drawn, has accomplished the highly confidential property to data file, and also rare document report at present.
The content of the invention
It is an object of the invention to provide a kind of mixing cipher text retrieval method based on double trapdoors, applied to multi-key word and
Fuzzy word searching ciphertext, the inquiry privacy of user is protected, realize the efficient keyword encryption search for supporting sequence, increase
The security of retrieving simultaneously improves effectiveness of retrieval and precision ratio.
In order to achieve the above object, the present invention provides a kind of mixing cipher text retrieval method based on double trapdoors, comprising following
Step：
S1, data file pretreatment；
S2, data file encryption；
S3, index construction and encryption；
S4, retrieval request is generated to trapdoor vector；
S5, user download retrieval file.
A kind of above-mentioned mixing cipher text retrieval method based on double trapdoors, wherein, in the step S1, to data file
Pretreatment includes keyword extraction and fuzzy set of words construction.
A kind of above-mentioned mixing cipher text retrieval method based on double trapdoors, wherein, in the step S1, to primary data text
Part collection F=(f1, f2, f3 ..., fn) and corresponding keyword set W=(w'1 w'2 w'3,...,w'n) carry out degree of correlation scoring：
Sc(w′i, F) and=∑fn∈FSc(w′i,fn)
Wherein, Sc (wi ', F ') is file set F overall score, Sc (w 'i,fn) it is that each subset is commented in file set F
Point.
A kind of above-mentioned mixing cipher text retrieval method based on double trapdoors, wherein, in the step S2, input primary data
File set F=(f1, f2, f3 ..., fn), output ciphertext F ' is encrypted to F by symmetric encipherment algorithm, while generate and refer to accordingly
Show two invertible matrix of vector sum, ultimately produce a triple key SK.
A kind of above-mentioned mixing cipher text retrieval method based on double trapdoors, wherein, in the step S3, data owner is first
Single keyword fuzzy index and keywords-based retrieval index is constructed, and described two indexes are encrypted, then will encryption
Index after processing is uploaded to Cloud Server.
A kind of above-mentioned mixing cipher text retrieval method based on double trapdoors, wherein, in the step S4, pass through single keyword
The authorized user of fuzzy search sends search key to trusted third party, and trapdoor generating algorithm construction calls in trusted third party
Trapdoor set, including Huffman (Huffman) encoded radio.
A kind of above-mentioned mixing cipher text retrieval method based on double trapdoors, wherein, in the step S4, pass through multi-key word
The authorized user of retrieval sends a series of keywords, and multiple crucial phrases are synthesized retrieval vector by trusted third party, and are multiplied by
Encrypted after random number.
A kind of above-mentioned mixing cipher text retrieval method based on double trapdoors, wherein, in the step S5, pass through single keyword
The data consumer of fuzzy search, which sends Huffman (Huffman) encoded radio to Cloud Server, Cloud Server, passes through this Hough
Graceful (Huffman) encoded radio combination indexed search goes out corresponding node, then the fuzzy word encrypted under node is mapped into ciphertext, retrieval
Whether contain the ciphertext under the node.
A kind of above-mentioned mixing cipher text retrieval method based on double trapdoors, wherein, in the step S5, multi-key word obscures
Score value is obtained in retrieval, and builds matrix, calculates the degree of correlation scoring of retrieval vector sum index, and scoring summation is obtained most
Degree of correlation scoring afterwards, finally the two is all the ID that user's score value highest cryptograph files are returned to using heapsort, and by
User downloads retrieval.
The advantages of the present invention are：
(1) multi-key word and fuzzy word searching ciphertext are supported, adds the security of retrieving.Pass through TF-IDF
(Term Frequency-Inverse Document Frequency) rule conceals keyword word frequency, introduces Huffman
(Huffman) index information, is blurred, in Cloud Server by tree construction and the double index structures of construction by the dimension for increasing matrix
If the no key of attacker can not be inferred to index information, while the generation of trapdoor and indexing key words calculating also have certain difference
It is different, even if attacker has broken trapdoor Crypted password, also can not accurately know real keyword so that except data owner and
Either party beyond authorized user can not know data and index information.Therefore, this greatly protects data
Safety and the inquiry privacy of user, add the security of retrieving.
(2) search can be encrypted by realizing the efficient keyword for supporting sequence.By constructing double indexes, Huffman is introduced
(Huffman) set, each keyword is encoded, Huffman (Huffman) is set according to the search condition of data user into
The efficient traversal of row, when traversing leaf node, search condition is mapped in dictionary L, to judge the scoring of keyword, finally
Return to user's scoring highest data file.It is thereby achieved that the efficient keyword encryption search for supporting sequence.
(3) effectiveness of retrieval and precision ratio are improved.The design object this time proposed can not only realize fuzzy word and search,
And the keywords-based retrieval framework of design, the result that user file sorts with keyword relevance is returned to, works as search condition
When being on the increase, retrieve that the time used is also more and more lower therewith, and efficiency also just improves therewith.This improves data text
Part effectiveness of retrieval and precision ratio.
Brief description of the drawings
Technical scheme in order to illustrate the embodiments of the present invention more clearly, institute in being described below to the embodiment of the present invention
The accompanying drawing needed to use is briefly described, it should be apparent that, drawings in the following description are only embodiments of the invention, right
For those of ordinary skill in the art, on the premise of not paying creative work, can also be according to embodiments of the present invention
Content and these accompanying drawings obtain other accompanying drawings.
Fig. 1 is the flow chart of the present invention.
Embodiment
Below in conjunction with accompanying drawing, by describing a preferable specific embodiment in detail, the present invention is further elaborated.
As shown in figure 1, a kind of mixing cipher text retrieval method based on double trapdoors, is comprised the steps of：
S1, data file pretreatment；
S2, data file encryption；
S3, index construction and encryption；
S4, retrieval request is generated to trapdoor vector；
S5, user download retrieval file.
Further, in the step S1, the pretreatment to data file includes keyword extraction and fuzzy set of words structure
Make.To initial data file collection F=(f1, f2, f3 ..., fn) and corresponding keyword set W=(w '1w′2w′3,...,w′n) enter
The row degree of correlation scores.
Specifically construction process is：
S11, keyword extraction：It is input with initial data file collection F=(f1, f2, f3 ..., fn), passes through segmentation methods
Extract corresponding keyword set W=(w '1w′2w′3,...,w′n), and each subset is calculated by formula (1) as follows
Degree of correlation scoring Sc (w 'i,fn), then file set F general comment is divided intoIncluded in file
Keyword wiSet of identifiers be ID={ id1, id2, id3... idn}。
Wherein wiRefer to keyword, w 'iRefer to taking the keyword after segmentation methods extraction, | fj| refer to file
Length, fj,tThe frequency that keyword wi occurs hereof is referred to, N represents the total number of files in file set, ftRepresent text
Occurs keyword w in part setiFrequency, Sc (w 'i,fn) refer to that the degree of correlation scores, idnRefer to by comprising keyword
File identifier, W refer to the keyword in index and search condition, take keyword the keyword extracted after segmentation methods
Set.
S12, fuzzy word set construction：It is each keyword w ' using DFSC technologiesiConstruct a fuzzy setWherein, d is editing distance, and L is all fuzzy word w of stipulationsnDictionary,Refer to
Editing distance is d, keyword w ' i fuzzy set of words.
Further, in the step S2, initial data file collection F=(f1, f2, f3 ..., fn) is inputted, by symmetrical
AES encrypts output ciphertext F ' to F, while generates corresponding instruction two invertible matrix of vector sum, ultimately produces one three
Tuple key SK.Described instruction vector is that have indicative vector by what symmetric encipherment algorithm generated to file F.
Data file encryption specific method be：Input initial data file collection F=(f1, f2, f3..., fn), by right
Claim AES to encrypt output ciphertext F ' to F, while generate the instruction vector Y and two l × l invertible matrix of (i+2) position
{N1, N2, a triple key SK is ultimately produced, is：
SK={ Y, N1, N2}
Wherein F ' refer to taking file symmetric encipherment algorithm export after ciphertext, Y refers to an instruction of generation
Vector, N1, N2Refer to two invertible matrix of generation.
Further, in the step S3, data owner first constructs single keyword fuzzy index and keywords-based retrieval
Index, and described two indexes are encrypted, then the index after encryption is uploaded to Cloud Server.
Specific index construction and encryption method are：
S31, construction fuzzy index I1, introducing Huffman (Huffman) is set to realize during the index constructs.Root node is pass
Keyword w '1In file set F scoring Sc (w '1, F), leaf node is keyword w '1It is the 1 fuzzy key for arriving d in editing distance
Word, i.e.,Leaf node and its root node constitute the subtree of Huffman (Huffman) treeWill
SubtreeOperation is merged, two minimum stalk trees of Sc values are chosen in subtree collection and are merged, generation is new
Node be left and right subtree value Sc sums, with this merge untilIt is integrated into tree, is until being configured to one tree
Only.Huffman (Huffman) coding is carried out to each node, since the minimum leafy node of Sc values, left and right subtree is marked respectively
Note 0,1, then encodes to each node, is configured to last Huffman (Huffman) index tree.Wherein Sc refers to closing
Degree of correlation scoring of the keyword based on TF-IDF rules, i.e. Sc (w '1, F),In Huffman (Huffman) tree for referring to construction
Subtree.
S32, construction keywords-based retrieval index I2, according to the keyword extracted in pre-treatment step to data file set F
Set W structural matrixes A'(i × n), shown in computational methods such as formula (2).In formula (2), to matrix A ' (i × n) carry out it is unusual
Value is decomposed, and obtains A'=C' × D' × E', wherein C'(i × s), D'(s × s), E'(s × n) and, D' is diagonal matrix, on diagonal
For A' singular value, larger k singular value (k≤s) is chosen in D', k row are chosen in C' and k rows are chosen in E',
And to Matrix C ' and E' dimensionality reductions be k-space size, then rebuild matrix A, A=C' × D' × E' ≈ A'.Finally to square
Battle array A (i+1) dimension expands to random number θ, θ ≠ 0, and (i+1) dimension expands to 1, then matrix is changed into：
Wherein A' be keyword set W associated data files collection F rating matrix, C', D', E' refer to matrix A ' carry out
Matrix obtained by after singular value decomposition, A refer to the matrix reconfigured according to space size, and θ refers to expanding matrix
The random number of exhibition.
S33, to index I1, I2It is encrypted.I1OutputHuffman (Huffman) encoded radio；I2OutputData file after encryption and index are uploaded to Cloud Server.WhereinRefer to asking
Take N1, N2The transposed matrix of matrix.
Further, in the step S4, sent out by the authorized user of single keyword fuzzy search to trusted third party
Search key is sent, trusted third party calls the construction trapdoor set of trapdoor generating algorithm, compiled including Huffman (Huffman)
Code value.Described trapdoor refers to the set of keywords of he input corresponding safety of the data consumer using key generation
Thresholding.The process of single keyword fuzzy search trapdoor generation is：Authorized user sends search key Q to trusted third party,
Trapdoor generating algorithm construction trapdoor set TQ calls in trusted third party, and it is { TQ, F { Q } } to send to Cloud Server.Wherein Q refers to
Be data user send search key,TQ refers to trapdoor set, is Huffman (Huffman) code value,
Q conversions are drawn by the index that data owner sends by trusted third party.
Further, in the step S4, a series of keywords are sent by the authorized user of keywords-based retrieval, can
Believe that multiple crucial phrases are synthesized retrieval vector by third party, and encrypted after being multiplied by random number.Described retrieval vector refer to by
Retrieval request is expressed by the vector pattern in mathematics.Multi-key word fuzzy search trapdoor generation process be：It is authorized to and uses
Family gives a series of keywords, and the multiple crucial phrases provided synthesis is retrieved vectorial P by trusted third party first, and P is extended
It is 1 into (i+1) dimension, multiplied by with random number τ, (τ ≠ 0), then (i+2) dimension is set to random number σ, then vectorial P expands to (i+
2) tie up, after encryptionWherein P refer to retrieval vector, P1, P2 refer to extension after it is random to
Amount, τ, σ refer to the random number chosen.
Further, in the step S5, by the data consumer of single keyword fuzzy search by Huffman
(Huffman) encoded radio, which is sent to Cloud Server, Cloud Server, passes through this Huffman (Huffman) encoded radio combination indexed search
Go out corresponding node, then the fuzzy word encrypted under node is mapped into ciphertext, retrieve whether contain the ciphertext under the node.It is single crucial
Data consumer sends trapdoor collection and is bonded to Cloud Server, the trapdoor T that Cloud Server is sent by data consumer in word and searchQIn
Huffman (Huffman) coding contained, and according to index I1, corresponding node is retrieved, then it is fuzzy by what is encrypted under node
Word maps ciphertext Q, retrieves whether contain ciphertext Q under the node.Now, corresponding Sc values have been retrieved.Utilize heapsort
Algorithm returns to the ID of user's Sc value highest cryptograph files, and user is downloaded decryption.Wherein Q refers to using Huffman
(Huffman) ciphertext that coding maps after being encrypted to fuzzy word.
Further, in the step S5, score value is obtained in multi-key word fuzzy search, and builds matrix, calculates inspection
The degree of correlation scoring of rope vector sum index, and the degree of correlation for summing to the end by scoring scores, finally the two is all to utilize heap
Sequence returns to the ID of user's score value highest cryptograph files, and is downloaded and retrieved by user.What described degree of correlation scoring referred to
It is the standard for being used to measure the correlation degree between keyword and file based on TF-IDF rules.Described ID is referred to uniquely
The ID symbols that can be identified for that document, this identifier is provided by server.
In multi-key word fuzzy search commenting for each keyword is obtained according to the step of above-mentioned single keyword fuzzy search
Score value is designated as Sc', and Sc' is built into the matrix A as shown in formula (3) ", calculate and retrieve vectorial P and index I2The degree of correlation
Score sc, as shown in formula (4).And the degree of correlation for summing to the end by scoring scores shown in SC such as formula (5).SC values are entered
Row heapsort, user SC value highest cryptograph files ID are returned to, finally downloaded and retrieved from Cloud Server by user.
Specific formula for calculation is as follows：
SC=τ (APT+θ)+σ+A″ (5)
Embodiment
It is assumed that 50000 parts of english literature conducts have been downloaded at random from Springer, Elsevier and middle National IP Network at present
Test data, using RAM as 4G, CPU frequency 2.40GHz PC carries out emulation experiment as test platform, sets file number
To ciphertext when for 5000,10000,20000,30000,40000,50000 search key numbers being 5,10,20,30,40,50
Index generation time in retrieval, storage overhead, recall precision these three important parameters carry out emulation experiment, and with representational Li
Jin (transliteration：Lijin) propose to be contrasted suitable for the fuzzy keyword retrieval scheme of cloud computing, the data obtained such as following table institute
Show.
The index generation time of table 1
| Number of files/part | 5000 | 10000 | 20000 | 30000 | 40000 | 50000 |
| The present invention/s | 0.35 | 0.52 | 1.02 | 1.33 | 1.62 | 2.2 |
| Li Jin scheme/s | 0.24 | 0.28 | 0.34 | 0.51 | 0.72 | 1 |
As can be seen from Table 1 with the increase of retrieval file number, the present invention, which indexes, generates time sustainable growth, and Li
The time growth that Jin scheme indexes generation is slower, and because the present invention is meets multi-key word fuzzy search, generation is double to be indexed, and
Li Jin scheme only supports single mode to paste word and search, therefore present invention generation index time consume is big, is consumed in 50000 parts of files
Damage more than 2 times of the scheme for Li Jin.
Table 2 indexes memory space
| Number of files/part | 5000 | 10000 | 20000 | 30000 | 40000 | 50000 |
| The present invention/GB | 2.8 | 4.9 | 8.2 | 12.3 | 16.8 | 20.4 |
| Li Jin scheme/GB | 2.5 | 5.1 | 11.4 | 16.8 | 22.4 | 27.8 |
Expense is just indexed in table 2 with regard to two schemes to be compared, although the present invention is indexed using double, as in step S31
The index I constructed1Built and indexed using Huffman (Huffman) code tree, and leaf node fuzzy word is such as step S12
Described in same dictionary L mapping, reduce introduce fuzzy set memory space.Li Jin scheme is then as number of files increases
Long, the memory space shared by caused fuzzy set also becomes big.By the visible Li Jin of table 2 scheme index space-consuming with
Number of files increases, and is higher by than the present invention close to 50%.
The recall precision of table 3
| Search condition/ | 5 | 10 | 20 | 30 | 40 | 50 |
| The present invention/s | 1.49 | 1.51 | 1.64 | 1.73 | 1.87 | 2.01 |
| Li Jin scheme/s | 1.48 | 1.62 | 2.21 | 2.72 | 3.23 | 3.83 |
It can be seen that according to table 3, with increasing for search condition, advantage of the invention is fully shown.Be characterized in
Increasing for the search condition of offer, the time is more and more lower used in retrieval.And Li Jin scheme is because needing to keyword one by one
Retrieval, retrieval time loss increase as search condition increases.It is identical by the visible time used when search condition is 5 of table 3,
When search condition is 50, recall precision of the present invention improves 50%.
Technical scheme is described in detail above in association with accompanying drawing, although present disclosure is by above-mentioned excellent
Embodiment is selected to be discussed in detail, but it should be appreciated that the description above is not considered as limitation of the present invention.In ability
After field technique personnel have read the above, all it will be apparent for a variety of modifications and substitutions of the present invention.Therefore, originally
The protection domain of invention should be limited to the appended claims.
Claims (9)
1. a kind of mixing cipher text retrieval method based on double trapdoors, it is characterised in that comprise the steps of：
S1, data file pretreatment；
S2, data file encryption；
S3, index construction and encryption；
S4, retrieval request is generated to trapdoor vector；
S5, user download retrieval file.
A kind of 2. mixing cipher text retrieval method based on double trapdoors as claimed in claim 1, it is characterised in that the step S1
In, the pretreatment to data file includes keyword extraction and fuzzy set of words constructs.
A kind of 3. mixing cipher text retrieval method based on double trapdoors as claimed in claim 2, it is characterised in that the step S1
In, to initial data file collection F=(f1, f2, f3..., fn) and corresponding keyword set W=(w'1 w'2 w'3,...,w'n) enter
The row degree of correlation scores：
<mrow>
<mi>S</mi>
<mi>c</mi>
<mrow>
<mo>(</mo>
<msubsup>
<mi>w</mi>
<mi>i</mi>
<mo>&prime;</mo>
</msubsup>
<mo>,</mo>
<mi>F</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msub>
<mi>&Sigma;</mi>
<mrow>
<mi>f</mi>
<mi>n</mi>
<mo>&Element;</mo>
<mi>F</mi>
</mrow>
</msub>
<mi>S</mi>
<mi>c</mi>
<mrow>
<mo>(</mo>
<msubsup>
<mi>w</mi>
<mi>i</mi>
<mo>&prime;</mo>
</msubsup>
<mo>,</mo>
<msub>
<mi>f</mi>
<mi>n</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
Wherein, Sc (wi', F ') be file set F overall score, Sc (w 'i,fn) be file set F in each subset scoring.
4. a kind of mixing cipher text retrieval method based on double trapdoors as described in claim 1 or 2 or 3, it is characterised in that described
In step S2, initial data file collection F=(f are inputted1, f2, f3..., fn), output ciphertext is encrypted to F by symmetric encipherment algorithm
F ', while corresponding instruction two invertible matrix of vector sum are generated, ultimately produce a triple key SK.
5. a kind of mixing cipher text retrieval method based on double trapdoors as described in claim 1 or 2 or 3, it is characterised in that described
In step S3, data owner first constructs single keyword fuzzy index and keywords-based retrieval index, and to described two indexes
It is encrypted, then the index after encryption is uploaded to Cloud Server.
A kind of 6. mixing cipher text retrieval method based on double trapdoors as claimed in claim 5, it is characterised in that the step S4
In, search key is sent to trusted third party by the authorized user of single keyword fuzzy search, trusted third party calls
Trapdoor generating algorithm constructs trapdoor set, including huffman coding value.
A kind of 7. mixing cipher text retrieval method based on double trapdoors as claimed in claim 5, it is characterised in that the step S4
In, a series of keywords are sent by the authorized user of keywords-based retrieval, trusted third party synthesizes multiple crucial phrases
Retrieval vector, and encrypted after being multiplied by random number.
A kind of 8. mixing cipher text retrieval method based on double trapdoors as claimed in claim 6, it is characterised in that the step S5
In, huffman coding value is sent to Cloud Server, Cloud Server by the data consumer of single keyword fuzzy search and passed through
This huffman coding value combination indexed search goes out corresponding node, then the fuzzy word encrypted under node is mapped into ciphertext, and retrieval should
Whether contain the ciphertext under node.
A kind of 9. mixing cipher text retrieval method based on double trapdoors as claimed in claim 7, it is characterised in that the step S5
In, score value is obtained in multi-key word fuzzy search, and matrix is built, the degree of correlation scoring of retrieval vector sum index is calculated, and
The degree of correlation of summing to the end of scoring scores, it is last the two be all that to return to user's score value highest using heapsort close
The ID of file, and downloaded and retrieved by user.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710954119.6A CN107622212A (en) | 2017-10-13 | 2017-10-13 | A kind of mixing cipher text retrieval method based on double trapdoors |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710954119.6A CN107622212A (en) | 2017-10-13 | 2017-10-13 | A kind of mixing cipher text retrieval method based on double trapdoors |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107622212A true CN107622212A (en) | 2018-01-23 |
Family
ID=61092105
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710954119.6A Pending CN107622212A (en) | 2017-10-13 | 2017-10-13 | A kind of mixing cipher text retrieval method based on double trapdoors |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107622212A (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109492410A (en) * | 2018-10-09 | 2019-03-19 | 华南农业大学 | Data can search for encryption and keyword search methodology, system and terminal, equipment |
| CN109740362A (en) * | 2019-01-03 | 2019-05-10 | 中国科学院软件研究所 | A kind of ciphertext index generation and search method and system based on entropy coding |
| CN109885762A (en) * | 2019-01-25 | 2019-06-14 | 平安科技（深圳）有限公司 | Information retrieval method, device, storage medium and computer equipment |
| CN110489971A (en) * | 2018-05-15 | 2019-11-22 | 微软技术许可有限责任公司 | The data set management of safety |
| CN111541535A (en) * | 2020-04-17 | 2020-08-14 | 西南交通大学 | Boolean retrieval attribute-based encryption method capable of verifying search results |
| CN112527949A (en) * | 2020-12-15 | 2021-03-19 | 建信金融科技有限责任公司 | Data storage and retrieval method and device, computer equipment and storage medium |
| CN112836005A (en) * | 2019-11-25 | 2021-05-25 | 浙江树人学院（浙江树人大学） | Cipher text sequencing search method and system based on PCA |
| CN113158087A (en) * | 2021-04-09 | 2021-07-23 | 深圳前海微众银行股份有限公司 | Query method and device for space text |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102034049A (en) * | 2010-12-30 | 2011-04-27 | 华中科技大学 | Mass data compression, encryption, storage and retrieval system and using method thereof |
| CN103593476A (en) * | 2013-11-28 | 2014-02-19 | 中国科学院信息工程研究所 | Multi-keyword plaintext and ciphertext retrieving method and device oriented to cloud storage |
| CN106407447A (en) * | 2016-09-30 | 2017-02-15 | 福州大学 | Simhash-based fuzzy sequencing searching method for encrypted cloud data |
| CN106326360B (en) * | 2016-08-10 | 2019-04-09 | 武汉科技大学 | A kind of fuzzy keywords-based retrieval method of ciphertext data in cloud environment |
-
2017
- 2017-10-13 CN CN201710954119.6A patent/CN107622212A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102034049A (en) * | 2010-12-30 | 2011-04-27 | 华中科技大学 | Mass data compression, encryption, storage and retrieval system and using method thereof |
| CN103593476A (en) * | 2013-11-28 | 2014-02-19 | 中国科学院信息工程研究所 | Multi-keyword plaintext and ciphertext retrieving method and device oriented to cloud storage |
| CN103593476B (en) * | 2013-11-28 | 2017-01-25 | 中国科学院信息工程研究所 | Multi-keyword plaintext and ciphertext retrieving method and device oriented to cloud storage |
| CN106326360B (en) * | 2016-08-10 | 2019-04-09 | 武汉科技大学 | A kind of fuzzy keywords-based retrieval method of ciphertext data in cloud environment |
| CN106407447A (en) * | 2016-09-30 | 2017-02-15 | 福州大学 | Simhash-based fuzzy sequencing searching method for encrypted cloud data |
Non-Patent Citations (1)
| Title |
|---|
| 常良等: "云计算中基于双陷门技术的混合密文检索方案", 《计算机应用技术》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110489971A (en) * | 2018-05-15 | 2019-11-22 | 微软技术许可有限责任公司 | The data set management of safety |
| CN109492410A (en) * | 2018-10-09 | 2019-03-19 | 华南农业大学 | Data can search for encryption and keyword search methodology, system and terminal, equipment |
| CN109492410B (en) * | 2018-10-09 | 2020-09-01 | 华南农业大学 | Data searchable encryption and keyword search method, system, terminal and equipment |
| CN109740362A (en) * | 2019-01-03 | 2019-05-10 | 中国科学院软件研究所 | A kind of ciphertext index generation and search method and system based on entropy coding |
| CN109885762A (en) * | 2019-01-25 | 2019-06-14 | 平安科技（深圳）有限公司 | Information retrieval method, device, storage medium and computer equipment |
| CN112836005A (en) * | 2019-11-25 | 2021-05-25 | 浙江树人学院（浙江树人大学） | Cipher text sequencing search method and system based on PCA |
| CN112836005B (en) * | 2019-11-25 | 2022-05-17 | 浙江树人学院（浙江树人大学） | Cipher text sequencing search method and system based on PCA |
| CN111541535A (en) * | 2020-04-17 | 2020-08-14 | 西南交通大学 | Boolean retrieval attribute-based encryption method capable of verifying search results |
| CN111541535B (en) * | 2020-04-17 | 2021-12-28 | 西南交通大学 | Boolean retrieval attribute-based encryption method capable of verifying search results |
| CN112527949A (en) * | 2020-12-15 | 2021-03-19 | 建信金融科技有限责任公司 | Data storage and retrieval method and device, computer equipment and storage medium |
| CN112527949B (en) * | 2020-12-15 | 2023-01-13 | 建信金融科技有限责任公司 | Data storage and retrieval method and device, computer equipment and storage medium |
| CN113158087A (en) * | 2021-04-09 | 2021-07-23 | 深圳前海微众银行股份有限公司 | Query method and device for space text |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107622212A (en) | A kind of mixing cipher text retrieval method based on double trapdoors | |
| Pham et al. | Survey on secure search over encrypted data on the cloud | |
| US10922273B1 (en) | Forward-private dynamic searchable symmetric encryption (DSSE) with efficient search | |
| CN106934301B (en) | Relational database secure outsourcing data processing method supporting ciphertext data operation | |
| Wang et al. | Privacy-preserving ranked multi-keyword fuzzy search on cloud encrypted data supporting range query | |
| Wang et al. | Generalized pattern matching string search on encrypted data in cloud systems | |
| CN109471964A (en) | A kind of fuzzy multi-key word based on synset can search for encryption method | |
| Zhang et al. | PRMS: A personalized mobile search over encrypted outsourced data | |
| Guo et al. | Dynamic multi-keyword ranked search based on bloom filter over encrypted cloud data | |
| CN107704768A (en) | A kind of multiple key classification safety search method of ciphertext | |
| CN102708216A (en) | Word-segmentation organizing method and clustering method for ciphertext search | |
| Peng et al. | LS-RQ: A lightweight and forward-secure range query on geographically encrypted data | |
| CN109740378B (en) | Security pair index structure resisting keyword privacy disclosure and retrieval method thereof | |
| Zhang et al. | A dynamic searchable symmetric encryption scheme for multiuser with forward and backward security | |
| Cai et al. | EncELC: Hardening and enriching ethereum light clients with trusted enclaves | |
| CN104009981B (en) | A kind of real-time big data method for secret protection based on symmetric cryptography | |
| Zhao et al. | Forward privacy multikeyword ranked search over encrypted database | |
| Ba et al. | A Blockchain‐Based CP‐ABE Scheme with Partially Hidden Access Structures | |
| CN107342863A (en) | Public key encryption method that is a kind of while supporting conjunction and keyword query of extracting | |
| Xiong et al. | An efficient searchable symmetric encryption scheme for smart grid data | |
| Mohammed et al. | Secure Multi-keyword Similarity Search Over Encrypted Data With Security Improvement. | |
| Siwach et al. | Encrypted Search & Cluster Formation in Big Data | |
| Xu et al. | Symmetric searchable encryption with supporting search pattern and access pattern protection in multi‐cloud | |
| Handa et al. | An efficient approach for secure information retrieval on cloud | |
| Zhao et al. | Privacy-preserving ranked searchable encryption based on differential privacy |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180123 |