CN110069944A - Searchable encrypted data retrieval method and system - Google Patents
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
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- G06F21/6227—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database where protection concerns the structure of data, e.g. records, types, queries
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
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Abstract
The invention discloses a data retrieval method and a system capable of searching and encrypting, belonging to the field of network information security, wherein the method comprises the following steps: generating a data index associated with the original data by the data owner; encrypting the original data and the data index, sending the encrypted data and the data index to a cloud server to generate a decryption key, and sending the decryption key to an authorized data searcher; a data searcher inputs keywords, generates an encryption keyword index and sends the encryption keyword index to a cloud server; the cloud server matches the encrypted keyword index with the encrypted data index to obtain encrypted target data, and then returns the encrypted target data to the data searcher; the method for constructing the data index is safe and efficient, facilitates retrieval matching at the server, and enables a searcher to quickly find a retrieval target result.
Description
Technical field
The present invention relates to technical field of network information safety more particularly to it is a kind of can search for encryption data retrieval method and
System.
Background technique
The maximum characteristic of smart grid is its two-way communications capabilities, it can pass through intelligent electric meter remote collection user
Fine-grained energy expenditure data enable grid operator effectively to manage the demand and supply of electricity, reasonable by providing
Peak of power consumption is shifted in the measures such as Spot Price, has the function that " pin peak load " and load balance, to greatly improve electricity
The reliability and safety of net.
However, the relevant fine granularity consumption data of these users has revealed the electric quantity consumption mould of resident to a certain extent
Formula.For example be related to the leakage of residential power consumption and may result in the generation of the criminal offences such as burglary, thus intelligence electricity
The Privacy Protection of resident, which becomes, in table measurement data restricts smart grid development and a universal main bottleneck.
In terms of protecting resident's privacy, current research to be concentrated mainly on encryption and the data aggregate of measurement data, seldom
It is concerned about the query process of encryption data in smart grid, but traditional encryption data is more numerous in storage and retrieving
It is trivial, it needs further to promote whole efficiency.There is researcher's proposition, by some cryptogram search of cryptography and database field
Technological achievement is used for reference and is improved in the art.For example, by ammeter measure resident's data store in an encrypted form with
Transmission is to protect a kind of more effective means of data-privacy, and this scheme is just known as can search for encrypting
The data retrieval method of (Searchable Encryption, SE).
It is so-called to can search for encrypting, it is to store data on the server with encrypted test mode, utilizes the powerful calculating of server
Ability carries out the retrieval of keyword, the privacy without revealing any user to server.This not only obtains the privacy of user
Effective protection is arrived, and recall precision has also obtained significantly being promoted with the help of server.It can search for encryption technology
General process is broadly divided into 4 steps:
1) file encryption: data owner (Data Owner) locally using encryption key to the file that will be uploaded into
Row encryption, and by ciphertext upload server.
2) key word index (Trapdoor) generates: the data consumer (Data authorized by data owner
Searcher key word index) is generated using key pair keyword to be checked, is sent to server.
3) query and search: the concordance list of key word index and each upper transmitting file that server submits data consumer into
Row retrieval, returns to the cryptograph files comprising key word index keyword.
4) file decryption: data consumer is decrypted and is obtained using the cryptograph files that decruption key returns to Cloud Server
Obtain data.
Tradition can search for encrypting, building uploads the inadequate efficiently and accurately of method of the concordance list of data, and user is caused to exist
The problem of cannot being quickly found required data is still had when inquiry, therefore, constructs a kind of retrieval scheme of highly effective and safe
Just urgently further investigate.
Summary of the invention
In view of this, the purpose of the present invention is to provide a kind of data retrieval method that can search for encryption and systems.This hair
It is bright to generate data directory associated with initial data, data are pre-processed, data vector is formed；By hyperspace
Every dimension is divided into multiple sections, constructs mesh space, and according to predefined conditions grid division space；It is empty to the grid after division
Between convert and sub-clustering；Cluster in different mesh spaces is clustered, and distributes label.The present invention constructs data directory
Method can safely and efficiently express initial data, convenient for doing retrieval matching in server, searcher be enabled to be quickly found out retrieval
Objective result.
According to an aspect of the invention, there is provided a kind of data retrieval method that can search for encryption, which is characterized in that packet
Include following steps:
Step 1, data owner generates data directory associated with initial data；
Step 2, data owner encrypts initial data, encrypts to the data directory of generation, after encryption
Initial data and data directory be sent to Cloud Server；Decruption key is generated, decruption key is sent to authorized data
Searcher；
Step 3, data retrieval person inputs keyword and creates keyword search request in search；Generated encryption key
The cryptography key word indexing is sent to Cloud Server by word indexing；
Step 4, Cloud Server matches cryptography key word indexing and encrypted data directory according to matching algorithm,
The target data encrypted, returns again to data retrieval person；
Step 5, data retrieval person is decrypted using target data of the decruption key to encryption, obtains target data.
Further, step 1 data owner generation data directory associated with initial data includes:
Step 101, data are pre-processed, forms multi-C vector；
Step 102, every dimension of hyperspace is divided into multiple sections, constructs mesh space, and according to predefined conditions
Grid division space；
Step 103, convert simultaneously sub-clustering to the mesh space after division；
Step 104, the cluster in different mesh spaces is clustered, and distributes label.
Further, predefined conditions described in step 102 include first condition and second condition, and the first condition is every
Vector density is more than or equal to closeness threshold value in a dimension, and the second condition closes on net with divided for remaining mesh space
Grid space distance is less than or equal to distance threshold, and the distance threshold is variable, finishes until all spaces divide.
Further, the mesh space after step 103 pair divides convert and sub-clustering, comprising: to the grid after division
Space carries out wavelet transformation；According to characteristic threshold value to mesh space assignment, the assignment includes to more than or equal to characteristic threshold value
Space sets 1, otherwise sets 0, then to adjacent 1 or 0 difference sub-clustering.
Further, step 104 distribution label constructs data directory using R tree method.
According to another aspect of the present invention, the present invention provides a kind of data retrieval system that can search for encryption, feature
It is, including following equipment:
Data all devices, for generating data directory associated with initial data；Initial data is encrypted, it is right
The data directory of generation is encrypted, and encrypted initial data and data directory are sent to Cloud Server；It is close to generate decryption
Decruption key is sent to authorized data retrieval person by key；
Data retrieval device when for scanning for, inputting keyword and creating keyword search request；Encryption is generated to close
The cryptography key word indexing is sent to Cloud Server by key word indexing；It is solved using target data of the decruption key to encryption
It is close, obtain target data；
Cloud Server, for cryptography key word indexing and encrypted data directory to be matched according to matching algorithm,
The target data encrypted, returns again to data retrieval person.
A kind of data retrieval method can search for encryption provided by the invention and system, solve the rope that building uploads data
The inadequate efficiently and accurately of method for drawing table, asking for required data cannot be quickly found by causing user to still have in inquiry
Topic, constructs a kind of retrieval scheme of highly effective and safe.The method that the present invention constructs data directory can safely and efficiently be expressed original
Data enable searcher to be quickly found out searched targets result convenient for doing retrieval matching in server.
Detailed description of the invention
In order to keep the purpose of the present invention, technical scheme and beneficial effects clearer, the present invention provides following attached drawing and carries out
Illustrate:
Fig. 1 is a kind of data retrieval method flow diagram that can search for encryption provided in an embodiment of the present invention；
Fig. 2 is three-dimensional vector space cutting method schematic diagram provided in an embodiment of the present invention；
Fig. 3 is clustering operation schematic diagram in space provided in an embodiment of the present invention；
Fig. 4 is the clustering method schematic diagram of the cluster in different mesh spaces provided in an embodiment of the present invention；
Fig. 5 is R tree data directory method for building up schematic diagram provided in an embodiment of the present invention；
Fig. 6 is a kind of data retrieval system structure chart that can search for encryption provided in an embodiment of the present invention.
Specific embodiment
The present invention is further detailed with reference to the accompanying drawings of the specification.
Fig. 1 is a kind of data retrieval method flow diagram that can search for encryption provided in an embodiment of the present invention, the side
Method includes following five steps:
Step 1, data owner generates data directory associated with initial data；
Step 2, data owner encrypts initial data, encrypts to the data directory of generation, after encryption
Initial data and data directory be sent to Cloud Server；Decruption key is generated, decruption key is sent to authorized data
Searcher；
Step 3, data retrieval person inputs keyword and creates keyword search request in search；Generated encryption key
The cryptography key word indexing is sent to Cloud Server by word indexing；
Step 4, Cloud Server matches cryptography key word indexing and encrypted data directory according to matching algorithm,
The target data encrypted, returns again to data retrieval person；
Step 5, data retrieval person is decrypted using target data of the decruption key to encryption, obtains target data.
Preferably, step 1 data owner generation data directory associated with initial data includes:
Step 101, data are pre-processed, forms multi-C vector；
Step 102, every dimension of hyperspace is divided into multiple sections, constructs mesh space, and according to predefined conditions
Grid division space；
Step 103, convert simultaneously sub-clustering to the mesh space after division；
Step 104, the cluster in different mesh spaces is clustered, and distributes label.
For step 101, data are pre-processed, form multi-C vector.Illustrated with the data instance of electric network database,
There are the data fields in table 1:
Table 1
| File number | Name | Address | Electricity consumption | Charging unit price | …… |
| D1 | King two | South | 500 | 2 | …… |
| D2 | Zhang San | North | 200 | 2 | …… |
In conjunction with table 1, for arbitrary data i (i=1,2 ... N), such as name, ammeter IP, User ID, home address etc.
It will do it pretreatment first Deng, data owner, pre-process and Chinese word segmentation is carried out to the keyword of data itself and removes stop words
Processing, keyword can be individual character and is also possible to word, sentence, then according to the word in deactivated vocabulary by data to content of text
Identification has little significance but the very high word of the frequency of occurrences, symbol, punctuate and messy code etc. remove, such as " this, and, meeting is " or " this
Kind, those " etc., remaining word, word are the keyword of document data.Based on this, to any document data DiIt (generates corresponding
Data vector DCi=w | w1,w2,...,wn}.For example, DC1=w | ... and king, two, south, area, 5,0,2 ... }, it is noted that
It only enumerates herein, not limiting keyword must be individual word.
Calculate document data DCiThe weight of middle keyword.Also there are many prior art, the present invention uses weighing computation method
More common term frequency-inverse document frequency (Term Frequency-Inverse Document Frequency, TF-IDF) is calculated
Method is described.Wherein, TF refers to the frequency that some keyword occurs in the data,
W is the number that certain keyword occurs, and M is the total amount of keyword.IDF(Inverse Document Frequency)
Reverse document frequency refers to that IDF is bigger if the data comprising keyword are fewer, then illustrates that entry has good classification area
The ability of dividing,
Wherein, D indicates total data number, DwIndicate the data number comprising certain keyword.
According to low document frequency of certain keyword frequency and the keyword in a certain data in entire data acquisition system
Rate can produce out the term frequency-inverse document frequency TF-IDF of high weight,
TF-IDF=TF*IDF,
Data are turned to the vector with n dimension, as described in Table 2 by the finally weight according to keyword in the data.
Table 2
| … | King | ? | … | South | North | … | 2 | 0 | 5 | … | |
| DC1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 1 | 0 |
| DC2 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 2 | 0 | 0 |
Then it is found that for DC1, vector is expressed asDC2It is then similar.
For safeguard safety, for n dimension data vector, can also add it is some obscure dimension, obscure dimension by u table
Show, so that data vector becomes (n+u) dimension.
For step 102, every dimension in multi-C vector space is divided into multiple sections, constructs mesh space, and according to pre-
Definition condition grid division space.
In multi-C vector space, each dimension is divided into m sections, for convenient for indicate, by taking three-dimensional vector space as an example, such as
Shown in Fig. 2, X-axis, Y-axis, Z axis are carried out respectively to be uniformly cut into m=3 segment, and by the figure after cutting with grid-shaped
Formula indicates.
Preferably, predefined conditions described in step 102 include first condition and second condition, and the first condition is each
Vector density is more than or equal to closeness threshold value in dimension, and the second condition is remaining mesh space and divided meshes
Space length is less than or equal to distance threshold, and the distance threshold is variable, finishes until all spaces divide.
For step 103, convert simultaneously sub-clustering to the mesh space after division.
Preferably, the present invention uses improved WaveCluster clustering algorithm, and recall precision can be improved.
For expanded all n+u dimension data vector DCi, the grid of division is subjected to wavelet transformation, is obtained new
Space lattice.The first predefined characteristic threshold value Y is set, it, should when the parameter in any one space lattice is more than or equal to Y
Space sets 1, otherwise sets 0.
Then to adjacent 1 or 0 difference sub-clustering.Specific clustering process is as shown in Figure 3.In Fig. 3 (a), by each dimension,
I.e. X/Y/Z axis has all been divided into 3 sections, forms the space lattice of 3*3*3 in entire space, also can be regarded as the net of 3 layers of 3*3
Lattice.For each layer namely any one m segment mesh of each dimension, the clustering process such as Fig. 3 (b) is carried out, i.e., by net
It is adjacent for cluster between 1 and 1 in lattice, it is adjacent for cluster between 0 and 0.
For step 104, the cluster in different mesh spaces is clustered, and distributes label.
After completing every layer of clustering process, the logical AND carried out between layers to all layers is calculated, as shown in Figure 4.?
To final grid, z cluster numbers are set, z cluster centre in grid merges periphery grid.
Preferably, this patent constructs data directory using R tree index establishing method.R tree mainly utilizes spatial entities
Boundary rectangle establishes spatial index.Fig. 5 is R tree data directory method for building up schematic diagram provided in an embodiment of the present invention.For
Grid after sub-clustering establishes label E (for matrix) for each unit, and the label of each unit is distributed in the unit
All objects, the data directory constructed.
Step 2, data owner encrypts initial data, encrypts to the data directory of generation, after encryption
Initial data and data directory be sent to Cloud Server；Decruption key is generated, decruption key is sent to authorized data
Searcher.
Data owner, which executes, generates security key SK={ S, M that key algorithm generates triple1,M2, wherein S is one
The binary system random division vector of a (n+u) dimension, wherein n is the data vector dimension generated, and u is to obscure dimension, M1And M2It serves as reasons
Random function generates the invertible matrix of two (n+u) × (n+u).
Then pass through split vector S for n dimension or n+u dimension data DCiCenter vector be divided into two subvector DCua
[i] and DCub[i], if Si=1, then DCua[i] and DCub[i] all be random value, and they the sum of be DCua[i]+DCub[i]=
DCi[i]；If Si=0, then DCua[i]=DCub[i]=DCi.Finally, passing through two matrix M1And M2Data directory I is added
Data directory that is close, being encrypted
Finally by the initial data of encryption and data directory, it is uploaded to Cloud Server.And the decruption key of generation is sent out
Data retrieval person is given, for being decoded to the target data result of encryption obtained from Cloud Server.Target data result
The information such as the electricity charge amount of money including but not limited to using electricity historical record, and monthly.
Step 3, data retrieval person inputs keyword and creates keyword search request in search；Generated encryption key
The cryptography key word indexing is sent to Cloud Server by word indexing.
Data retrieval person inputs keyword when retrieving, and whether occurs setting Q in the database according to the keyword of inputu:
If occurred in data vector, then the Q of corresponding positionu[i]=1；Otherwise Qu[i]=0.Then, by split vector S to Qu
It is split: Si=0, then Qua[i] and Qub[i] all be random value, and they the sum of be Qua[i]+Qub[i]=Qu[i]；If Si=
1, then Qua[i]=Qub[i]=Qu[i].Pass through invertible matrix M1M2To QuaAnd QubIt is encrypted, key word index are as follows:
Step 4, Cloud Server matches cryptography key word indexing and encrypted data directory according to matching algorithm,
The target data encrypted, returns again to data retrieval person.
Server provides a large amount of memory spaces and computing resource needed for searching ciphertext, and main task is processing search
The keyword search request of user, and query result is returned into data retrieval person.
Retrieving matching algorithm is executed by server, and the present invention obtains the searching algorithm of data using following formula:
Fi=Tw× I=Qu×DCu
Wherein, TwFor key word index, I is data directory, QuFor data retrieval person input keyword in data vector
Corresponding position, DCuFor data vector.It is available similar to the keyword of data retrieval person's input according to the matching of the algorithm
The highest Top-K data of property.In addition, the output result of the algorithm and we are with uniformity using the result obtained in plain text.
Step 5, data retrieval person is decrypted using target data of the decruption key to encryption, obtains target data.I.e.
Data retrieval person receives the search result of Cloud Server return, the decryption that data retrieval person is given with data owner to data into
Row decryption oprerations obtain target data, complete retrieving.
Fig. 6 is a kind of data retrieval system structure chart that can search for encryption provided in an embodiment of the present invention, the system packet
Include following part:
Data all devices, for generating data directory associated with initial data；Initial data is encrypted, it is right
The data directory of generation is encrypted, and encrypted initial data and data directory are sent to Cloud Server；It is close to generate decryption
Decruption key is sent to authorized data retrieval person by key；
Data retrieval device when for scanning for, inputting keyword and creating keyword search request；Encryption is generated to close
The cryptography key word indexing is sent to Cloud Server by key word indexing；It is solved using target data of the decruption key to encryption
It is close, obtain target data；
Cloud Server, for cryptography key word indexing and encrypted data directory to be matched according to matching algorithm,
The target data encrypted, returns again to data retrieval person.
Preferably, data all devices are specifically used for for generation data directory associated with initial data:
Step a, pre-processes data, forms multi-C vector；
Every dimension of hyperspace is divided into multiple sections by step b, constructs mesh space, and draw according to predefined conditions
Subnetting grid space；
Step c convert simultaneously sub-clustering to the mesh space after division；
Cluster in different mesh spaces is clustered, and distributes label by step d.
Preferably, predefined conditions described in step b include first condition and second condition, and the first condition is each dimension
Vector density is more than or equal to closeness threshold value on degree, and the second condition is that remaining mesh space and divided meshes are empty
Between distance be less than or equal to distance threshold, the distance threshold is variable, finishes until all spaces divide.
Preferably, step c convert simultaneously sub-clustering to the mesh space after division, comprising: to the mesh space after division
Carry out wavelet transformation；According to characteristic threshold value to mesh space assignment, the assignment includes to the space for being more than or equal to characteristic threshold value
1 is set, otherwise sets 0, then to adjacent 1 or 0 difference sub-clustering.
Preferably, step d distributes label using R tree method building index.
Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of above-described embodiment is can
It is completed with instructing relevant hardware by program, which can be stored in a computer readable storage medium, storage
Medium may include: ROM, RAM, disk or CD etc..
Embodiment provided above has carried out further detailed description, institute to the object, technical solutions and advantages of the present invention
It should be understood that embodiment provided above is only the preferred embodiment of the present invention, be not intended to limit the invention, it is all
Any modification, equivalent substitution, improvement and etc. made for the present invention, should be included in the present invention within the spirit and principles in the present invention
Protection scope within.
Claims (10)
1. a kind of data retrieval method that can search for encryption characterized by comprising
Step 1, data owner generates data directory associated with initial data；
Step 2, data owner encrypts initial data, encrypts to the data directory of generation, by encrypted original
Beginning data and data directory are sent to Cloud Server；Decruption key is generated, decruption key is sent to authorized data retrieval
Person；
Step 3, data retrieval person inputs keyword and creates keyword search request in search；Generated encryption key word rope
Draw, which is sent to Cloud Server；
Step 4, Cloud Server matches cryptography key word indexing and encrypted data directory according to matching algorithm, obtains
The target data of encryption, returns again to data retrieval person；
Step 5, data retrieval person is decrypted using target data of the decruption key to encryption, obtains target data.
2. the method according to claim 1, wherein data owner generates data associated with initial data
Index includes:
Step 101, data are pre-processed, forms multi-C vector；
Step 102, every dimension of hyperspace is divided into multiple sections, constructs mesh space, and divide according to predefined conditions
Mesh space；
Step 103, convert simultaneously sub-clustering to the mesh space after division；
Step 104, the cluster in different mesh spaces is clustered, and distributes label.
3. according to the method described in claim 2, it is characterized in that, predefined conditions described in step 102 include first condition and
Second condition, the first condition are that vector density is more than or equal to closeness threshold value in each dimension, and the second condition is remaining
Lower mesh space is less than or equal to distance threshold with divided meshes space length, wherein the distance threshold is variable, directly
It divides and finishes to all spaces.
4. according to the method described in claim 2, it is characterized in that, the mesh space after step 103 pair divides is converted simultaneously
Sub-clustering, comprising: wavelet transformation is carried out to the mesh space after division；According to characteristic threshold value to mesh space assignment, the assignment
Including setting 1 to the space for being more than or equal to characteristic threshold value, 0 is otherwise set, then to adjacent 1 or 0 difference sub-clustering.
5. the method according to claim 2, which is characterized in that step 104 distributes label using R tree method building index.
6. a kind of data retrieval system that can search for encryption characterized by comprising
Data all devices, for generating data directory associated with initial data；Initial data is encrypted, to generation
Data directory encrypted, encrypted initial data and data directory are sent to Cloud Server；Decruption key is generated, it will
Decruption key is sent to authorized data retrieval person；
Data retrieval device when for scanning for, inputting keyword and creating keyword search request；Generated encryption key word
Index, is sent to Cloud Server for the cryptography key word indexing；It is decrypted, is obtained using target data of the decruption key to encryption
To target data；
Cloud Server is obtained for matching cryptography key word indexing and encrypted data directory according to matching algorithm
The target data of encryption, returns again to data retrieval person.
7. system according to claim 6, which is characterized in that it is described to generate data directory associated with initial data,
Include:
Step a, pre-processes data, forms multi-C vector；
Every dimension of hyperspace is divided into multiple sections by step b, constructs mesh space, and divide net according to predefined conditions
Grid space；
Step c convert simultaneously sub-clustering to the mesh space after division；
Cluster in different mesh spaces is clustered, and distributes label by step d.
8. system according to claim 7, which is characterized in that predefined conditions described in step b include first condition and the
Two conditions, the first condition are that vector density is more than or equal to closeness threshold value in each dimension, and the second condition is remainder
Mesh space is less than or equal to distance threshold with divided meshes space length, wherein the distance threshold is variable, until
All spaces divide and finish.
9. system according to claim 7, which is characterized in that step c is converted and divided to the mesh space after division
Cluster, comprising: wavelet transformation is carried out to the mesh space after division；According to characteristic threshold value to mesh space assignment, the assignment packet
It includes and 1 is set to the space for being more than or equal to characteristic threshold value, otherwise set 0, then to adjacent 1 or 0 difference sub-clustering.
10. the system according to claim 7, which is characterized in that step d distributes label using R tree method building index.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110851481A (en) * | 2019-11-08 | 2020-02-28 | 青岛大学 | Searchable encryption method, device, equipment and readable storage medium |
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| CN111858826B (en) * | 2020-07-30 | 2024-08-16 | 深圳前海微众银行股份有限公司 | Space-time track retrieval method, system, terminal equipment and storage medium |
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| CN117910022A (en) * | 2024-03-19 | 2024-04-19 | 深圳高灯计算机科技有限公司 | Data searching method, device, computer equipment, storage medium and product |
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