CN104536984B - The verification method and system of a kind of space text Top k inquiries in Outsourced database - Google Patents
The verification method and system of a kind of space text Top k inquiries in Outsourced database Download PDFInfo
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Abstract
The present invention relates to a kind of verification method of the inquiries of the space text Top k in Outsourced database and system, including：IR trees are built, IR trees are combined structure MIR trees with Merkle Hash trees；MIR trees are separated into a MR tree and many keyword trees；Pair to input keyword it is related keyword tree progress beta pruning, generate beta pruning after keyword tree；By traveling through the keyword tree after MR trees and beta pruning, identifying object is generated；Recover the cryptographic Hash of MR trees and keyword root vertex by traveling through identifying object, be compared with the cryptographic Hash in raw data base, if identical, then it represents that Query Result is complete, and otherwise Query Result is imperfect；The scoring of each object in identifying object is calculated, is ranked up, the order of the k result with inquiring is compared, if identical, then it represents that Query Result is correct, otherwise Query Result mistake.The method of the present invention indexes the method with entry beta pruning by forest, reduces the redundancy in identifying object, reduces communication overhead and computing cost.
Description
Technical field
The present invention relates to the space text in database information query technical field, more particularly to a kind of Outsourced database
The verification method and system of Top-k inquiries.
Background technology
In the last few years, the equipment with positioning function is (such as：Smart mobile phone) a large amount of popularizations and mobile Internet
Flourish, location Based service (Location-based Services, LBS) is had become in our daily lifes
One important part.Positions of the LBS based on user is supplied to the inquiry experience that customer location is perceived.Space text Top-
K inquires about the extensive concern for receiving academia and industrial quarters.In a space text database, each object includes two
Individual attribute：Position attribution and text attribute.A space text database is given, text Top-k inquiries in space can look for position
Put relative query point closer to and the text k object more like with keyword that user inputs, for example, a user may wish
Look and inquire about one " dining room for having barbecue and beer ", and this dining room is also closer from the position of user.
The data owner of space text database needs to spend a large amount of expenses to safeguard substantial amounts of space text data
And efficient space text Top-k inquiries are provided, such as infrastructure is set up, the talent of specialty is employed and is advertised on the net
Deng.Therefore, in order to preferably utilize these space text datas, the data owner of space text database is by database outsourcing
It it is one fine to third party's location Based service provider (Location-based Services Provider, LBSP)
Selection.
However, such database outsourcing huge challenge, i.e. third party's location Based service provider can be brought to have can
It can return incorrect Query Result.The reason for causing this problem has following：Firstth, LBSP is possible to because personal
Interests go to distort the result of inquiry to meet the demand of some paying businessmans；Even if the second, LBSP is believable, but he
Server is it is possible to the attack of person under attack, and then attacker is possible to that the Query Result of mistake can be returned.
Therefore, above-mentioned the reason for, promotes us to be necessary to study the space text Top-k query method that user can verify that.
For specific, user needs to verify two aspects, and first is integrality, i.e., original space text data is not by third party
Location Based service provider distorted；Second is correctness, i.e., the order of preceding k result is correct and correct
As a result all it is not missed.
The present invention studies and realizes the verification method of the inquiries of the space text Top-k in Outsourced database.Existing phase
Or work is closed to verify for space querying, or for text query checking.But in the text database of space, it is each right
As there is two attributes in space and text, and text Top-k inquiries in space not only need to consider between query point and object
The spatially distance of distance, it is also contemplated that the text that object is included inputs the similarity degree of keyword with inquiry, therefore,
Some work can not solve the validation problem of space text Top-k inquiries.The intuitively idea for solving the problem is simply will
(one can for IR trees (up to the present solving the best index structure of space text Top-k inquiries problem) and Merkle Hash trees
Classical index structure for validation problem) it is combined to form MIR trees, pass through k before the algorithm queries of best first traversal
As a result, while the identifying object (Verification Object, VO) for checking in tree ergodic process also based on having access to
Node and produce.After user receives k result and identifying object, user can be by recovering the Kazakhstan of MIR root vertexes
The method of value is wished to verify the integrality of luv space text data, and correctness then can be by recalculating in identifying object
The scoring of each object, and then allow user relatively to complete checking after being reordered compared with the order of k result.But, should
It is used for the inverted file for indexing each object text message in the problem of method has very serious, MIR trees, is comprised in identifying object
In, and inverted file is huge, therefore identifying object is when third-party location Based service provider returns to user,
Substantial amounts of communication overhead can be caused.Meanwhile, user using identifying object when being verified, it is necessary to travel through each row's of falling text
Part is to calculate the scoring of each object in identifying object, and this also results in very big computing cost.Therefore, space text Top-k
The validation problem of inquiry is still a problem.
The content of the invention
The present invention provides the verification method and system of the space text Top-k inquiries in a kind of Outsourced database, by gloomy
Woods indexes the method with entry beta pruning, and the information of redundancy in identifying object is deleted, the size of identifying object is reduced, from
And reduce communication overhead and computing cost.
According to above-mentioned purpose, the invention provides a kind of authentication of the inquiries of the space text Top-k in Outsourced database
Method, methods described includes：
S1, the space text data in Outsourced database is built into IR trees, and by the IR trees and Merkle Hash trees
It is combined and is built into MIR trees；
S2, the MIR trees are separated into a MR tree and many keyword trees, every keyword tree corresponds to described
A keyword in the text data of space；
S3, a pair keyword tree related to the keyword inputted carry out beta pruning, the keyword tree after generation beta pruning；
S4, by traveling through the keyword tree after the MR trees and beta pruning, generate identifying object；
S5, by traveling through the cryptographic Hash that identifying object recovers the root node of MR trees and keyword tree, and in raw data base
Cryptographic Hash be compared, if identical, then it represents that Query Result is complete, and otherwise Query Result is imperfect；
S6, the scoring for calculating each object in the identifying object, are ranked up, then with k result inquiring
Order is compared, if identical, then it represents that Query Result is correct, otherwise inquiry error.
Wherein, the step S3 is specifically included：
S31, by it is described to input keyword it is related keyword tree progress structural modification；
S32, travel through amended keyword tree, k Query Result of acquisition, and form during traversal spanning tree；
S33, the scoring for recording k-th of result, then subtract each of the spanning tree by the scoring of k-th of result
Distance in node scores best scoring, using result as beta pruning scoring threshold value；
S34, beta pruning is carried out in spanning tree, will be removed less than the node of the scoring threshold value.
Wherein, the step S31 is specifically included：
S311, by the weights progress of the entry to the node of the related keyword tree of keyword that is inputting from greatly to
Small sequence；
S312, one will to be built in the node of related to the keyword of input keyword tree described in after sequence embedded
Merkle Hash trees.
Wherein, the embedded Merkle Hash trees are with the keyword related to the keyword of input after the sequence
All entries in the node of tree are the y-bend Merkle Hash tree that leaf node is built.
There is provided a kind of checking of the inquiries of the space text Top-k in Outsourced database according to another aspect of the present invention
System, the system includes：
MIR tree generation units, for the space text data in Outsourced database to be built into IR trees, and by the IR trees
It is combined to form MIR trees with Merkle Hash trees；
MIR tree separative elements, for the MIR trees to be separated into a MR tree and many keyword trees, every pass
The keyword that keyword tree corresponds in the space text data；
Beta pruning unit, beta pruning, the key after generation beta pruning are carried out for a pair keyword tree related to the keyword inputted
Word tree；
Traversal Unit, for by traveling through the keyword tree after the MR trees and beta pruning, generating identifying object；
Integrity verifying unit, for by traveling through the Hash that identifying object recovers the root node of MR trees and keyword tree
Value, is compared with the cryptographic Hash in raw data base, if identical, then it represents that Query Result is complete, and otherwise Query Result is endless
It is whole；
Verification of correctness unit, the scoring for calculating each object in the identifying object, is ranked up, then with looking into
The order for the k result ask out is compared, if identical, then it represents that Query Result is correct, otherwise inquiry error.
The verification method and system of space text Top-k inquiries in the Outsourced database of the present invention, are indexed by forest
With the method for entry beta pruning, the information of redundancy in identifying object is deleted, the size of identifying object is reduced, so as to reduce
Communication overhead and computing cost.
Brief description of the drawings
The features and advantages of the present invention can be more clearly understood from by reference to accompanying drawing, accompanying drawing is schematical without that should manage
Solve to carry out any limitation to the present invention, in the accompanying drawings：
Fig. 1 shows the flow chart of the verification method of the space text Top-k inquiries in the Outsourced database of the present invention.
Fig. 2 shows the structured flowchart of the checking system of the space text Top-k inquiries in the Outsourced database of the present invention.
Fig. 3 A show the method for embodiments of the invention and time and the number of objects of the structure index of existing method
Change comparative graph.
Fig. 3 B show the size and number of objects of the method for embodiments of the invention and the structure index of existing method
Change comparative graph.
Fig. 4 A show that the forest index of embodiments of the invention becomes with the query time of entry beta pruning with keyword quantity
The comparative graph of change.
Fig. 4 B show the forest index of embodiments of the invention and the query time of entry beta pruning and k size variation
Comparative graph.
Fig. 5 A show the forest index of embodiments of the invention and the identifying object size and keyword number of entry beta pruning
Measure the comparative graph of change.
Fig. 5 B show that the forest index of embodiments of the invention and the identifying object size of entry beta pruning and k size become
The comparative graph of change.
Fig. 6 A show proving time and the quantity of keyword of the forest index of embodiments of the invention with entry beta pruning
The curve map of change.
Fig. 6 B show the forest index of embodiments of the invention and the proving time of entry beta pruning and k size variation
Curve map.
Embodiment
Below in conjunction with accompanying drawing, embodiments of the present invention is described in detail.
Fig. 1 shows the flow chart of the verification method of the space text Top-k inquiries in the Outsourced database of the present invention.
Reference picture 1, the present invention is the verification method of the space text Top-k inquiries in Outsourced database, methods described bag
Include：
S1, the space text data in database is built into IR trees, and the IR trees and Merkle Hash trees are mutually tied
Conjunction is built into MIR trees；
S2, the MIR trees are separated into a MR tree and many keyword trees, every keyword tree corresponds to described
A keyword in the text data of space；
In verification process, due to containing the inverted file for index object text message in identifying object, and this
A little inverted files are huge, when identifying object returns to user, can cause substantial amounts of communication overhead, and when user is to looking into
When inquiry is verified, these inverted files can bring substantial amounts of computing cost again.Therefore, for MIR trees, it is proposed that one kind is based on
The scheme of forest index, i.e., be separated into a MR tree (being used for the index structure that space querying is verified) and many keys by MIR trees
Word tree (Merkle Term, MT), every MT tree corresponds to a keyword, and MR trees are applied not only to each object space attribute
Checking, is additionally operable to calculate query point to the space length between object.Similar, every MT tree is applied not only to each object text category
Property checking, be additionally operable to calculate user input similarity of the keyword relative to object text message.
By above-mentioned processing, a MR tree is pertained only in inquiry and several (on a small quantity) are related to family input keyword
MT trees, although inverted file is no longer included comprising multiple entries in identifying object, therefore the size of identifying object will be big
Width is reduced, and communication overhead and computing cost are also greatly reduced therewith.
S3, a pair keyword tree related to the keyword inputted carry out beta pruning, the keyword tree after generation beta pruning；
Substantial amounts of entry can be included in the node of every MT tree, and wherein only has sub-fraction to include result object.
In order to further reduce the size of identifying object, so that communication overhead is further reduced, to the entry of the redundancy in every MT tree
Information is deleted by the method for entry beta pruning.
Before beta pruning is carried out, the structure of pair MT tree related to the keyword inputted is modified.First, by every MT
The weights of entry in tree node are ranked up from big to small；Then structure one is Embedded in the node of every MT tree
Merkle Hash trees, i.e., build a y-bend Merkle Hash tree by leaf node of all entries in this node.This
This MT tree changed is referred to as MT* trees by place.
Then MT* trees are traveled through, the process record of traversal is got off, some spanning trees are formed, then, in these spanning trees
The entry of node carry out beta pruning, the main thought of its beta pruning is the scoring for recording k-th of result, is then subtracted in each node
Then that scoring that distance scores best, the threshold value scored as text carries out text beta pruning in spanning tree.
S4, by traveling through the keyword tree after the MR trees and beta pruning, generate identifying object；
S5, by traveling through the cryptographic Hash that identifying object recovers the root node of MR trees and keyword tree, and in raw data base
Cryptographic Hash be compared, if identical, then it represents that Query Result is complete, if that is, in third party's location Based service provider
Database in, then it represents that third party database does not distort luv space text data, that is, demonstrates integrality.Otherwise inquire about
As a result it is imperfect；
S6, the scoring for calculating each object in the identifying object, are ranked up, then suitable with k result of inquiry
Sequence is compared, if identical, then it represents that Query Result is correct, otherwise inquiry error.
Fig. 2 shows the structured flowchart of the checking system of the space text Top-k inquiries in the Outsourced database of the present invention.
The checking system of space text Top-k inquiries in reference picture 2, Outsourced database is specifically included：
MIR trees generation unit 10, for the space text data in Outsourced database to be built into IR trees, and by the IR
Tree is combined with Merkle Hash trees is built into MIR trees；
MIR trees separative element 20, for the MIR trees to be separated into a MR tree and many keyword trees, described in every
The keyword that keyword tree corresponds in the space text data；
Beta pruning unit 30, beta pruning, the pass after generation beta pruning are carried out for a pair keyword tree related to the keyword inputted
Keyword tree；
Traversal Unit 40, for by traveling through the keyword tree after the MR trees and beta pruning, generating identifying object；
Integrity verifying unit 50, for by traveling through the Hash that identifying object recovers the root node of MR trees and keyword tree
Value, is compared with the cryptographic Hash in raw data base, if identical, then it represents that Query Result is complete, and otherwise Query Result is endless
It is whole；
Verification of correctness unit 60, the scoring for calculating each object in the identifying object, is ranked up, Ran Houyu
The order of k result of inquiry is compared, if identical, then it represents that Query Result is correct, otherwise inquiry error.
The verification method and system of space text Top-k inquiries in the Outsourced database of the present invention can apply to various
The space text authentication of database, in order to describe simplicity, following examples are mainly for the space text of Outsourced database
The correctness and integrality of Top-k inquiries is relatively analyzing the present invention using method of the present invention and existing method
Method superiority.
Following is to be based on position from the expense according to owner, the expense of third party's location Based service provider, third party
The superiority-inferiority of communication overhead between the service provider and user that put and method of the invention on the computing cost of user.
Experimental design is as follows, and the present embodiment uses the point in the street of California, USA Los Angeles as the collection of object
Close, have 131461 objects, document is then randomly selected from 20Newsgroups document as the document of each object.
1. the expense of data owner
Fig. 3 A show the method for embodiments of the invention and time and the number of objects of the structure index of existing method
Change comparative graph.
Fig. 3 B show the size and number of objects of the method for embodiments of the invention and the structure index of existing method
Change comparative graph.
As shown in figs.3 a and 3b, rope of the index size than currently existing scheme such as IR and MIR of the scheme indexed based on forest
Draw size big, the time of structure will also be grown.Because it will calculate more cryptographic Hash, and based on the scheme of entry beta pruning
Index size it is bigger than the index size that forest indexes scheme, the structure time will also be grown because it be related to it is embedded
Merkle Hash trees calculating.However, the expense that the scheme based on forest index and the scheme based on entry beta pruning are brought is still
So in the acceptable scope of data owner.
2. the expense of third party's location Based service provider
Fig. 4 A show that the forest index of embodiments of the invention becomes with the query time of entry beta pruning with keyword quantity
The comparative graph of change.
Fig. 4 B show the forest index of embodiments of the invention and the query time of entry beta pruning and k size variation
Comparative graph.
As illustrated in figures 4 a and 4b, query time the looking into than the scheme based on entry beta pruning of the scheme indexed based on forest
The inquiry time is short, because the latter relate to inquiry and two steps of beta pruning.Nevertheless, the query time of the two is all at 1 second
Within, this also illustrates the validity of our methods.Simple scheme such as MIR query time curve is not drawn herein, because
The query time of simple scheme is far longer than the query time of the scheme based on forest index and the scheme based on entry beta pruning.
3. the communication overhead between third party's location Based service provider and user
Fig. 5 A show the forest index of embodiments of the invention and the identifying object size and keyword number of entry beta pruning
Measure the comparative graph of change.
Fig. 5 B show that the forest index of embodiments of the invention and the identifying object size of entry beta pruning and k size become
The comparative graph of change.
As shown in Figure 5 A and 5B, the size of the identifying object of the scheme indexed based on forest is than the side based on entry beta pruning
The identifying object of case is big.Because the further beta pruning of the latter reduces the size of identifying object.The identifying object of the two
Size is all within 50KB, to be our acceptable scopes, also illustrate that the validity of our methods.We do not have herein
There is a curve for the identifying object size for drawing simple scheme such as MIR, because the identifying object of simple scheme is sized substantially larger than
The size of the identifying object of the scheme indexed based on forest and the scheme based on entry beta pruning.
4. the computing cost of user
Fig. 6 A show proving time and the quantity of keyword of the forest index of embodiments of the invention with entry beta pruning
The curve map of change.
Fig. 6 B show the forest index of embodiments of the invention and the proving time of entry beta pruning and k size variation
Curve map.
As shown in Figure 6 A and 6B, proving time the testing than the scheme based on entry beta pruning of the scheme indexed based on forest
The card time is small, because the latter first has to recover the cryptographic Hash of embedded Merkle Hash root vertex, then recovers whole again
The cryptographic Hash of root vertex.The proving time of the two, this illustrated the validity of our methods all in 1 second.We are herein
The curve of simple scheme such as MIR proving time is not drawn, because the proving time of simple scheme is far longer than based on forest
The proving time of the scheme of index and scheme based on entry beta pruning.
For example, user input keyword be " dining room for having barbecue and beer ", k size is 1, and user position
Coordinate.Service provider searches for from customer location relative close in whole map and more meets pair that user inputs keyword
As.After the completion of search, result and identifying object are returned into user together, user can be with the complete of the result by identifying object
Whole property and correctness.Verification method can be in the form of browser plug-in in user terminal operation, if checking is correct, browser is carried
Show correct, if authentication failed, browser prompts checking not over.
The verification method and system of space text Top-k inquiries in the Outsourced database of the present invention, are indexed by forest
With the method for entry beta pruning, the information of redundancy in identifying object is deleted, the size of identifying object is reduced, so as to reduce
Communication overhead and computing cost.
Although being described in conjunction with the accompanying embodiments of the present invention, those skilled in the art can not depart from this hair
Various modifications and variations are made in the case of bright spirit and scope, such modifications and variations are each fallen within by appended claims
Within limited range.
Claims (5)
1. the verification method of the space text Top-k inquiries in a kind of Outsourced database, it is characterised in that methods described includes：
S1, the space text data in Outsourced database is built into IR trees, and the IR trees and Merkle Hash trees are mutually tied
Conjunction is built into MIR trees；
S2, the MIR trees are separated into a MR tree and many keyword trees, every keyword tree corresponds to the space
A keyword in text data；
S3, a pair keyword tree related to the keyword inputted carry out beta pruning, the keyword tree after generation beta pruning；
S4, by traveling through the keyword tree after the MR trees and beta pruning, generate identifying object；
S5, by traveling through the cryptographic Hash that identifying object recovers the root node of the keyword tree after MR trees and the beta pruning, it is and original
Cryptographic Hash in database is compared, if identical, then it represents that Query Result is complete, and otherwise Query Result is imperfect；
S6, the scoring for calculating each object in the identifying object, are ranked up, then with the order of the k result inquired
It is compared, if identical, then it represents that Query Result is correct, otherwise inquiry error.
2. the verification method of the space text Top-k inquiries in Outsourced database according to claim 1, its feature exists
In the step S3 is specifically included：
S31, by it is described to input keyword it is related keyword tree progress structural modification；
S32, travel through amended keyword tree, k Query Result of acquisition, and form during traversal spanning tree；
S33, the scoring for recording k-th of result, then subtract each node of the spanning tree by the scoring of k-th of result
In distance score best scoring, using result as beta pruning scoring threshold value；
S34, beta pruning is carried out in spanning tree, will be removed less than the node of the scoring threshold value.
3. the verification method of the space text Top-k inquiries in Outsourced database according to claim 2, its feature exists
In the step S31 is specifically included：
S311, by the weights progress of the entry to the node of the related keyword tree of keyword that is inputting from big to small
Sequence；
Structure one is embedded in S312, each node of keyword tree related to the keyword of input described in after sequence
Merkle Hash trees.
4. the verification method of the space text Top-k inquiries in Outsourced database according to claim 3, its feature exists
In the embedded Merkle Hash trees are with the node of the keyword tree related to the keyword of input after the sequence
All entries be leaf node build a y-bend Merkle Hash tree.
5. a kind of checking system of the space text Top-k inquiries in Outsourced database, the system includes：
MIR tree generation units, for the space text data in Outsourced database to be built into IR trees, and by the IR trees with
Merkle Hash trees are combined to form MIR trees；
MIR tree separative elements, for the MIR trees to be separated into a MR tree and many keyword trees, every keyword
The keyword that tree corresponds in the space text data；
Beta pruning unit, beta pruning, the keyword tree after generation beta pruning are carried out for a pair keyword tree related to the keyword inputted；
Traversal Unit, for by traveling through the keyword tree after the MR trees and beta pruning, generating identifying object；
Integrity verifying unit, the root node for recovering the keyword tree after MR trees and the beta pruning by traveling through identifying object
Cryptographic Hash, be compared with the cryptographic Hash in raw data base, if identical, then it represents that Query Result is complete, otherwise inquiry knot
It is really imperfect；
Verification of correctness unit, the scoring for calculating each object in the identifying object, is ranked up, then with inquiring
The order of k result be compared, if identical, then it represents that Query Result is correct, otherwise inquiry error.
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| CN106776791B (en) * | 2016-11-23 | 2019-12-10 | 深圳大学 | Pattern string matching verification method and device based on cloud service |
| CN109614766B (en) * | 2018-10-31 | 2021-01-22 | 创新先进技术有限公司 | Method and device for carrying out block chaining and evidence saving on webpage through file acquisition |
| CN110069592A (en) * | 2019-04-24 | 2019-07-30 | 上海交通大学 | The searching method that spatial key applied to electronic map is inquired |
| CN112966310B (en) * | 2021-03-23 | 2023-01-10 | 西安电子科技大学 | SQLite-based fine-grained data integrity verification method and device |
| CN114756603B (en) * | 2022-05-23 | 2023-04-07 | 天津大学 | High-efficiency verifiable query method for lightweight block chain |
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