CN107169372A - Privacy protection enquiring method based on Voronoi polygons Yu Hilbert curve encodings - Google Patents
Privacy protection enquiring method based on Voronoi polygons Yu Hilbert curve encodings Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—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
- G06F21/6245—Protecting personal data, e.g. for financial or medical purposes
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- G06—COMPUTING; CALCULATING OR COUNTING
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- 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
- G06F16/22—Indexing; Data structures therefor; Storage structures
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
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- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
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Abstract
The present invention relates to a kind of privacy protection enquiring method based on Voronoi polygons Yu Hilbert curve encodings, comprise the following steps：Server end carries out Voronoi polygon divisions to plane where destination object, and carries out division coding using Hilbert curves, builds mapping Hilbert units trellis coding and the polygonal B+ trees indexes of Voronoi；User submits self-position p Hilbert curve encoding value H (p) to carry out k NN Queries by user end to server；Server end searches the corresponding Voronoi polygons C of H (p) on index tree, generates C minimum enclosed rectangle R；Server end searches the R neighbour's Voronoi polygons of k 1, and the corresponding Hilbert curve encodings value of these Voronoi polygons is constituted into candidate query results set CaS, returns to client；User decodes to the Hilbert curve encoding values in CaS, filters out arest neighbors destination object.Realize the k NN Queries of protective position privacy.
Description
Technical field
The present invention relates to a kind of querying method, and in particular to one kind is based on Voronoi polygons and Hilbert curve encodings
Privacy protection enquiring method, belong to data query technique field.
Background technology
In recent years, the service based on positional information is widely used in computer application field, and for example GPS is led
Boat, spatial-attribute mutual query etc..This kind of application is most to be based on k NN Queries, and its pattern is that service provider is stored on the server
The information (including positional information) of all user's object of interest, user configuring can obtain user site positional information (2
Tie up latitude and longitude coordinates) equipment (such as GPS terminal), by sending user current location to service provider, server side returns
Return the relevant information away from k nearest object of user current location.This pattern requires that user must send to service provider
Its actual position information, with growing interest of the user to individual private data, occurs in that user is not leaking self-position letter
The application demand of k NN Queries service is obtained in the case of breath, the k NN Queries of protective position privacy turn into database and pacified with information
The focus studied entirely.User does not leak own location information and is mainly manifested in two aspects：(1) user to service without providing
Side sends its actual position information；(2) the possibility sexual satisfaction for user's possible position that attacker (insincere side) can deduce
Requirement of the user on protecting oneself location privacy.
Existing some technologies and method majority is realized using dependence trusted third party (online or off-line mode) auxiliary and protected
The k NN Queries of location privacy are protected, for example：Cloaking technologies：User is by actual position and to minimum backstepping region area
It is required that being sent to trusted third party, trusted third party is that each user generates the rectangular area for including its position, and rectangle is sent out
It is sent to server side, what server side returned to point in rectangle is possible to k neighbours, and trusted third party filters out legitimate reading,
Return to client；Based on data converter technique tend to rely on offline third party by data and inquiry be converted to a new data
Space is to protect the location privacy information of user.The method blocked based on position, is participated in without trusted third party, using client
False position is sent, the Neighbor Points that server is continuously transmitted to false position are detected for client, and user is met until client is found
It is required that k neighbour's object untill, but this method exist inquiry the uncontrollable problem of iterative process, with most secret protection issuers
Method is the same, there is the problem of query processing efficiency is low.
In protection customer location privacy inquiry research field, in order to protect inquiry's location privacy not reveal, it is necessary to looking into
The accurate location of inquiry person carries out Fuzzy processing, and specially treated and interaction mechanism by server and inquiring client terminal are realized
Taking into account for location privacy and Query Result accuracy, it is this to take into account often using the loss of query performance as cost.Therefore, lifting is looked into
It is the major issue that the inquiry of protective position privacy needs to solve to ask performance.
Voronoi diagram is provided in a kind of quick mode, each Voronoi polygonal regions for inquiry k neighbours to be owned
The arest neighbors POI of point is exactly the polygonal central points of its Voronoi, can be very according to the polygonal adjacent polygons of Voronoi
Some POI k nearest neighbor Voronoi polygons are found soon, and building the polygonal indexes of Voronoi in server end can have
Effect improves the efficiency that server end inquires about destination object k nearest neighbor.
The content of the invention
The present invention exactly for technical problem present in prior art there is provided one kind based on Voronoi polygons with
The privacy protection enquiring method of Hilbert curve encodings,
To achieve these goals, technical scheme is as follows, and one kind is based on Voronoi polygons and Hilbert
The privacy protection enquiring method of curve encoding, it is characterised in that the querying method is as follows,
(1) 2- that is stored with dimension position coordinates collection T server carries out voronoi polygon divisions to plane S where T, with T
All POI points are the Voronoi diagram that object builds S in set；Hilbert parameters of curve N (Hilbert curves exponent number) is set again
With fitting a curve starting point Q (x0,y0), coding is filled to S using Hilbert curves, the Hilbert of coordinate points is compiled in T set
The Hilbert values of the interval central point of Hilbert unit of the code value where it；Build on Voronoi polygons with
The index tree HilVOR (T) of Hilbert curve encodings (exponent number is N) mapping relations；
(2) user inputs its current true location coordinate p in client；Client is utilized and server end identical
Hilbert curvilinear functions are encoded to p, using with server end identical Hilbert fitting a curve starting point Q (x0,y0) and compile
Code exponent number N, calculating obtains encoded radio H (p), and is initiated to server on H (p) k NN Queries are asked；
(3) server end searches what is stored in the leaf node leaf, leaf where H (p) on index tree HilVOR (T)
Thiessen polygon is the main voronoi polygons C where H (p), calculates C minimum enclosed rectangle R；
(4) beta pruning distance threshold d is set, and initial value is 0；Rectangle R all k-1 neighbours Voronoi polygons are searched, i.e.,
The Voronoi polygons that k-1 is no more than apart from hop count of those central points and C central point, it is polygon to k-1 neighbours Voronoi
Shape C ', calculates the maximum distance dmax (C, C ') on C ' centre distance rectangle R summits, if dmax (C, C ')>D, then replace beta pruning
Apart from currency, i.e. d=dmax (C, C ')；Otherwise, C ' correspondences POI Hilbert curve encodings add Candidate Set CaS；
(5) Query Result CaS is returned to inquiring client terminal by server end；
(6) client to the Hilbert curve encodings value in the set CaS of return according to parameter Q (x0,y0) and N solved
Code, obtains the coordinate P (x, y) of two-dimensional points corresponding to Hilbert encoded radios, and according to self-position p, the Europe with p is searched from CaS
K minimum neighbor positions coordinate of family name's distance, i.e. target query result.
As a modification of the present invention, built in the step (1) on Voronoi polygons and Hilbert curves
The index tree HilVOR (T) of (exponent number is N) mapping relations is encoded, it is specific as follows,
Index tree generation method is as follows：
1. sky B+ trees bptree is generated；
2. by the incremental order of Hilbert curve encoding values, to each encoded radio hi3. and 4. step is performed；
3. Hilbert curve encoding values h is inquired aboutiThe polygonal central point q of arest neighbors Voronoi of correspondence position；
If 4. hiCorresponding Voronoi polygons and upper Hilbert curve encoding values hi-1Arest neighbors
Voronoi polygons are different, then by (hi,q)(hiFor key, q is value) it is inserted into as leaf node in bptree.
As a modification of the present invention, all data pair during the 2- dimensions position coordinates integrates T as server data space S
The set of elephant；Actual position p is located in data space S；It is mono- that H (p) main Voronoi polygons C refers to the corresponding Hilbert of H (p)
First case is in C；Polygonal of the Voronoi that the straightway for referring to 2 points of connection apart from hop count between two location points is crossed over
Number.
Relative to prior art, the invention has the advantages that, (1) technical scheme provides one kind and is efficiently based on
The privacy protection enquiring method of Voronoi polygons and Hilbert curve encodings, this method is not only protected efficiently but also well
Privacy information, improves operating efficiency, because HILVOR (T) index tree can be built offline, its build time can be disregarded
Enter the overall time loss of query process, the time loss of server end inquiry k neighbours is mainly split in HilVOR (T) index tree
Search and k neighbour's Candidate Sets lookup, verify that this method inquiry velocity is relatively fast through analysis, and due to customer side and
The content of server end transmission is Hilbert encoded radios, and the traffic is reduced for other method.
Brief description of the drawings
Fig. 1 is present system frame diagram；
Fig. 2 is present system process chart；
Fig. 3 is client process flow figure of the present invention；
Fig. 4 is server-side processes flow chart of the present invention；
Fig. 5 is plane domain Voronoi polygons division where server end 2- dimension datas collection in embodiment of the present invention,
And its Hilbert curve encoding schematic diagrames；
Fig. 6 is the HilVOR index tree schematic diagrames of embodiment of the present invention.
Embodiment：
In order to deepen the understanding of the present invention, the present embodiment is described in detail below in conjunction with the accompanying drawings.
Embodiment 1：The location-based information services of Fig. 1 depend on actual position p and the position of query object of user
Put, the longitude and latitude of some position can generally be represented using 2 dimension coordinates.
The server storage positional information of W data object of the present invention, this W data object composition 2- dimension data collection
T；S is 2 tieing up during region, i.e. user's actual position p are data space S comprising this W data object and user's actual position p
Data point, d represents the Euclidean distance of point-to-point transmission in 2- dimension data space Ss.
System framework is referring to Fig. 1, the wherein embedded equipment that radio communication is carried out with server of client (user uses), clothes
Business device stores all data object informations, it is possible to carry out query and search to data object, and client can also be to data
Object carries out query and search.
Referring to Fig. 2~Fig. 6, in the present embodiment, server end POI numbers be 16 (the transverse and longitudinal coordinate codomain of position for [0,
1]), Hilbert curves exponent number is set to 2, and 16 encoded radios are produced by Hilbert curves c.The Voronoi of server end construction
Polygon is divided and Hilbert curves are as shown in Figure 5.
(1) the HilVOR indexes B set up according to the mapping relations between Hilbert values and Voronoi polygons+Set (B+
Order is set for 3) as shown in fig. 6, the Voronoi polygons where two-dimensional coordinate point corresponding to Hilbert values are Hilbert values institute
The Voronoi polygons of mapping, the intermediate node of index tree stores the boundary value of subtree Hilbert values, leaf node storage
Hilbert values and its Voronoi polygons of mapping.
(2) client initiates inquiry request, and the position coordinates of user is p (0.2,0.6), and it is with (0.125,0.125)
The 2 rank Hilbert coding H (p)=4 of fitting a curve starting point, the NN Query for initiating H (p)=4 and k=3 to server end please
Ask；
(3) the Hilbert values 4 that server end is sent according to user, where searching it in HilVOR index trees
Voronoi polygons (object P5The Voronoi polygons at place), by P5Hilbert encoded radio H (P5) it is added to candidate's knot
In fruit collection S；
(4) server end continues to search for P5Place Voronoi polygonal 2 (i.e. k-1) neighbour's Voronoi polygons, and
The Hilbert encoded radios of central point are added to candidate result collection S, with P5Voronoi polygon of the distance within double bounce
Central point be { P1,P2,P3,P4,P6,P7,P8,P9,P10,P15, end product S={ H (P5),H(P1),H(P2),H(P3),H
(P4),H(P6),H(P7),H(P8),H(P9),H(P10),H(P15)}；
(5) S is returned to client by server end；
(6) client carries out Hilbert decoding (parameters to candidate result collection S：Fitting a curve starting point (0.125,0.125),
Exponent number N=2), the corresponding two-dimensional coordinate of Hilbert values in S is obtained, calculates and searches 3 position coordinateses nearest apart from P, determine P
3- neighbours be { P3,P5,P6}
The k NN Query efficiency to server end of the present invention is analyzed below.
Just like drawing a conclusion：
1. the corresponding main Voronoi polygons of arbitrary plane position coordinates p Hilbert encoded radios have and only one of which
(not considering borderline coordinate points).
Prove：Hilbert orders of a curve are N, and the space dimensionality after changing is 2, then H (p) ∈ [0,22N-1].Due to
Hilbert curve encodings are [0,22N- 1] it is continuous and dull on, so H (p) is unique.Again because Voronoi is polygon
Shape divides and covers whole plane domain S, then there will necessarily be some Voronoi and block the coding interval that Hilbert curves are obtained
Include point p coding.Therefore point p main Voronoi polygons are certainly existed.Dividing property by Voronoi polygons can
Know, given plane is carried out after Voronoi polygon divisions, each Voronoi polygon of generation mutually disjoints, and can release
P main Voronoi polygons have and only one of which.
2. the time complexity of server end generation inquiry user's k neighbour's candidate result collection is
Prove：Server end is only needed to HilVOR indexed search once during arest neighbors is inquired about, it is determined that main
Voronoi polygons and its minimum enclosed rectangle R, inquire about B+The time complexity of tree index is O (N), and N is Hilbert curves
The exponent number of function；Then, R k-1 neighbour's Voronoi polygon processes are searched, using binary chop recursive search, generation time
Valency isWherein M is the polygon figurate numbers of candidate Voronoi, and W is the sum of server end query object；In R each side
It is 8M to time loss of the Voronoi polygons center with R summits, back gauge is calculated.Therefore, total time complexity isConsider M withBelong to the same order of magnitude, therefore server end generation inquiry user k neighbours candidate ties
Really the time complexity of collection is
It should be noted that above-described embodiment, not for limiting protection scope of the present invention, in above-mentioned technical proposal
On the basis of made equivalents or replacement each fall within the scope that the claims in the present invention are protected.
Claims (3)
1. a kind of privacy protection enquiring method based on Voronoi polygons Yu Hilbert curve encodings, it is characterised in that institute
State querying method as follows,
(1) 2- that is stored with dimension position coordinates collection T server carries out voronoi polygon divisions to plane S where T, with set T
In all POI points be object build S Voronoi diagram；Hilbert parameters of curve N (Hilbert curves exponent number) and song are set again
Line filling starting point Q (x0,y0), coding is filled to S using Hilbert curves, the Hilbert encoded radios of coordinate points in T set
The Hilbert values of the central point in the Hilbert units interval where it；Build bent on Voronoi polygons and Hilbert
The index tree HilVOR (T) of line coding (exponent number is N) mapping relations；
(2) user inputs its current true location coordinate p in client；Client is utilized and server end identical Hilbert
Curvilinear function is encoded to p, using with server end identical Hilbert fitting a curve starting point Q (x0,y0) and curve exponent number
N, calculating obtains encoded radio H (p), and is initiated to server on H (p) k NN Queries are asked；
(3) server end searches the Tyson stored in the leaf node leaf, leaf where H (p) on index tree HilVOR (T)
Polygon is the main voronoi polygons C where H (p), calculates C minimum enclosed rectangle R；
(4) beta pruning distance threshold d is set, and initial value is 0；Search rectangle R all k-1 neighbours Voronoi polygons, i.e., those
The Voronoi polygons that k-1 is no more than apart from hop count of central point and C central point, to k-1 neighbour Voronoi polygon C ',
The maximum distance dmax (C, C ') on C ' centre distance rectangle R summits is calculated, if dmax (C, C ')>D, then replace beta pruning distance and work as
Preceding value, i.e. d=dmax (C, C ')；Otherwise, C ' correspondences POI Hilbert curve encodings add Candidate Set CaS；
(5) Query Result CaS is returned to inquiring client terminal by server end；
(6) client to the Hilbert curve encodings value in the set CaS of return according to parameter Q (x0,y0) and N decoded,
Obtain Hilbert encoded radios corresponding to two-dimensional points coordinate P (x, y), according to self-position p, from CaS search with p Euclideans away from
From K minimum neighbor positions coordinate, i.e. target query result.
2. the privacy protection enquiring method of Voronoi polygons according to claim 1 and Hilbert curve encodings, its
It is characterised by, is built in the step (1) on Voronoi polygons and Hilbert curve encodings (exponent number is N) mapping relations
Index tree HilVOR (T), it is specific as follows,
Index tree generation method is as follows：
1. sky B+ trees bptree is generated；
2. by the incremental order of Hilbert curve encoding values, to each encoded radio hi3. and 4. step is performed；
3. Hilbert curve encoding values h is inquired aboutiThe polygonal central point q of arest neighbors Voronoi of correspondence position；
If 4. hiCorresponding Voronoi polygons and upper Hilbert curve encoding values hi-1Arest neighbors Voronoi it is polygon
Shape is different, then by (hi,q)(hiFor key, q is value) it is inserted into as leaf node in bptree.
3. the privacy protection enquiring method of Voronoi polygons according to claim 2 and Hilbert curve encodings, its
It is characterised by, the set of all data objects during the 2- dimensions position coordinates integrates T as server data space S；Actual position p
In data space S；H (p) main Voronoi polygons C refers to the corresponding Hilbert cells of H (p) in C；Two positions
The polygonal numbers of Voronoi that the straightway for referring to 2 points of connection apart from hop count between point is crossed over.
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| CN109446293A (en) * | 2018-11-13 | 2019-03-08 | 嘉兴学院 | A kind of parallel higher-dimension nearest Neighbor |
| CN109618338A (en) * | 2018-12-22 | 2019-04-12 | 山西财经大学 | A kind of sensor network routing method based on Hilbert space filling curve |
| CN109618338B (en) * | 2018-12-22 | 2021-08-17 | 山西墨丘利科技有限公司 | Sensor network routing method based on Hilbert filling curve |
| CN110287271A (en) * | 2019-06-14 | 2019-09-27 | 南京拾柴信息科技有限公司 | A kind of method for building up of wireless base station and domain type geography atural object incidence matrix |
| CN110489606A (en) * | 2019-07-31 | 2019-11-22 | 云南师范大学 | A kind of grouping Hilbert coding and decoding methods |
| CN110489606B (en) * | 2019-07-31 | 2023-06-06 | 云南师范大学 | Packet Hilbert coding and decoding method |
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| CN113255002A (en) * | 2021-06-09 | 2021-08-13 | 北京航空航天大学 | Federal k nearest neighbor query method for protecting multi-party privacy |
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