CN107682817B - Cross-road network position anonymizing method for maintaining constant statistical characteristics - Google Patents

Cross-road network position anonymizing method for maintaining constant statistical characteristics Download PDF

Info

Publication number
CN107682817B
CN107682817B CN201710797476.6A CN201710797476A CN107682817B CN 107682817 B CN107682817 B CN 107682817B CN 201710797476 A CN201710797476 A CN 201710797476A CN 107682817 B CN107682817 B CN 107682817B
Authority
CN
China
Prior art keywords
road network
points
network region
color
pixel
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.)
Active
Application number
CN201710797476.6A
Other languages
Chinese (zh)
Other versions
CN107682817A (en
Inventor
桂小林
冀亚丽
代兆胜
郑怡清
方毓楚
廖东
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xian Jiaotong University
Original Assignee
Xian Jiaotong University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Xian Jiaotong University filed Critical Xian Jiaotong University
Priority to CN201710797476.6A priority Critical patent/CN107682817B/en
Publication of CN107682817A publication Critical patent/CN107682817A/en
Application granted granted Critical
Publication of CN107682817B publication Critical patent/CN107682817B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/02—Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/025—Services making use of location information using location based information parameters

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Computer Security & Cryptography (AREA)
  • Traffic Control Systems (AREA)
  • Facsimile Transmission Control (AREA)

Abstract

The invention discloses a cross-road network position anonymizing method for maintaining constant statistical characteristics, which comprises the steps of firstly, extracting a road network according to the road distribution condition in a target area and dividing the road network to obtain a road network area; then, according to the actually required protection range and the road network region centroid position, constructing a position anonymity candidate set meeting the protection requirement; and finally, determining the road network region with anonymous position according to the quantity difference of the position points between the road network region to be protected and the road network region in the candidate set, the center-of-mass distance and other parameters, and performing position anonymization across the road network in a position exchange mode. According to the method, the road network regions are divided by using the road network, and position anonymity is carried out among different road network regions, so that an attacker can be prevented from acquiring user privacy by using background knowledge of the relationship between the position and the location; the invention carries out position anonymity by a position exchange mode, can maintain the statistic characteristics of the whole data not to change, and does not influence the usability of the data after privacy protection.

Description

Cross-road network position anonymizing method for maintaining constant statistical characteristics
Technical Field
The invention relates to the field of information security, in particular to a privacy protection method for spatial position big data.
Background
With the development of technologies such as GPS and base station positioning, a mobile operator can acquire spatial location information of a large number of mobile users, and the location information can provide decision-making services for governments, enterprises and individuals through analysis and mining. However, the user is also at risk of leakage of personal sensitive information during the process of acquiring these services. Therefore, how to provide high-quality data analysis and decision service for users while protecting the privacy of the users is an important scientific problem which must be solved in the process of spatial data service.
Scholars at home and abroad have conducted many beneficial studies on location privacy protection, including: meyerowitz et al propose a CacheCloak system that can ensure the on-line anonymity of location data; zhang et al rasterize the location area into sensitive and non-sensitive areas, and when a mobile object enters the sensitive area, the location update is suppressed or postponed; the users in the anonymous group replace the real position with the density center of the group to realize the k-anonymous effect; shin et al, based on a polygon and clustering spatio-temporal mixed privacy protection mechanism, implement generalization of user locations, and assess the location anonymity level of users through probability k anonymity. However, the existing location privacy protection method cannot well meet the requirements of spatial data services, and particularly for large spatial location data, a new method needs to be provided to improve the anti-attack capability of privacy protection and the usability of data after privacy protection.
Disclosure of Invention
The present invention provides a cross-road network location anonymization method maintaining the statistical characteristics unchanged, so as to solve the above technical problems. Firstly, extracting a road network according to the road distribution condition in a target area and dividing the road network to obtain road network areas; then, according to the actually required protection range and the road network region centroid position, constructing a position anonymity candidate set meeting the protection requirement; and finally, determining the road network region with anonymous position according to the quantity difference of the position points between the road network region to be protected and the road network region in the candidate set, the center-of-mass distance and other parameters, and performing position anonymization across the road network in a position exchange mode.
In order to achieve the purpose, the invention adopts the following technical scheme:
a cross-road network position anonymization method for maintaining constant statistical characteristics comprises the following steps:
A. extracting a road network to divide a target area into road network areas: according to the road distribution condition in the target area, extracting a road network to realize the division of the target area, and acquiring the number of position points and the position characteristics of the mass center of the road network area;
B. constructing a position anonymity candidate set meeting protection requirements: according to the actually required protection area range and the road network area centroid position, the road network areas meeting the conditions form a candidate set of the road network areas to be protected, and the number difference of non-anonymous position points and the centroid point distance parameter between the road network areas in the candidate set and the road network areas to be protected are calculated;
C. maintaining the anonymity of the cross-road network position with constant statistical characteristics: and according to the quantity difference of the position points and the distance of the center of mass points, determining a road network region with anonymous position in the candidate set, and performing cross-road network anonymity on the position points in the road network region to be protected in a position exchange mode.
Further, the step a specifically includes the following steps:
let the coordinates of the lower left corner and the upper right corner of the target area G be (X)MIN，YMIN)、(XMAX，YMAX) Intercepting a map picture of the target area on the open API within the range, and obtaining a divided road network area R through color space transformation, corrosion, expansion, boundary extraction and inclusion relation judgment operations1、R2、…、Ri、…、RM；
Road network region RiContaining NiEach position point is respectively
Figure BDA0001400687280000022
Obtaining the road network region R through the following calculationiCenter of mass (X)i,Yi)：
Figure BDA0001400687280000021
Wherein i is 1,2, … …, M.
Further, the step a specifically includes the following steps:
let the coordinates of the lower left corner and the upper right corner of the target area G be (X)MIN，YMIN)、(XMAX，YMAX) Intercepting a target area map picture on the open API according to the range, and converting the target area map picture into an HSV color space; the Color value of the line representing the road is set as Color, and the upper bound of the Color range is set as Colormax＝{Hmax,Smax,VmaxAt the lower bound of Colormin＝{Hmin,Smin,Vmin}; setting HSV Color value of pixel point as ColorpixelH, s, v, if Colormin≤Colorpixel≤ColormaxThen colorpixel0, otherwise colorpixel1 is ═ 1; by color of all pixel pointspixelObtaining a binary image Figures；
Scanning binary image FiguresEach pixel of (1) is a 3 x 3 structural element
Figure BDA0001400687280000031
And its covered binary image FiguresIn (1)
Figure BDA0001400687280000032
Partially performing an AND operation, if the operation result is
Figure BDA0001400687280000033
If the pixel of the result image is 1, otherwise, the pixel of the result image is 0, and the operations are repeated twice; scanning binary image FiguresEach pixel of (1) is a 3 x 3 structural element
Figure BDA0001400687280000034
And its covered binary image FiguresIn (1)
Figure BDA0001400687280000035
Performing AND operation if the operation result is
Figure BDA0001400687280000036
Then the pixel of the resulting image is 0, otherwise it is 1; the result image after two corrosion operations and one expansion operation is the road network map FigureR；
Road network image FigureRObtaining divided road network region R after boundary extraction operation1、R2、…、Ri、…、RMUsing a set of points (x)i1,yi1),(xi2,yi2),(xi3,yi3),……(xie,yie) Indicates a road network region RiThe boundary of (2); for any location point (x, y), it and (x) are calculated separatelyi1,yi1),(xi2,yi2),(xi3,yi3),……(xie,yie) The space distance between them, and find the minimum space distance dminIf d isminPoint (x, y) belongs to road network region R when 0i(ii) a If d isminNot equal to 0, using (x, y) as the center of circle, dminCalculating (x, y) and (x) respectively for making Circle with radiusi1,yi1),(xi2,yi2),(xi3,yi3),……(xie,yie) The e intersections of the straight line and the Circle are set as o1、o2、o3……、oeBy the vector calculation formula ojoj+1＝oj+1-oj(j ═ 1,2, … … e) to obtain the vector sum
Figure BDA0001400687280000037
If the vector sum is 0, the point (x, y) belongs to the road network region Ri；
Road network region RiContaining NiEach position point is respectively
Figure BDA0001400687280000039
Obtaining the road network region R through the following calculationiCenter of mass (X)i,Yi)：
Figure BDA0001400687280000038
Wherein i is 1,2, … …, M.
Further, the step B specifically includes the following steps:
setting the actually needed side length of the protection area as d, and aiming at the road network area R to be protectediIf the road network region R ispCentroid of p ≠ i (X)p,Yp) Satisfies the conditions
Figure BDA0001400687280000041
And is
Figure BDA0001400687280000042
Then R ispAdding RiCandidate switching set S (R) ofi) Namely:
Figure BDA0001400687280000043
and is
Figure BDA0001400687280000044
Set up and wait to protect road network region RiAnd a certain network region R in the candidate setpThe number of the position points which are not anonymous is ni、np，RiAnd RpThe number difference of the non-anonymous position points is delta n (R)i,Rp) Distance between centroid points is D (R)i,Rp) Then, there are:
Δn(Ri,Rp)＝|ni-np| (3)
Figure BDA0001400687280000045
further, the step C specifically includes the following steps:
all Δ n (R) are comparedi,Rp) Recording the road network region with the minimum difference of the number of the non-anonymous position points as RqIf the road network region with the minimum difference of the number of the non-anonymous position points is not unique, respectively calculating the centroid point and the R of the road network regioniSelecting the road network region with the minimum distance value as Rq；
If n isi≤nqThen from RqIn the random selection of niDot and RiAll of n iniThe points are interchanged and n isq＝Δn(Ri,Rp) (ii) a If n isi>nqThen R isqAll points in and RiIn a randomly selected niThe random points are interchanged, and n isi＝Δn(Ri,Rp)。
Further, repeating the step B and the step C until all the road network areas in the target area are processed.
Compared with the prior art, the invention has the following beneficial effects:
according to the method, the road network regions are divided by using the road network, and position anonymity is carried out among different road network regions, so that an attacker can be prevented from acquiring user privacy by using background knowledge of the relationship between the position and the location; the invention carries out position anonymity by a position exchange mode, can maintain the statistic characteristics of the whole data not to change, and does not influence the usability of the data after privacy protection.
Detailed Description
The following is a more detailed description of the practice of the invention.
The invention discloses a cross-road network position anonymizing method for maintaining constant statistical characteristics, which comprises the following steps:
step A, extracting a road network to divide a target area into road network areas: according to the road distribution condition in the target area, extracting the road network to realize the division of the target area, and acquiring the number of position points and the position characteristics of the centroid of the road network area.
Let the longitude and latitude of the lower left corner and the upper right corner of the target area respectively be (X)MIN，YMIN)、(XMAX，YMAX) According to the actual application requirements (for example: the scale bar is 1: 1000) and intercepting the target area map picture above the open API in the range, and converting the target area map picture into an HSV color space. The Color value of the line representing the road is set as Color, and the upper bound of the Color range is set as Colormax＝{Hmax,Smax,VmaxAt the lower bound of Colormin＝{Hmin,Smin,Vmin}; setting HSV Color value of pixel point as ColorpixelH, s, v, if Colormin≤Colorpixel≤ColormaxThen colorpixel0, otherwise colorpixel1 is ═ 1; by color of all pixel pointspixelObtaining a binary image Figures。
Scanning binary image FiguresEach pixel of (1) is a 3 x 3 structural element
Figure BDA0001400687280000051
And its covered binary image FiguresIn (1)
Figure BDA0001400687280000052
Partially performing an AND operation, if the operation result is
Figure BDA0001400687280000053
Then the pixel of the resulting image is 1 and otherwise 0, repeating the above operations twice. Scanning binary image FiguresEach pixel of (1) is a 3 x 3 structural element
Figure BDA0001400687280000054
And its covered binary image FiguresIn (1)
Figure BDA0001400687280000055
Performing AND operation if the operation result is
Figure BDA0001400687280000056
Then the pixel of the resulting image is 0 and otherwise is 1. The result image after two corrosion operations and one expansion operation is the road network map FigureR。
Road network image FigureRObtaining divided road network region R after boundary extraction operation1、R2、…、Ri、…、RMUsing a set of points (x)i1,yi1),(xi2,yi2),(xi3,yi3),……(xie,yie) Indicates a road network region RiThe boundary of (2). For any location point (x, y), it and (x) are calculated separatelyi1,yi1),(xi2,yi2),(xi3,yi3),……(xie,yie) The space distance between them, and find the minimum space distance dminIf d isminPoint (x) when equal to 0Y) belonging to the road network region Ri(ii) a If d isminNot equal to 0, using (x, y) as the center of circle, dminCalculating (x, y) and (x) respectively for making Circle with radiusi1,yi1),(xi2,yi2),(xi3,yi3),……(xie,yie) The e intersections of the straight line and the Circle are set as o1、o2、o3……、oeBy the vector calculation formula ojoj+1＝oj+1-oj(j ═ 1,2, … … e) to obtain the vector sum
Figure BDA0001400687280000061
If the vector sum is 0, the point (x, y) belongs to the road network region Ri。
Road network region Ri(i-1, 2, … …, M) contains NiEach position point is respectively
Figure BDA0001400687280000069
Figure BDA00014006872800000610
Obtaining the road network region R through the following calculationiCenter of mass (X)i,Yi)：
Figure BDA0001400687280000062
B, constructing a position anonymous candidate set meeting the protection requirement: according to the actually required protection area range and the road network area centroid position, the road network areas meeting the conditions form a candidate set of the road network areas to be protected, and the position point number difference and the centroid point distance parameter which are not anonymous between the road network areas in the candidate set and the road network areas to be protected are calculated.
For road network region R to be protectediThe corresponding protection range is set as follows
Figure BDA0001400687280000063
Figure BDA0001400687280000064
The protection area is a rectangular area formed by vertexes, wherein d is the side length of the protection area set according to actual needs. If the road network region Rp(p ≠ i) centroid (X)p,Yp) In the road network region RiWithin the corresponding protective range, i.e. (X)p,Yp) At the same time satisfy
Figure BDA0001400687280000065
And
Figure BDA0001400687280000066
then the road network region R is formedpAs RiAnd forming all the candidate exchange objects meeting the conditions into a road network region R to be protectediCandidate switching set S (R) ofi) Namely:
Figure BDA0001400687280000067
and is
Figure BDA0001400687280000068
Set up and wait to protect road network region RiThe number of the position points which are not anonymous is niA certain network region R in the candidate switching setpThe number of the position points which are not anonymous is npThen n isiIs Ni，npIs Np(ii) a Set up and wait to protect road network region RiThe number of non-anonymous position points and a certain routing area R in the candidate exchange setpThe number difference of the non-anonymous position points is delta n (R)i,Rp) Then, there are:
Δn(Ri,Rp)＝|ni-np| (3)
road network region R to be protectediPoint of mass center and a certain road network region R in the candidate exchange setpIs set as D (R)i,Rp) Then, there are:
Figure BDA0001400687280000071
step C, maintaining the anonymity of the cross-road network position with unchanged statistical characteristics: and according to the quantity difference of the position points and the distance of the center of mass points, determining a road network region with anonymous position in the candidate set, and performing cross-road network anonymity on the position in the road network region to be protected in a position exchange mode.
For candidate switching set S (R)i) All of R inpRespectively calculate them and the road network region R to be protectediΔ n (R) betweeni,Rp) And the number of non-anonymous position points is differenced by delta n (R)i,Rp) The area of the road network with the smallest value is marked as Rq(ii) a If the road network region with the minimum difference of the number of the non-anonymous position points is not unique, respectively calculating the centroid points and the R of the road network regionsiDistance D (R) between centroid points ofi,Rp) And will distance D (R)i,Rp) The area of the road network with the smallest value is marked as Rq(ii) a Road network region RqTo be protected with road network region RiAnd performing position exchange to finish anonymity.
For the road network region R to be protectediRoad network region RqIf n isi≤nqThen, the road network region R to be protected is describediCan be anonymized, i.e. from RqIn the random selection of niDot and RiAll of n iniThe points are exchanged in position, R after the exchangeiNumber n of non-anonymous location pointsi0, and road network region RqNumber n of non-anonymous location pointsq＝Δn(Ri,Rq) (ii) a If n isi>nqThen, the road network region R to be protected is describediIf all the position points in the road network region R cannot be anonymizediIn the random selection of niIndividual position point and road network region RqExchanging positions of all points in the network, and exchanging back network area RiNumber n of non-anonymous location pointsi＝Δn(Ri,Rp)。
And D, repeating the step B and the step C until all the road network areas in the target area are processed.

Claims (6)

1. A cross-road network position anonymizing method for maintaining constant statistical characteristics is characterized by comprising the following steps:
A. extracting a road network to divide a target area into road network areas: according to the road distribution condition in the target area, extracting a road network to realize the division of the target area, and acquiring the number of position points and the position characteristics of the mass center of the road network area;
B. constructing a position anonymity candidate set meeting protection requirements: according to the actually required protection area range and the road network area centroid position, the road network areas meeting the conditions form a candidate set of the road network areas to be protected, and the number difference of non-anonymous position points and the centroid point distance parameter between the road network areas in the candidate set and the road network areas to be protected are calculated;
C. maintaining the anonymity of the cross-road network position with constant statistical characteristics: and according to the quantity difference of the position points and the distance of the center of mass points, determining a road network region with anonymous position in the candidate set, and performing cross-road network anonymity on the position points in the road network region to be protected in a position exchange mode.
2. The method according to claim 1, wherein step a comprises the following steps:
let the coordinates of the lower left corner and the upper right corner of the target area G be (X)MIN，YMIN)、(XMAX，YMAX) Intercepting a map picture of the target area on the open API within the range, and obtaining a divided road network area R through color space transformation, corrosion, expansion, boundary extraction and inclusion relation judgment operations1、R2、...、Ri、...、RM；
Road network region RiContaining NiEach position point is respectively
Figure FDA0002254150670000011
Obtaining the road network region R through the following calculationiCenter of mass (X)i，Yi)：
Figure FDA0002254150670000012
Wherein, i is 1, 2.
3. The method according to claim 2, wherein step a comprises the following steps:
let the coordinates of the lower left corner and the upper right corner of the target area G be (X)MIN，YMIN)、(XMAX，YMAX) Intercepting a target area map picture on the open API according to the range, and converting the target area map picture into an HSV color space; the Color value of the line representing the road is set as Color, and the upper bound of the Color range is set as Colormax＝{Hmax，Smax，VmaxAt the lower bound of Colormin＝{Hmin，Smin，Vmin}; setting HSV Color value of pixel point as ColorpixelH, s, v, if Colormin≤Colorpixel≤ColormaxThen colorpixel0, otherwise colorpixel1 is ═ 1; by color of all pixel pointspixelObtaining a binary image Figures；
Scanning binary image FiguresEach pixel of (1) is a 3 x 3 structural element
Figure FDA0002254150670000021
And its covered binary image FiguresIn (1)
Figure FDA0002254150670000022
Partially performing an AND operation, if the operation result is
Figure FDA0002254150670000023
If the pixel of the result image is 1, otherwise, the pixel of the result image is 0, and the operations are repeated twice; scanning binary image FiguresEach of (1)A pixel, using a 3 × 3 structuring element
Figure FDA0002254150670000024
And its covered binary image FiguresIn (1)
Figure FDA0002254150670000025
Performing AND operation if the operation result is
Figure FDA0002254150670000026
Then the pixel of the resulting image is 0, otherwise it is 1; the result image after two corrosion operations and one expansion operation is the road network map FigureR；
Road network image FigureRObtaining divided road network region R after boundary extraction operation1、R2、...、Ri、...、RMUsing a set of points (x)i1，yi1)，(xi2，yi2)，(xi3，yi3)，......(xie，yie) Indicates a road network region RiThe boundary of (2); for any location point (x, y), it and (x) are calculated separatelyi1，yi1)，(xi2，yi2)，(xi3，yi3)，......(xie，yie) The space distance between them, and find the minimum space distance dminIf d isminPoint (x, y) belongs to road network region R when 0i(ii) a If d isminNot equal to 0, using (x, y) as the center of circle, dminCalculating (x, y) and (x) respectively for making Circle with radiusi1，yi1)，(xi2，yi2)，(xi3，yi3)，......(xie，yie) The e intersections of the straight line and the Circle are set as o1、o2、o3……、oeBy the vector calculation formula ojoj+1＝oj+1-oj(j ═ 1, 2.... e) to obtain the vector sum
Figure FDA0002254150670000027
If the vector sum is 0, the point (x, y) belongs to the road network region Ri；
Road network region RiContaining NiEach position point is respectively
Figure FDA0002254150670000028
Obtaining the road network region R through the following calculationiCenter of mass (X)i，ri)：
Figure FDA0002254150670000029
Wherein, i is 1, 2.
4. The method according to claim 2, wherein step B comprises the following steps:
setting the actually needed side length of the protection area as d, and aiming at the road network area R to be protectediIf the road network region R ispCentroid of p ≠ i (X)p，Yp) Satisfies the conditions
Figure FDA0002254150670000031
And is
Figure FDA0002254150670000032
Then R ispAdding RiCandidate switching set S (R) ofi) Namely:
Figure FDA0002254150670000033
set up and wait to protect road network region RiAnd a certain network region R in the candidate setpThe number of the position points which are not anonymous is ni、np，RiAnd RpThe number difference of the non-anonymous position points is delta n (R)i，Rp) Distance between centroid points is D (R)i，Rp) Then, there are:
Δn(Ri，Rp)＝|ni-np| (3)
Figure FDA0002254150670000034
。
5. the method according to claim 4, wherein the step C comprises the following steps:
all Δ n (R) are comparedi，Rp) Recording the road network region with the minimum difference of the number of the non-anonymous position points as RqIf the road network region with the minimum difference of the number of the non-anonymous position points is not unique, respectively calculating the centroid point and the R of the road network regioniSelecting the road network region with the minimum distance value as Rq；
If n isi≤nqThen from RqIn the random selection of niDot and RiAll of n iniThe points are interchanged and n isq＝Δn(Ri，Rq) (ii) a If n isi＞nqThen R isqAll points in and RiIn a randomly selected nqThe random points are interchanged, and n isi＝Δn(Ri，Rq)。
6. The method of claim 1, wherein steps B and C are repeated until all the areas of the network in the target area have been processed.
CN201710797476.6A 2017-09-06 2017-09-06 Cross-road network position anonymizing method for maintaining constant statistical characteristics Active CN107682817B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710797476.6A CN107682817B (en) 2017-09-06 2017-09-06 Cross-road network position anonymizing method for maintaining constant statistical characteristics

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710797476.6A CN107682817B (en) 2017-09-06 2017-09-06 Cross-road network position anonymizing method for maintaining constant statistical characteristics

Publications (2)

Publication Number Publication Date
CN107682817A CN107682817A (en) 2018-02-09
CN107682817B true CN107682817B (en) 2020-03-17

Family

ID=61134492

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710797476.6A Active CN107682817B (en) 2017-09-06 2017-09-06 Cross-road network position anonymizing method for maintaining constant statistical characteristics

Country Status (1)

Country Link
CN (1) CN107682817B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108848075B (en) * 2018-05-31 2020-10-09 东南大学 Road network environment position anonymizing method based on user distribution balance degree

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103249038A (en) * 2013-04-09 2013-08-14 哈尔滨工程大学 Privacy protection method based on location of moving object in road network space
CN104394509A (en) * 2014-11-21 2015-03-04 西安交通大学 High-efficiency difference disturbance location privacy protection system and method
CN104780173A (en) * 2015-04-16 2015-07-15 西安电子科技大学 System and method for selecting anonymous road section under location privacy protection in road network
CN105120431A (en) * 2015-07-24 2015-12-02 中山大学 Location privacy protection method based on location sensitivity and hot degree in road network
CN105516190A (en) * 2016-01-13 2016-04-20 福建师范大学 Position privacy protection method based on road network in continuous position service

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9154906B2 (en) * 2002-03-28 2015-10-06 Telecommunication Systems, Inc. Area watcher for wireless network
US20120239584A1 (en) * 2011-03-20 2012-09-20 Microsoft Corporation Navigation to dynamic endpoint

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103249038A (en) * 2013-04-09 2013-08-14 哈尔滨工程大学 Privacy protection method based on location of moving object in road network space
CN104394509A (en) * 2014-11-21 2015-03-04 西安交通大学 High-efficiency difference disturbance location privacy protection system and method
CN104780173A (en) * 2015-04-16 2015-07-15 西安电子科技大学 System and method for selecting anonymous road section under location privacy protection in road network
CN105120431A (en) * 2015-07-24 2015-12-02 中山大学 Location privacy protection method based on location sensitivity and hot degree in road network
CN105516190A (en) * 2016-01-13 2016-04-20 福建师范大学 Position privacy protection method based on road network in continuous position service

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
位置服务中的查询隐私度量框架研究;张学军，桂小林等;《西安交通大学学报》;20140310;全文 *

Also Published As

Publication number Publication date
CN107682817A (en) 2018-02-09

Similar Documents

Publication Publication Date Title
Yang et al. A blockchain-based location privacy-preserving crowdsensing system
CN105205408B (en) Track data method for secret protection based on spatial clustering and system
CN107967702A (en) The thermodynamic chart method for visualizing of reverse color applying drawing
CN110175547B (en) Crowd density early warning method for multi-domain information
CN107682817B (en) Cross-road network position anonymizing method for maintaining constant statistical characteristics
Lavigne et al. Predicting financial crime: Augmenting the predictive policing arsenal
Li et al. Extracting check dam areas from high‐resolution imagery based on the integration of object‐based image analysis and deep learning
CN106844688A (en) The plant habitat analyzed based on space overlapping and GAP protects red line demarcation method
Lopes et al. Assessment of urban cultural-heritage protection zones using a co-visibility-analysis tool
Wu et al. Landscape scaling of different land-use types, geomorphological styles, vegetation regionalizations, and geographical zonings differs spatial erosion patterns in a large-scale ecological restoration watershed
Li et al. Research on remote sensing dynamic monitoring of ecological resource environment based on GIS
CN105096240B (en) Image sensitive target hidden method based on textures synthesis
CN107220615B (en) Urban impervious surface information extraction method fusing interest point big data
CN111475592B (en) Method for making basin map facing vector graphic file
Huang et al. Reserve network planning for fishes in the middle and lower Yangtze River basin by systematic conservation approaches
Yu et al. A street view image privacy detection and protection method based on mask-RCNN
Alfred et al. Urban growth and landuse cover change in Nigeria using GIS and remote sensing applications. Case Study of Suleja LGA, Niger State
CN111309833B (en) Privacy protection method for large geographic space data release based on map adjustment
CN109947877B (en) Method and system for improving map positioning precision of GIS mobile terminal
CN106844852A (en) Ship distribution thermodynamic chart construction method based on VTS system
CN110969700A (en) 3D electronic sand table model generation method and device and electronic equipment
Armstrong et al. Map hacking: On the use of inverse address-matching to discover individual identities from point-mapped information sources
CN103150495B (en) Based on the digital finger-print detection and tracking method of Agent
Arianti et al. Analysis of Land Use Changes in Pontianak City from 2008 to 2021 Using GIS and Remote Sensing
CN111079668B (en) Method for rapidly dividing lanes where vehicles are located

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
GR01 Patent grant
GR01 Patent grant