CN107995205B - Self-adaptive k-anonymization rasterization method for personnel density guidance - Google Patents

Self-adaptive k-anonymization rasterization method for personnel density guidance Download PDF

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CN107995205B
CN107995205B CN201711321144.7A CN201711321144A CN107995205B CN 107995205 B CN107995205 B CN 107995205B CN 201711321144 A CN201711321144 A CN 201711321144A CN 107995205 B CN107995205 B CN 107995205B
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桂小林
郑怡清
戴慧珺
杨广知
冀亚丽
方毓楚
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Xian Jiaotong University
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Abstract

The invention discloses a self-adaptive K-anonymization rasterization method for personnel density guidance, which comprises the steps of firstly, carrying out unit division and conversion on a region to be rasterized according to the number of position data points of the region to be rasterized, the area of the region and an anonymity coefficient K; then, dividing the granularity level of the number of the position points of the divided unit area; and finally, performing self-adaptive k-anonymization rasterization according to the granularity level of the unit region where the position point of the user to be anonymized is located and the granularity level of the peripheral region. The population density characteristics of the region to be rasterized are pre-analyzed, the region is divided into a plurality of small units, anonymous regions are dynamically formed by combining and splitting the divided unit regions with different densities, and the self-adaptive k anonymous rasterization algorithm of personnel density guidance is realized to adapt to position privacy protection of scenes with different densities. The invention realizes the position anonymity by carrying out position replacement through the geographical midpoint, and can improve the anonymity service quality under the condition of unchanged position privacy degree.

Description

Self-adaptive k-anonymization rasterization method for personnel density guidance
Technical Field
The invention relates to the field of information security, in particular to a k-anonymization rasterization method supporting location privacy in location service.
Background
Location-based services have become one of the most popular mobile services, and when a mobile user uses an intelligent terminal to enjoy various location services, a large amount of user location information is left on a location server of a service provider, and context information attached to the location information can expose personal sensitive information of the user, such as living habits, hobbies, daily activities, social relationships, physical conditions, and the like. As this information grows and is revealed to untrusted third parties (such as service providers), it opens the door to misuse personal privacy data. Personal concerns about location privacy will inevitably hinder the healthy development of the mobile internet location services industry. Therefore, how to provide high-quality service for users while protecting the privacy of the users is a scientific problem that must be solved by the location privacy protection technology in the mobile internet environment.
Foreign and domestic scholars have conducted many beneficial studies on location privacy protection in location-based services, and most location privacy protection schemes are based on the K-anonymity model. The method comprises the following steps: bamba et al propose a privacyGrid method, which divides the space into grids and uses the grids for K anonymization, thereby improving the privacy protection degree of the user. Deutsch and the like only can defend attackers without anonymous policy background knowledge aiming at a part of K anonymous methods, and provide a policy-aware K anonymous method which can defend more malicious attackers who know anonymous box generation strategies in reality. Pan et al propose an incremental ICliqueCloak method for location correlation attacks in continuous queries. Wang et al propose the location-aware location privacy protection problem, and realize the privacy diversity and dynamic problem in continuous query. Ghini ta et al use the concept of k-anonymity to achieve privacy protection by replacing the user's precise location with the location of the spatially-anonymous area. However, the sensitivity of the k-anonymity privacy protection method in the conventional location data distribution is low, so that the k-anonymity rasterization processing cannot be performed according to the location data in different scenes, and a new method needs to be provided to ensure higher privacy degree and service quality and improve the adaptivity of the k-anonymity privacy protection method.
Disclosure of Invention
The invention aims to provide a self-adaptive k-anonymization rasterization method for personnel density guidance so as to solve the technical problem. Firstly, carrying out unit division and conversion on a region to be rasterized according to the number of position data points of the region to be rasterized, the area of the region and an anonymity coefficient K; then, dividing the granularity level of the number of the position points of the divided unit area; and finally, performing self-adaptive k-anonymization rasterization according to the granularity level of the unit region where the position point of the user to be anonymized is located and the granularity level of the peripheral region.
In order to achieve the purpose, the invention adopts the following technical scheme:
a rasterization k anonymization method for personnel density guidance comprises the following steps:
A. rasterization is performed based on personnel density: calculating the grid size of the target region according to the number of the position points in the target region, the area size of the target region and the size of the anonymity coefficient K, and rasterizing the target region according to the grid size;
B. calculating the personnel density distribution of the target area: calculating the granularity level of each grid according to the number of position points in the grid and the size of the anonymous coefficient K, and formalizing the granularity levels of all the grids in the target area into a granularity matrix;
C. k-anonymity for person density guidance: according to the granularity level of the grids, merging and splitting self-adaptive processing is carried out on each grid to form an anonymous area, and k anonymity is carried out on position points in the anonymous area.
Further, the step a specifically includes the following steps:
collecting a position data point N contained in the region to be rasterizedcountAnd the total area S of the regionareaFor the value of the input parameter K in the given K-anonymity model, the partition granularity rho of the region is calculated by the following formula:
Figure BDA0001504802990000021
dividing a region to be rasterized into a two-dimensional plane formed by small square units from left to right and from top to bottom; these small cells are divided cells, and the area S of the small cellsminIs the product of the partition granularity p and the value of K:
Smin＝K*ρ
mapping the position data points in the area to a two-dimensional plane, counting the number of the position data points in each divided unit, and representing the target area by using a unit matrix, wherein the unit matrix is as follows:
Figure BDA0001504802990000031
the relative position of the elements of the matrix in the matrix is the same as the relative position of the dividing unit in the area; each element mijRepresents the number of positions in each division unitThe number of data points.
Further, the step B specifically includes the following steps:
for any division unit, the number of data points contained in the division unit is set as ncountIts practical area sareaAnd dividing the cell area SminIs s'areaThe granularity level is calculated by the following formula:
Figure BDA0001504802990000032
replacing the numerical value in the unit matrix by using the formula, and converting the unit matrix into a granularity matrix:
Figure BDA0001504802990000033
each element r of the granularity matrixijRepresenting the level of granularity of the corresponding unit area.
Further, the step C specifically includes the following steps:
the method comprises the following steps that a dividing unit where a position point of a user to be anonymous is located is processed according to the granularity level of the dividing unit and the granularity level of a peripheral dividing unit according to the following rules:
if the granularity level of a dividing unit where a position point of a user to be anonymous is located is-1, scanning the dividing unit adjacent to the position point, if an area with the granularity level larger than or equal to 1 exists, selecting the dividing unit with the largest granularity level, and if the granularity levels of a plurality of dividing units are all the largest, selecting the dividing unit with the largest number of data points; merging the dividing units containing the anonymous user position points with the selected dividing units, and updating the granularity level after merging; if the updated granularity level is still less than 0, continuously scanning adjacent partition units of the plurality of partition units after combination, and combining the adjacent partition units until the granularity level after combination is more than-1;
if the granularity level of the division unit where the position point of the user to be anonymous is located is larger than-1, the number of people in the area is indicated to reach the parameter requirement of k anonymity, and the anonymity operation is directly carried out.
Further, if the level of the unit area where the position point of the user to be anonymous is located is larger than 1, which indicates that the number of people in the area exceeds the parameter requirement of k anonymity, a balance division method based on a geographical central line is adopted to divide the area into a plurality of sub-anonymity areas, and anonymous operation is carried out on the sub-anonymity areas.
Further, for each location point a in the area of the unit to be anonymized with a level greater than 1iExpressed as (x, y), x is latitude data, and y is longitude data; the region can be represented by a position matrix P, which is a matrix of | P | × 2, | P | represents the number of position points contained in P, and is formed as:
Figure BDA0001504802990000041
each row of data x in P in the position matrixi，yiRespectively represent the position points aiLatitude data and longitude data of;
respectively sorting the x columns and the y columns of the position matrix to obtain the maximum latitude value xmaxMinimum latitude value xminMaximum longitude value ymaxMinimum longitude value ymin(ii) a Comparison (x)max-xmin) And (y)max-ymin) The relationship of (1); if (x)max-xmin)≥(ymax-ymin) Then equals (x) in latitude valuemax+xmin) The weft of/2 is a dividing line which divides P into P1、P2(ii) a If (y)max-ymin)＞(xmax-xmin) Then the longitude value equals toymax+ymin) The meridian of/2 is a dividing line, and P is divided into sub-areas P1、P2(ii) a Points on the dividing line are all divided into P1Performing the following steps; and repeating the process, and continuously dividing the P until the sub-regions meet the k-anonymity requirement.
Further, the division of the area P by the geographical central line may result in divided sub-areas P1、P2If the number of the position points of a certain sub-region is less than K, the balance algorithm is adopted to carry out the position points of the sub-regionBalancing the amount so that each subregion finally meets the k-anonymity requirement; let P1Number of points | P1Less than K; selecting P2(K- | P) of the closest dividing line1I) points, from P2Division into P1Performing the following steps; specifically, if the dividing line is a meridian, then P is selected2The (K- | P) with the closest longitude value to the longitude1I) point, if the dividing line is a weft, then P is selected2The value of the middle latitude is closest to that of the weft (K- | P)1I) points; if the number of the position points on the dividing line exceeds (K- | P)1I) are selected randomly from the points (K-P)1I) points; will this (K- | P)1I) dots, added to P1From P2Deleting; the position points of each subregion meet the k-anonymity requirement through the balance algorithm.
Finally, using the same characteristic point as the coordinate of all position data points of the rasterized anonymous unit region or the sub-anonymous region, namely the geographic midpoint a of the regionm(xm,ym) Alternatively, wherein:
Figure BDA0001504802990000051
compared with the prior art, the invention has the following beneficial effects:
the population density characteristics of the region to be rasterized are pre-analyzed, the region is divided into a plurality of small units, anonymous regions are dynamically formed by combining and splitting the divided unit regions with different densities, and the self-adaptive k anonymous rasterization algorithm of personnel density guidance is realized to adapt to position privacy protection of scenes with different densities. The invention realizes the position anonymity by carrying out position replacement through the geographical midpoint, and can improve the anonymity service quality under the condition of unchanged position privacy degree.
Detailed Description
The following is a more detailed description of the practice of the invention.
The invention discloses a self-adaptive k-anonymization rasterization method for personnel density guidance, which comprises the following steps of:
step A, rasterization is carried out based on personnel density: and calculating the grid size of the target region according to the number of the position points in the target region, the area size of the target region and the size of the anonymity coefficient k, and rasterizing the target region according to the grid size.
Collecting a position data point N contained in the region to be rasterizedcountAnd the total area S of the regionareaFor the value of the input parameter K in the given K anonymity model, the partition granularity rho of the region is calculated by the following formula:
Figure BDA0001504802990000052
the area to be rasterized is divided from left to right and from top to bottom into two-dimensional planes of square small cells. These small cells are called partition cells (or grids), and their area is the product of partition granularity and K value:
Smin＝K*ρ
and mapping the position data points in the area to a two-dimensional plane, counting the number of the position data points in each divided unit, and representing the area to be rasterized by using a unit matrix. The unit matrix is as follows:
Figure BDA0001504802990000061
the elements of the matrix represent the partitioning units. The relative position of the element in the matrix is the same as the relative position of the partition unit in the area. Each element mijRepresenting the number of location data points in each partition unit.
B, calculating the personnel density distribution of the target area: and calculating the granularity level of each grid according to the number of the position points in the grid and the size of the anonymous coefficient k, and formalizing the granularity levels of all the grids of the target area into a granularity matrix.
For easier processing, the cell matrix is converted into a granularity matrix. For any division unit, the number of data points contained in the division unit is set as ncountIts practical area sareaAnd dividing the cell area SminIs sa′reaThe granularity level is calculated by the following formula:
Figure BDA0001504802990000062
replacing the numerical value in the unit matrix by using the formula, and converting the unit matrix into a granularity matrix:
Figure BDA0001504802990000063
each element r of the granularity matrixijRepresents the granularity level of the corresponding unit area, and the value range is-2, -1, 0, … …, N.
Step C, self-adaptive k anonymity of personnel density guidance: according to the granularity level of the grids, merging each grid (a plurality of merged grids as a whole may need to be subdivided), forming an anonymous area after self-adaptive processing based on geographic centerline division and the like, and carrying out k anonymization on position points in the anonymous area.
And rasterizing the unit area where the position point of the user to be anonymous is located according to the granularity level of the unit area and the granularity level of the peripheral area of the unit area according to the following rules:
since the cell area where the location point of the user to be anonymized is located has at least one location point, no discussion is needed for the level-2.
If the level of a unit area where the position point of the user to be anonymous is located is-1, scanning the area adjacent to the unit area, if the granularity level of the area is larger than or equal to 1, selecting the area with the maximum level, and if the levels of a plurality of areas are the maximum levels, selecting the area with the maximum number of data points in the corresponding division unit of the area. Merging the unit area to be anonymized and the selected area, and updating the granularity level after merging; if not, the region is merged with the adjacent region with the most position data points, the merged granularity level is updated, whether the updated granularity level is greater than or equal to 0 is judged, if not, the adjacent region of the merged region is continuously scanned, the adjacent region is merged according to the principle, and the scanning merging is stopped until the merged region level is greater than or equal to 0. And finally, performing subsequent processing according to the combined region level.
If the level of the unit area where the position point of the user to be anonymous is located is 0, the number of people in the area reaches the parameter requirement of k anonymity, and the unit area is directly subjected to anonymity operation without being merged or split.
If the level of the unit area where the position point of the user to be anonymous is located is greater than 1, the number of people in the area exceeds the parameter requirement of k anonymity, but in order to further improve the quality of anonymity service, a balance division method based on a geographic central line is required to divide the area into a plurality of sub-anonymity areas, and anonymous operation is carried out on the sub-anonymity areas. For each location point a in the area of the unit to be anonymized with a level greater than 1iAnd (x, y), wherein x is latitude data, and y is longitude data. The region can therefore be represented by a position matrix P, which is a matrix of | P | × 2(| P | represents the number of position points contained in P), and is formed as:
Figure BDA0001504802990000071
each row of data x in P in the position matrixi，yiRespectively represent the position points aiLatitude data and longitude data of (a).
Respectively sorting the x columns and the y columns of the position matrix to obtain the maximum latitude value xmaxMinimum latitude value xminMaximum longitude value ymaxMinimum longitude value ymin. Comparison (x)max-xmin) And (y)max-ymin) The relationship (2) of (c). If (x)max-xmin)≥(ymax-ymin) Then equals (x) in latitude valuemax+xmin) The weft of/2 is a dividing line which divides P into P1、P2. In the same way as if (y)max-ymin)＞(xmax-xmin) Then the longitude value equals (y)max+ymin) Warp of/2 is divided intoSecant dividing P into sub-regions P1、P2. Points on the dividing line are all divided into P1In (1). And repeating the process, and continuously dividing the P until the sub-regions meet the k-anonymity requirement.
Dividing a region P by a geographical center line may result in a divided sub-region P1、P2And if the number of the position points of a certain sub-region is less than K, balancing the number of the position points of the sub-region by adopting a balance algorithm, so that each sub-region finally meets the K-anonymity requirement. Let P1Number of points | P1And | < K. Selecting P2(K- | P) of the closest dividing line1I) points, from P2Division into P1In (1). Specifically, if the dividing line is a meridian, then P is selected2The (K- | P) with the closest longitude value to the longitude1I) point, if the dividing line is a weft, then P is selected2The value of the middle latitude is closest to that of the weft (K- | P)1|) points. If the number of the position points on the dividing line exceeds (K- | P)1I) are selected randomly from the points (K-P)1|) points. Will this (K- | P)1I) dots, added to P1From P2And deleted. The position points of each subregion meet the k-anonymity requirement through the balance algorithm.
Finally, using the same characteristic point as the coordinate of all position data points of the rasterized anonymous unit region or the sub-anonymous region, namely the geographic midpoint a of the regionm(xm,ym) Alternatively, wherein:
Figure BDA0001504802990000081

Claims (7)

1. a rasterization k anonymization method for personnel density guidance is characterized by comprising the following steps:
A. rasterization is performed based on personnel density: calculating the grid size of the target region according to the number of the position points in the target region, the area size of the target region and the size of the anonymity coefficient K, and rasterizing the target region according to the grid size;
B. calculating the personnel density distribution of the target area: calculating the granularity level of each grid according to the number of position points in the grid and the size of the anonymous coefficient K, and formalizing the granularity levels of all the grids in the target area into a granularity matrix;
C. k-anonymity for person density guidance: according to the granularity level of the grids, merging and splitting self-adaptive processing is carried out on each grid to form an anonymous area, and k anonymity is carried out on position points in the anonymous area.
2. The rasterization k anonymization method for personnel density guidance according to claim 1, wherein the step A specifically comprises the following steps:
collecting a position data point N contained in a region to be rasterizedcountAnd the total area S of the regionareaAnd calculating the partition granularity rho of the region by the following formula:
Figure FDA0002273652500000011
dividing a region to be rasterized into a two-dimensional plane formed by small square units from left to right and from top to bottom; these small cells are divided cells, and the area S of the small cellsminIs the product of the partition granularity p and the value of K:
Smin＝K*ρ
the K value is an input parameter K value in a given K anonymity model;
mapping the position data points in the area to a two-dimensional plane, counting the number of the position data points in each divided unit, and representing the target area by using a unit matrix, wherein the unit matrix is as follows:
Figure FDA0002273652500000012
the relative position of the elements of the matrix in the matrix is the same as the relative position of the dividing unit in the area; each element mijRepresenting position data in each partition unitThe number of points.
3. The rasterization k anonymization method for personnel density guidance according to claim 1, wherein the step B specifically comprises the following steps:
for any division unit, the number of data points contained in the division unit is set as ncountIts practical area sareaAnd dividing the cell area SminIs s'areaThe granularity level is calculated by the following formula:
Figure FDA0002273652500000021
replacing the numerical value in the unit matrix by using the formula, and converting the unit matrix into a granularity matrix:
Figure FDA0002273652500000022
each element r of the granularity matrixijRepresenting the level of granularity of the corresponding unit area.
4. The rasterization k anonymization method for personnel density guidance according to claim 1, wherein the step C specifically comprises the following steps:
the method comprises the following steps that a dividing unit where a position point of a user to be anonymous is located is processed according to the granularity level of the dividing unit and the granularity level of a peripheral dividing unit according to the following rules:
if the granularity level of a dividing unit where a position point of a user to be anonymous is located is-1, scanning the dividing unit adjacent to the position point, if an area with the granularity level larger than or equal to 1 exists, selecting the dividing unit with the largest granularity level, and if the granularity levels of a plurality of dividing units are all the largest, selecting the dividing unit with the largest number of data points; merging the dividing units containing the anonymous user position points with the selected dividing units, and updating the granularity level after merging; if the updated granularity level is still less than 0, continuously scanning adjacent partition units of the plurality of partition units after combination, and combining the adjacent partition units until the granularity level after combination is more than-1;
if the granularity level of the division unit where the position point of the user to be anonymous is located is larger than-1, the number of people in the area is indicated to reach the parameter requirement of k anonymity, and the anonymity operation is directly carried out.
5. The rasterization k anonymization method of personnel density guidance according to claim 4, wherein if the granularity level of the unit where the position point of the user to be anonymized is located is larger than 1, which indicates that the number of people in the area exceeds the parameter requirement of k anonymization, the method of balanced division based on the geographic central line is adopted to divide the area into a plurality of sub anonymization areas, and anonymous operation is carried out on the sub-areas.
6. The personnel density guided rasterization k anonymization method of claim 5, wherein for each location point a in the unit area to be anonymized with level greater than 1iExpressed as (x, y), x is latitude data, and y is longitude data; the region can be represented by a position matrix P, which is a matrix of | P | × 2, | P | represents the number of position points contained in P, and is formed as:
Figure FDA0002273652500000031
each row of data x in the position matrix Pi，yiRespectively represent the position points aiLatitude data and longitude data of;
respectively sorting the x columns and the y columns of the position matrix to obtain the maximum latitude value xmaxMinimum latitude value xminMaximum longitude value ymaxMinimum longitude value ymin(ii) a Comparison (x)max-xmin) And (y)max-ymin) The relationship of (1); if (x)max-xmin)≥(ymax-ymin) Then equals (x) in latitude valuemax+xmin) The weft of/2 is a dividing line which divides P into P1、P2(ii) a If (y)max-ymin)＞(xmax-xmin) Then the longitude value equals (y)max+ymin) The meridian of/2 is a dividing line, and P is divided into sub-areas P1、P2(ii) a Points on the dividing line are all divided into P1Performing the following steps; and repeating the process, and continuously dividing the P until the sub-regions meet the k-anonymity requirement.
7. The rasterization k anonymization method for personnel density guidance according to claim 6, wherein the division of the region P by the geography center line may result in the division of the sub-region P1、P2If the number of the position points of a certain sub-region is less than K, balancing the number of the position points of the sub-region by adopting a balance algorithm so that each sub-region finally meets the K-anonymity requirement; let P1Number of points | P1Less than K; selecting P2(K- | P) of the closest dividing line1I) points, from P2Division into P1Performing the following steps; specifically, if the dividing line is a meridian, then P is selected2The (K- | P) with the closest longitude value to the longitude1I) point, if the dividing line is a weft, then P is selected2The value of the middle latitude is closest to that of the weft (K- | P)1I) points; if the number of the position points on the dividing line exceeds (K- | P)1I) are selected randomly from the points (K-P)1I) points; will this (K- | P)1I) dots, added to P1From P2Deleting; the position points of each subregion meet the k-anonymity requirement through the balance algorithm.
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CN101873317A (en) * 2010-06-07 2010-10-27 孟小峰 Position privacy protection method for perceiving service quality
CN102892073A (en) * 2012-09-11 2013-01-23 北京航空航天大学 Continuous query oriented location anonymizing method applicable to location service system
CN103957501A (en) * 2014-03-31 2014-07-30 西安电子科技大学 Long-time request position privacy protection method based on road network prediction
CN104092692A (en) * 2014-07-15 2014-10-08 福建师范大学 Location privacy protection method based on combination of k-anonymity and service similarity
CN104618896A (en) * 2015-01-07 2015-05-13 上海交通大学 Method and system for protecting location service privacy based on grid density
US9756461B1 (en) * 2016-06-19 2017-09-05 Twinly Marketing Technologies Limited Privacy-preserving large-scale location monitoring
CN106803825A (en) * 2017-01-10 2017-06-06 西安电子科技大学 Anonymous zone building method based on query context

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