CN112601194B - Internet of vehicles position privacy protection method and system under road network environment - Google Patents
Internet of vehicles position privacy protection method and system under road network environment Download PDFInfo
- Publication number
- CN112601194B CN112601194B CN202011441365.XA CN202011441365A CN112601194B CN 112601194 B CN112601194 B CN 112601194B CN 202011441365 A CN202011441365 A CN 202011441365A CN 112601194 B CN112601194 B CN 112601194B
- Authority
- CN
- China
- Prior art keywords
- road
- anonymous
- vehicles
- vehicle
- area
- 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
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
- H04L63/0407—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the identity of one or more communicating identities is hidden
Landscapes
- Engineering & Computer Science (AREA)
- Computer Security & Cryptography (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Hardware Design (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a method and a system for protecting the privacy of a vehicle networking position in a road network environment. The method comprises the following steps: calculating the weight of each road section based on the number of vehicles on each road section in the Voionoi region; according to the method, other adjacent related vehicle road sections are selected according to the weight ranking of the road sections and added into the anonymous set, road section homogenization of a road network model can be reduced by increasing the road sections with a plurality of weights, a road network environment closer to the real situation is formed, on the basis, the area of an anonymous area is determined and calculated, the real position of a target vehicle is replaced by the anonymous area, an attacker is difficult to obtain the accurate position of a vehicle user, characteristic attack and conjecture attack are effectively avoided, privacy leakage risks of the position of the vehicle networking are reduced, in addition, the adjacent related vehicle road sections with similar or identical weights are added into the anonymous set in the process, the search range of roads and vehicles is reduced, and the time cost of anonymous calculation is reduced.
Description
Technical Field
The invention relates to the field of location privacy protection, in particular to a method and a system for protecting the location privacy of an internet of vehicles in a road network environment.
Background
The current thing networking is rapid to be developed, and communication technology promotes rapidly, and 5G cellular network's development can accelerate the thing networking. Computing power of mobile devices is rapidly increasing. These mobile devices may also be mounted on a number of devices. Location Based Services (LBS) is an essential part of people in modern society. Such as querying nearby hotels and schools, how to find the nearest bus stop, etc. The Internet of vehicles (IoV) is an emerging product, and is developed from the Internet of things, namely vehicle ad hoc networks (VANETs). Usually, the physical layer/MAC layer selects an 802.11p protocol, and the networking technology selects an Ad-hoc mode, so that the communication delay is effectively reduced, and the network quality of a vehicle under the conditions of high-speed movement and frequent change of a network topology structure is ensured. The car networking aims to provide communication between cars and vehicles (V2V) and communication between cars and infrastructure (V2I), and the system mainly comprises an On Board Unit (OBU), an Application Unit (AU), a road base unit (RSU), and the like.
The GPS and position service is a double-edged sword, which greatly facilitates the life of people, but the leakage of personal privacy also becomes a serious problem. Personal income, identity information and physical conditions are extremely easy to leak, and can be illegally used, so that the position privacy is protected. The privacy of the car networking is closely related to the privacy of individuals, so it is also extremely important to protect the privacy of the car networking. A series of information such as the position, track, vehicle number and the like of the vehicle are easy to leak, and the leakage is easy to be illegally utilized by an attacker, so that disastrous results are generated. How to protect the LBS-based car networking service is a problem worthy of intensive research.
At present, research institutions and scholars at home and abroad have obtained a lot of research achievements in the aspect of car networking location privacy protection algorithms. From the aspect of road network division, the technology mainly comprises the following steps: an Oldham-based and Voronoi-based car networking location privacy protection algorithm. Representative algorithms based on euclidean space are: k-anonymity, l-diversity, t-proximity, position ambiguity, etc., representative algorithms based on the Voionoi graph are: location semantics anonymity, ring anonymity, cellular anonymity, etc. The internet of vehicles position privacy protection algorithm based on the Euclidean space has higher safety in an experimental environment. When an anonymous area is constructed, an area with road section intersection points (not users) as Thiessen polygon control points is formed by a Voionoi diagram-based vehicle networking location privacy protection algorithm, road network conditions of the vehicle networking are considered to a certain extent, and privacy security guarantee capability of vehicle networking locations is improved under the real condition that the road network is as close as possible to the real condition. Therefore, the research of the Voionoi diagram-based vehicle networking location privacy protection algorithm has very important theoretical and application values.
The existing vehicle networking location privacy protection algorithm based on the Voionoi diagram has the following disadvantages: in the aspect of road network division, the dynamic property of vehicle nodes and the diversity of road sections cannot be fully considered in the conventional method, the deviation of the constructed road network model and the actual situation of a complex road network is still large, and the variability of an anonymous set is influenced; in the aspect of privacy protection algorithms, due to the restriction of key factors such as anonymous area change, time-space information correlation, road section characteristics and vehicle node sensitivity, the existing algorithms still have the problems of high privacy disclosure risk, low anonymous success rate, high time complexity and the like.
Disclosure of Invention
Based on this, it is necessary to provide a method and a system for protecting location privacy of a vehicle networking in a road network environment, so as to solve the problem that contradictions among anonymity set variability, location privacy protection strength and service quality in the existing method for protecting location privacy of a vehicle networking are difficult to balance, effectively reduce privacy disclosure risks, and reduce time overhead of anonymous calculation.
In order to achieve the purpose, the invention provides the following scheme:
a vehicle networking location privacy protection method under a road network environment comprises the following steps:
determining a target road section, the number of vehicles on the target road section, a related vehicle road section, the number of vehicles on the related vehicle road section and the positions of all vehicles in a Voionoi area where the target vehicle is located; the target road segment is a road segment of the target vehicle within the Voionoi area; the relevant vehicle road segment is a road segment in the Voionoi area except the target road segment;
calculating the weight of each road section based on the number of vehicles on each road section in the Voionoi region;
sequencing all road sections in the Voionoi region from small to large according to the weight to obtain a road section sequence;
judging whether the total number of the road sections in the current anonymous set is smaller than a preset section difference degree or not; the initial anonymous set is formed by target road segments; the total number of the road sections in the initial anonymous set is 1;
if yes, calculating the difference value between the weight values of two adjacent related vehicle road sections in the current road section sequence and the target road section respectively, adding the adjacent related vehicle road sections with smaller difference values into the current anonymous set, deleting the adjacent related vehicle road sections with larger difference values from the current road section sequence, updating the current anonymous set and the current road section sequence, and returning to the step of judging whether the total number of the road sections in the current anonymous set is smaller than the difference degree of the preset road sections; the two adjacent related vehicle road sections are two related vehicle road sections adjacent to the target road section in the current road section sequence;
if not, when the total number of the vehicles is larger than or equal to a preset privacy threshold value, calculating the area of an anonymous region according to the positions of the vehicles in the road section in the current anonymous set, and when the area of the anonymous region is in a set range and the total time delay of anonymous calculation is smaller than or equal to the average response time of an anonymous server, outputting an anonymous result set of the target vehicle; the total number of the vehicles is the number of the vehicles on all road sections in the current anonymous set; the anonymous result set comprises road sections in the current anonymous set, corresponding vehicle positions, total time delay of anonymous calculation and anonymous region areas.
Optionally, before determining the target road segment, the number of vehicles on the target road segment, the relevant vehicle road segment, the number of vehicles on the relevant vehicle road segment, and the positions of all vehicles in the Voionoi region where the target vehicle is located, the method further includes:
acquiring a road traffic map of a target vehicle;
converting the road traffic map into a Voionoi graph;
and determining the Voionoi graph as the Voionoi area where the target vehicle is located.
Optionally, the calculating the weight of each road section based on the number of vehicles on each road section in the Voionoi region specifically includes:
wherein, WiIs the weight of the ith road section, n is the total number of the road sections in the Voionoi area, KiThe number of vehicles on the ith road segment.
Optionally, the calculating an area of the anonymous region according to the position of the vehicle in the road section in the current anonymous set specifically includes:
wherein S is0For anonymous region area, { (x)1,y1),…,(xj,yj),…,(xm,ym) The position set of m vehicles in the current anonymous set is defined as K, the total number of all vehicles in the current anonymous set is defined as (x)1,y1) For the position coordinates of the 1 st vehicle in the current anonymous set, (x)j,yj) For the location coordinate of the jth vehicle in the current anonymous set, (x)m,ym) The position coordinates of the mth vehicle in the current anonymous set.
The invention also provides a vehicle networking location privacy protection system under the road network environment, which comprises the following steps:
the data acquisition module is used for determining a target road section, the number of vehicles on the target road section, a related vehicle road section, the number of vehicles on the related vehicle road section and the positions of all vehicles in a Voionoi area where the target vehicle is located; the target road segment is a road segment of the target vehicle within the Voionoi area; the relevant vehicle road segment is a road segment in the Voionoi area except the target road segment;
the weight calculation module is used for calculating the weight of each road section based on the number of vehicles on each road section in the Voionoi region;
the sorting module is used for sorting all road sections in the Voionoi region from small to large according to the weight values to obtain a road section sequence;
the anonymity module is used for judging whether the total number of the road sections in the current anonymity set is smaller than the difference degree of the preset road sections; the initial anonymous set is formed by target road segments; the total number of the road sections in the initial anonymous set is 1;
if yes, calculating the difference value between the weight values of two adjacent related vehicle road sections in the current road section sequence and the target road section respectively, adding the adjacent related vehicle road sections with smaller difference values into the current anonymous set, deleting the adjacent related vehicle road sections with larger difference values from the current road section sequence, updating the current anonymous set and the current road section sequence, and returning to the step of judging whether the total number of the road sections in the current anonymous set is smaller than the difference degree of the preset road sections; the two adjacent related vehicle road sections are two related vehicle road sections adjacent to the target road section in the current road section sequence;
if not, when the total number of the vehicles is larger than or equal to a preset privacy threshold value, calculating the area of an anonymous region according to the positions of the vehicles in the road section in the current anonymous set, and when the area of the anonymous region is in a set range and the total time delay of anonymous calculation is smaller than or equal to the average response time of an anonymous server, outputting an anonymous result set of the target vehicle; the total number of the vehicles is the number of the vehicles on all road sections in the current anonymous set; the anonymous result set comprises road sections in the current anonymous set, corresponding vehicle positions, total time delay of anonymous calculation and anonymous region areas.
Optionally, the car networking location privacy protection system in the road network environment further includes a Voionoi area determination module; the Voionoi area determining module specifically includes:
the traffic map determining unit is used for acquiring a road traffic map of the target vehicle;
the conversion unit is used for converting the road traffic map into a Voionoi graph;
and the area determining unit is used for determining the Voionoi graph as the Voionoi area where the target vehicle is located.
Optionally, the weight calculation module specifically includes:
wherein, WiIs the weight of the ith road section, n is the total number of the road sections in the Voionoi area, KiThe number of vehicles on the ith road segment.
Optionally, the area of the anonymous region in the anonymous module specifically includes:
wherein S is0For anonymous region area, { (x)1,y1),…,(xj,yj),…,(xm,ym) The position set of m vehicles in the current anonymous set is defined as K, the total number of all vehicles in the current anonymous set is defined as (x)1,y1) For the position coordinates of the 1 st vehicle in the current anonymous set, (x)j,yj) For the location coordinate of the jth vehicle in the current anonymous set, (x)m,ym) The position coordinates of the mth vehicle in the current anonymous set.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a method and a system for protecting the privacy of a vehicle networking position in a road network environment. The method comprises the following steps: calculating the weight of each road section based on the number of vehicles on each road section in the Voionoi region; the method comprises the steps that other adjacent vehicle road sections are selected according to the weight sequence of the road sections and added into an anonymous set, road section homogenization of a road network model is reduced by increasing the road sections with a plurality of weights, a road network environment closer to a real situation is formed, on the basis, the area of an anonymous area is determined and calculated, the real position of a target vehicle is replaced by the anonymous area, an attacker is difficult to obtain the accurate position of a vehicle user, characteristic attack and conjecture attack are effectively avoided, and the privacy leakage risk of the position of the Internet of vehicles is reduced; in the process, adjacent related vehicle road sections with similar or identical weights are added into the anonymous set, anonymity is favorably carried out in the Voionoi area where the target road section is located, the search range of roads and vehicles is reduced, the time consumption for searching vehicles across the Voionoi area to form the anonymous set is saved, and the anonymous calculation cost of the anonymous server is effectively controlled on the premise that high-density attack caused by the fact that vehicle nodes are located in a centralized position is avoided.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is an anonymous system architecture diagram provided by an embodiment of the present invention;
FIG. 2 is a schematic view of a Voionoi diagram generator;
FIG. 3 is a flowchart of a method for protecting privacy of locations in a car networking system in a road network environment according to an embodiment of the present invention;
FIG. 4 is a road network partition diagram provided by an embodiment of the present invention;
fig. 5 is a diagram of a specific implementation process of a method for protecting privacy of locations in a car networking in a road network environment according to an embodiment of the present invention;
FIG. 6 is a comparison graph of privacy disclosure risks provided by embodiments of the invention;
FIG. 7 is a graph comparing time cost for anonymous computation provided by an embodiment of the present invention;
fig. 8 is a structural diagram of a car networking location privacy protection system in a road network environment according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a vehicle networking location privacy protection method and system in a road network environment, so as to realize an anonymous set construction method capable of reflecting vehicle node dynamics and road section diversity, a location privacy protection method capable of reflecting anonymous set changes and effectively preventing road section feature attacks and vehicle node privacy leakage, solve the problem that contradictions among anonymous set variability, location privacy protection strength and service quality in the existing vehicle networking location privacy protection method are difficult to balance, effectively reduce privacy leakage risks and reduce anonymous calculation time overhead.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
The anonymous system structure for location privacy protection of the car networking is composed of three parts to meet the real-time property of the anonymous process due to the bandwidth limitation of the vehicle user (target vehicle) and the location server (LBS server). As shown in fig. 1, the first part is the internet of vehicles, and the second part is a third-party server (central anonymity server) responsible for anonymity-related processes. The third part is a location server for data processing, data return or receiving requests for location services. Data is transmitted between the vehicle and a third party server using a secure channel, such as: SSL. The third party anonymity server hides the IP, identification, and geographic information of the vehicle. Each vehicle is equivalent to a node, has communication and certain data processing capacity, can receive GPS signals and has a positioning function.
The car networking location privacy protection method in the road network environment of the embodiment is based on the following assumptions:
(1) moving videos and Third party servers are trusted, LBS is semi-trusted, service is provided to users, and user privacy may be revealed.
(2) Attackers can obtain some priori knowledge through a public database, and meanwhile, the attackers have analysis reasoning capability and can obtain late knowledge through anonymous information and the priori knowledge.
(3) The vehicle distribution has a certain regularity, but has a different distribution density.
The Voionoi diagram forming process comprises the following steps: let CS be a set CS that includes n uniformly distributed discrete points within a planar region ═ CS1,CS2,CS3,…CSnAt any point CSiBelongs to Voionoi region V of CS (CS)i) All points in U to CSiSet of points at minimum distance, called V zone, V (CS)i)＝{A|dist(A,CSi)≤dist(A,CSk),A∈U,k≠1},dist(A,CSi) Representative points A and CSiOf the Euclidean distance between, CSiCalled a voiono graph generator, as shown in fig. 2. By CSiThe entire road network Voionoi graph configured as a generator may be denoted as V (u) ═ V (CS)1),V(CS2),…V(CSn)}. Obviously, the adjacent V areas have common edges and are not overlapped with each other, so that the whole road network area is covered.
Wherein
In fact, as long as the perpendicular bisector between all the points is made, the area formed by these straight lines is the V region, such as the dashed line part B in the schematic diagram of the voiono diagram forming process1、B2、B3、B4The region of formation.
Anonymous requirement Q { (x)j,yj),K,L,Smin,Smax,T},(xj,yj) The position coordinates of the jth vehicle are shown, and K is the degree of anonymity. The size of K is proportional to the strength of the anonymity protection required. L is the set number of roads, and the probability of the presumed attack can be reduced by adding the roads to the anonymous set. Combining K and L, the probability of being attacked is
SminAnd SmaxThe size of the anonymous area can be dynamically controlled, so that the service quality and the resource consumption of the server maintain a certain balance, the anonymous area is controlled in the area to the greatest extent, and the anonymous area is prevented from being too large. The vehicle networking has dynamics, network topological structures at different moments are quite different, vehicles are located at different positions at different moments, the average response time T of the anonymous server is used as the upper limit of anonymous calculation time, anonymous calculation can be completed before the topological structures of the vehicle networking change, and the dynamics and the variability of anonymous sets of vehicle nodes in the privacy protection of the vehicle networking positions are fully considered. The main purpose of anonymous protection is to reduce resource consumption of the server while satisfying location privacy, thereby reducing processing delay of the server and improving service quality.
Anonymous region area control: let the center of gravity of the anonymous region be G (x)0,y0) The nodes constituting the region are { (x) respectively1,y1),(x2,y2),(x3,y3),…,(xm,ym)}(m≤K)，G(x0,y0) Maximum distance to nodes constituting polygon of anonymous region is RmaxThen, then
According to RmaxThe size of the anonymous region area S may be controlled.
Fig. 3 is a flowchart of a method for protecting privacy of a location in a car networking in a road network environment according to an embodiment of the present invention. Referring to fig. 3, the method for protecting privacy of a location of a vehicle networking in a road network environment of the embodiment includes:
step 101: and determining the target road section, the number of vehicles on the target road section, the related vehicle road section, the number of vehicles on the related vehicle road section and the positions of all vehicles in the Voionoi area where the target vehicle is located. The target road segment is a road segment of the target vehicle within the Voionoi area; the relevant vehicle road segment is a road segment within the Voionoi area except the target road segment.
Based on a road network undirected graph G, a vehicle user set W and a fuzzy position c (x, y, r) of a vehicle user, positioning road sections of positions of the vehicle user, counting the number of the road sections in a Voionoi area of the vehicle user, and setting the number of the ith road section as Li(i is 1,2,3 … n), and the number of vehicles on the ith road is Ki(i ═ 1,2,3 … n) in which a circular area with the coordinates (x, y) of the vehicle user as the center and r as the radius is taken as the ambiguous position of the vehicle user, and a neighboring node coordinate position (x) is obtained from the arear,yr) The real position of the user is replaced, and an attacker is difficult to obtain the accurate position of the vehicle user, so that privacy disclosure is avoided. r is anonymous user (x, y) to nodes { (x) constituting the polygon of anonymous region1,y1),(x2,y2),(x3,y3),…,(xm,ym) The minimum distance of (removing the anonymous node itself), then
Step 102: and calculating the weight of each road section based on the number of vehicles on each road section in the Voionoi region. The method specifically comprises the following steps:
wherein, WiIs the weight of the ith road section, n is the total number of the road sections in the Voionoi area, KiThe number of vehicles on the ith road segment.
Step 103: and sequencing all road sections in the Voionoi region from small to large according to the weight to obtain a road section sequence.
Step 104: and judging whether the total number of road sections in the current anonymous set S is smaller than a preset section difference degree. If yes, go to step 105; if not, go to step 106. Wherein the initial anonymous set is formed by the target road segment LbThe structure is as follows; the total number L of road segments in the initial anonymous set is 1.
Step 105: and calculating the difference value between the weight values of the two adjacent related vehicle road sections in the current road section sequence and the target road section, adding the adjacent related vehicle road sections with smaller difference values into the current anonymous set, deleting the adjacent related vehicle road sections with larger difference values from the current road section sequence, updating the current anonymous set and the current road section sequence, and returning to the step 104. And the two adjacent related vehicle road sections are two related vehicle road sections adjacent to the target road section in the current road section sequence.
Specifically, the method comprises the following steps: and constructing a road network model. (5a) If L is less than LC, adding a second road section into S; (5b) if L is greater than LC, go to step 106. Wherein LC is a preset road difference degree, and LC depends on different W in the previous step 103iThe greater the value, the more the number of road segments added to S, which can reduce the homogenization of road segments constituting the road network model and the smaller the probability of vehicle position privacy leakage. Let the road section L where the user is locatediWeight of and target link (reference link) LbThe weight difference value of is WbiI.e. Wbi＝|Wb-WiI, suppose Wbl1Is the difference value of the weight of the left road section and the reference road section of the sequenced reference road sections, Wbr1Is the difference value of the right side road section of the sorted reference road section and the weight value of the reference road section, if Wbl1≥Wbr1Adding the right road section of the sequenced road section, otherwise adding the left road section, and then comparing Wbl2And Wbr1And adding the road sections with smaller weight difference, namely the road sections with similar weights into the S so as to resist the high risk caused by the presumed attack due to the obvious and uneven distribution of the vehicles on the road, and so on to form a road network model and an anonymous set.
Step 106: and when the total number of the vehicles is greater than or equal to a preset privacy threshold value, calculating the area of the anonymous area according to the positions of the vehicles in the road section in the current anonymous set, and when the area of the anonymous area is in a set range and the total time delay of anonymous calculation is less than or equal to the average response time of an anonymous server, outputting an anonymous result set of the target vehicle. The total number of the vehicles is the number of the vehicles on all road sections in the current anonymous set; the anonymous result set comprises road sections in the current anonymous set, corresponding vehicle positions, total time delay of anonymous calculation and anonymous region areas.
Specifically, the method comprises the following steps: and constructing an anonymous set. (6a) Judging the number K of all vehicles in the S; (6b) if K < KC, anonymous failure; (6c) if K is larger than or equal to KC, executing the area calculation and control process of the anonymous area; and the KC is a preset privacy threshold value and is selected according to the K-anonymity thought and the position privacy security level.
The anonymous area calculation and control process comprises the following steps:
(7a) node in S { (x)1,y1),(x2,y2),(x3,y3),…,(xm,ym) Sequentially connecting (m is less than or equal to K) to form a polygonal anonymous region, wherein the area of the anonymous region is as follows:
wherein S is0For anonymous region area, { (x)1,y1),…,(xj,yj),…,(xm,ym) The position set of m vehicles in the current anonymous set is defined as K, the total number of all vehicles in the current anonymous set is defined as (x)1,y1) For the position coordinates of the 1 st vehicle in the current anonymous set, (x)j,yj) For the location coordinate of the jth vehicle in the current anonymous set, (x)m,ym) The position coordinates of the mth vehicle in the current anonymous set.
(7b) If Smin≤S0≤SmaxAn anonymous computation time control procedure is performed.
(7c) If S0＜SminOr S0＞SmaxIf the cache is not expired, the information in the cache is imported to reduce the anonymity time.
Wherein SminAnd SmaxRespectively representing the minimum and maximum anonymous area areas required to reach KC, i.e. SminAs the center of gravity G (x) of an anonymous polygon0,y0) Area of anonymous region S when distance d to nodes constituting polygon of anonymous region is minimummaxAs the center of gravity (x) of an anonymous polygon0,y0) The area of the anonymous region when the distance d to the node constituting the anonymous region polygon is maximum, and the center of gravity (x) of the anonymous polygon0,y0) The calculation formula is as follows:
the distance d is calculated as follows:
anonymous computing time control process:
(8a) extracting T and T from the anonymous system;
(8b) and if T is less than or equal to T, outputting an anonymous result set S.
(8c) If T is larger than T, anonymity fails, and waiting for next anonymity; wherein T is the total time delay of anonymous calculation, and T is the average response time of the anonymous server.
Preferably, before step 101, the method further comprises:
acquiring a road traffic map of a target vehicle; converting the road traffic map into a Voionoi graph; and determining the Voionoi graph as the Voionoi area where the target vehicle is located.
The method for protecting the privacy of the positions of the internet of vehicles under the road network environment is provided based on a Voionoi graph-based road network dividing method and K-anonymity, l-diversity and position-fuzzy privacy protection theory.
In practical application, the method for protecting the privacy of the car networking location in the road network environment is specifically implemented as follows:
(1) when a certain vehicle in the internet of vehicles needs to be anonymous, the vehicle firstly sends a request to a third-party server, possibly through an RSU (remote subscriber unit), and then the RSU sends request information to the third-party server.
(2) The third-party server locates the road section where the vehicle needing anonymization is located, an anonymization algorithm process is carried out to form an anonymization set, then the third-party server sends the anonymization set to the position server for location, the result is returned to the third-party server, the third-party server returns the request to the vehicle networking user after necessary result refinement, and the request may pass through an RSU.
(3) And the Internet of vehicles user performs necessary calculation on the obtained result, obtains the required result and finishes anonymization.
(4) The road network is abstracted into an undirected graph with edge weights, and as shown in FIG. 4, G (V, E) represents a set of road intersections of the road network, V represents a set of road intersections of the road networkiPoints where the road network edges and edges intersect are indicated, and E indicates a set of road network lower edges. Any of eijRepresenting nodes v of the road networkiAnd vjAre directly connected. In order to meet the requirement of road section diversity, Voionoi graph division is carried out on the road network, and a node dimension D is selectede(Pi)≥dmThe point of (2) is used as a V map generator. At this time, a point in any V region can reach dmAnd the diversity characteristic of the road is met. Get dmIs 3, node dimension De(vi)T＝{De(v1),De(v2)…De(v10)}T＝{3,4,2,4,3,2,1,2,4,3}TBecause of dmSo the point chosen is v 31,v2,v4,v5,v9,v10To compose a voiono diagram of the road.
(5) The road traffic map is firstly converted into a Voionoi diagram, then a road network undirected graph G which accords with K anonymity and l-diversity is simplified from the Voionoi diagram, namely when the position of vehicles in a scene is anonymized, other K-1 vehicles are selected according to the sequence of road section weight values and added into an anonymity set, and the road section homogeneity of a road network model is reduced by adding the road sections with a plurality of weight values.
(6) When a vehicle wants to request the location service, it sends a request to the location server, and after receiving the location request, the third-party server can look up the location condition of the requesting vehicle, the network topology structure of the node at the moment and the road network state.
(7) The position server locates the road where the vehicle is located, namely the road in the Voionoi area where the vehicle is located, and at the moment, the position server counts the number K of the vehicles in the road section where the vehicle is locatedbAnd counting the number of vehicles on other roads in the Voionoi area where the vehicles are located, and recording the number as Ki，i＝1,2,3,…,n。
(8) The anonymous server calculates the weight W of each roadiI is 1,2,3, …, n, and the weight W is giveniSorting the data from small to large to form a weight set { Wmin,…,Wmid,…,WmaxWhen the weights are equal, only parallel sorting is needed, and then a greedy algorithm is applied to enable the local weight mean value to be closest to the road mean value of the vehicle, so that a required road set { L } is formedb,(Ll1 or Lr1),(Ll2 or Lr1) … and the number of vehicles on each road { K }b,(Kl1 or Kr1),(Kl2 or Kr1) …, based on this, a road network model is constructed which approximates the real situation of the road network.
(9) After adding L roads, if L is greater than or equal to LC, counting the number of vehicles on the L roads, if the total number K of vehicles is greater than or equal to KC, calculating concealmentPolygonal area S formed by famous vehicles0If S ismin≤S0≤SmaxAnd if T is less than or equal to T (not overtime), the anonymization is successful, the anonymization server sends the anonymization result to the anonymization vehicle at the moment, and the step (10) is executed, otherwise, the anonymization fails.
(10) The anonymous vehicle selects its own location information from a result including a plurality of location information according to its own location demand. The specific process is shown in fig. 5.
The car networking location privacy protection method under the road network environment is described as follows:
the vehicle networking location privacy protection method under the road network environment has the following advantages:
(1) effectively reduce the privacy disclosure risk of the position of the Internet of vehicles
A road network model is constructed by adopting a Voionoi graph division method, an anonymous area and an anonymous set are constructed by comprehensively utilizing a K anonymous algorithm, a l diversity algorithm and a position fuzzy algorithm, a position privacy protection method is designed, other K-1 vehicles can be selected and added into the anonymous set according to the sequence of road section weights, road sections with a plurality of weights can be added, the homogenization of the road sections forming the road network model is reduced, a road network environment which is closer to the real condition is formed, on the basis, the real positions of users are replaced by circular areas (anonymous areas), an attacker is difficult to obtain the accurate positions of the vehicle users, characteristic attacks and conjecture attacks are effectively avoided, the privacy leakage risk of the position of the Internet of vehicles is reduced, and the effect is shown in figure 6.
(2) Effectively reduces the time overhead of anonymous calculation of vehicle nodes
In the design of the position privacy protection method, roads with similar or identical weights are added into the anonymous set, the area size of the anonymous set is controlled through the maximum distance between the gravity center of the anonymous area and the anonymous node, anonymity is favorably carried out in the Voionoi area where the roads are located, the searching range of the roads and vehicles is reduced, the time consumption for searching vehicles to form the anonymous set across the Voionoi area is saved, the anonymous calculation cost of the anonymous server is effectively controlled on the premise of avoiding high-density attack caused by the fact that the vehicle nodes are located in a centralized position, and the effect is shown in figure 7.
The invention also provides a car networking location privacy protection system under a road network environment, as shown in fig. 8, the car networking location privacy protection system under the road network environment comprises:
the data acquisition module 201 is configured to determine a target road segment, the number of vehicles on the target road segment, a related vehicle road segment, the number of vehicles on the related vehicle road segment, and positions of all vehicles in a Voionoi area where the target vehicle is located; the target road segment is a road segment of the target vehicle within the Voionoi area; the relevant vehicle road segment is a road segment within the Voionoi area except the target road segment.
And the weight calculation module 202 is configured to calculate a weight of each road segment based on the number of vehicles on each road segment in the Voionoi region.
And the sorting module 203 is configured to sort all road segments in the voinoi region from small to large according to the weights, so as to obtain a road segment sequence.
The anonymizing module 204 is configured to determine whether the total number of the road segments in the current anonymizing set is smaller than a preset segment difference degree; the initial anonymous set is formed by target road segments; the total number of road segments in the initial anonymous set is 1.
If yes, calculating the difference value between the weight values of two adjacent related vehicle road sections in the current road section sequence and the target road section respectively, adding the adjacent related vehicle road sections with smaller difference values into the current anonymous set, deleting the adjacent related vehicle road sections with larger difference values from the current road section sequence, updating the current anonymous set and the current road section sequence, and returning to the step of judging whether the total number of the road sections in the current anonymous set is smaller than the difference degree of the preset road sections; and the two adjacent related vehicle road sections are two related vehicle road sections adjacent to the target road section in the current road section sequence.
If not, when the total number of the vehicles is larger than or equal to a preset privacy threshold value, calculating the area of an anonymous region according to the positions of the vehicles in the road section in the current anonymous set, and when the area of the anonymous region is in a set range and the total time delay of anonymous calculation is smaller than or equal to the average response time of an anonymous server, outputting an anonymous result set of the target vehicle; the total number of the vehicles is the number of the vehicles on all road sections in the current anonymous set; the anonymous result set comprises road sections in the current anonymous set, corresponding vehicle positions, total time delay of anonymous calculation and anonymous region areas.
As an optional implementation manner, the car networking location privacy protection system in the road network environment further includes a Voionoi region determination module; the Voionoi area determining module specifically includes:
the traffic map determining unit is used for acquiring a road traffic map of the target vehicle; the conversion unit is used for converting the road traffic map into a Voionoi graph; and the area determining unit is used for determining the Voionoi graph as the Voionoi area where the target vehicle is located.
As an optional implementation manner, the weight calculation module specifically includes:
wherein, WiIs the weight of the ith road section, n is the total number of the road sections in the Voionoi area, KiThe number of vehicles on the ith road segment.
As an optional implementation manner, the area of the anonymous region in the anonymous module specifically includes:
wherein S is0For anonymous region area, { (x)1,y1),…,(xj,yj),…,(xm,ym) The position set of m vehicles in the current anonymous set is defined as K, the total number of all vehicles in the current anonymous set is defined as (x)1,y1) For the position coordinates of the 1 st vehicle in the current anonymous set, (x)j,yj) For the location coordinate of the jth vehicle in the current anonymous set, (x)m,ym) The position coordinates of the mth vehicle in the current anonymous set.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (8)
1. A vehicle networking position privacy protection method under a road network environment is characterized by comprising the following steps:
determining a target road section, the number of vehicles on the target road section, a related vehicle road section, the number of vehicles on the related vehicle road section and the positions of all vehicles in a Voionoi area where the target vehicle is located; the target road segment is a road segment of the target vehicle within the Voionoi area; the relevant vehicle road segment is a road segment in the Voionoi area except the target road segment;
calculating the weight of each road section based on the number of vehicles on each road section in the Voionoi region;
sequencing all road sections in the Voionoi region from small to large according to the weight to obtain a road section sequence;
judging whether the total number of the road sections in the current anonymous set is smaller than a preset section difference degree or not; the initial anonymous set is formed by target road segments; the total number of the road sections in the initial anonymous set is 1;
if yes, calculating the difference value between the weight values of two adjacent related vehicle road sections in the current road section sequence and the target road section respectively, adding the adjacent related vehicle road sections with smaller difference values into the current anonymous set, deleting the adjacent related vehicle road sections with larger difference values from the current road section sequence, updating the current anonymous set and the current road section sequence, and returning to the step of judging whether the total number of the road sections in the current anonymous set is smaller than the difference degree of the preset road sections; the two adjacent related vehicle road sections are two related vehicle road sections adjacent to the target road section in the current road section sequence;
if not, when the total number of the vehicles is larger than or equal to a preset privacy threshold value, calculating the area of an anonymous region according to the positions of the vehicles in the road section in the current anonymous set, and when the area of the anonymous region is in a set range and the total time delay of anonymous calculation is smaller than or equal to the average response time of an anonymous server, outputting an anonymous result set of the target vehicle; the total number of the vehicles is the number of the vehicles on all road sections in the current anonymous set; the anonymous result set comprises road sections in the current anonymous set, corresponding vehicle positions, total time delay of anonymous calculation and anonymous region areas.
2. The method for protecting privacy of locations in internet of vehicles in road network environment according to claim 1, further comprising, before determining the target road segment, the number of vehicles on the target road segment, the related vehicle road segment, the number of vehicles on the related vehicle road segment, and the locations of all vehicles in the Voionoi area where the target vehicle is located:
acquiring a road traffic map of a target vehicle;
converting the road traffic map into a Voionoi graph;
and determining the Voionoi graph as the Voionoi area where the target vehicle is located.
3. The method according to claim 1, wherein the weight of each road segment is calculated based on the number of vehicles on each road segment in the Voionoi area, specifically:
wherein, WiIs the weight of the ith road section, n is the total number of the road sections in the Voionoi area, KiThe number of vehicles on the ith road segment.
4. The method according to claim 1, wherein the method for privacy protection of locations in car networking in road network environment is characterized in that the area of the anonymous area is calculated according to the locations of the vehicles in the road section in the current anonymous set, specifically:
wherein S is0For anonymous region area, { (x)1,y1),…,(xj,yj),…,(xm,ym) The position set of m vehicles in the current anonymous set is defined as K, the total number of all vehicles in the current anonymous set is defined as (x)j,yj) J-1, 2,3,.. m, which is the position coordinate of the jth vehicle in the current anonymous set.
5. A car networking position privacy protection system under road network environment, characterized by includes:
the data acquisition module is used for determining a target road section, the number of vehicles on the target road section, a related vehicle road section, the number of vehicles on the related vehicle road section and the positions of all vehicles in a Voionoi area where the target vehicle is located; the target road segment is a road segment of the target vehicle within the Voionoi area; the relevant vehicle road segment is a road segment in the Voionoi area except the target road segment;
the weight calculation module is used for calculating the weight of each road section based on the number of vehicles on each road section in the Voionoi region;
the sorting module is used for sorting all road sections in the Voionoi region from small to large according to the weight values to obtain a road section sequence;
the anonymity module is used for judging whether the total number of the road sections in the current anonymity set is smaller than the difference degree of the preset road sections; the initial anonymous set is formed by target road segments; the total number of the road sections in the initial anonymous set is 1;
if yes, calculating the difference value between the weight values of two adjacent related vehicle road sections in the current road section sequence and the target road section respectively, adding the adjacent related vehicle road sections with smaller difference values into the current anonymous set, deleting the adjacent related vehicle road sections with larger difference values from the current road section sequence, updating the current anonymous set and the current road section sequence, and returning to the step of judging whether the total number of the road sections in the current anonymous set is smaller than the difference degree of the preset road sections; the two adjacent related vehicle road sections are two related vehicle road sections adjacent to the target road section in the current road section sequence;
if not, when the total number of the vehicles is larger than or equal to a preset privacy threshold value, calculating the area of an anonymous region according to the positions of the vehicles in the road section in the current anonymous set, and when the area of the anonymous region is in a set range and the total time delay of anonymous calculation is smaller than or equal to the average response time of an anonymous server, outputting an anonymous result set of the target vehicle; the total number of the vehicles is the number of the vehicles on all road sections in the current anonymous set; the anonymous result set comprises road sections in the current anonymous set, corresponding vehicle positions, total time delay of anonymous calculation and anonymous region areas.
6. The system of claim 5, further comprising a Voionoi region determining module; the Voionoi area determining module specifically includes:
the traffic map determining unit is used for acquiring a road traffic map of the target vehicle;
the conversion unit is used for converting the road traffic map into a Voionoi graph;
and the area determining unit is used for determining the Voionoi graph as the Voionoi area where the target vehicle is located.
7. The internet of vehicles location privacy protection system under road network environment of claim 5, wherein the weight calculation module specifically is:
wherein, WiIs the weight of the ith road section, n is the total number of the road sections in the Voionoi area, KiThe number of vehicles on the ith road segment.
8. The vehicle networking location privacy protection system under road network environment according to claim 5, wherein an area of the anonymous area in the anonymous module is specifically:
wherein S is0For anonymous region area, { (x)1,y1),…,(xj,yj),…,(xm,ym) The position set of m vehicles in the current anonymous set is defined as K, the total number of all vehicles in the current anonymous set is defined as (x)j,yj) J-1, 2,3,.. m, which is the position coordinate of the jth vehicle in the current anonymous set.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011441365.XA CN112601194B (en) | 2020-12-08 | 2020-12-08 | Internet of vehicles position privacy protection method and system under road network environment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011441365.XA CN112601194B (en) | 2020-12-08 | 2020-12-08 | Internet of vehicles position privacy protection method and system under road network environment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112601194A CN112601194A (en) | 2021-04-02 |
| CN112601194B true CN112601194B (en) | 2022-04-29 |
Family
ID=75192009
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011441365.XA Active CN112601194B (en) | 2020-12-08 | 2020-12-08 | Internet of vehicles position privacy protection method and system under road network environment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112601194B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113192331B (en) * | 2021-04-26 | 2022-04-15 | 吉林大学 | Intelligent early warning system and early warning method for riding safety in internet environment |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105246072A (en) * | 2015-09-01 | 2016-01-13 | 重庆邮电大学 | User position privacy protection method under road network environment and system thereof |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8856939B2 (en) * | 2008-09-05 | 2014-10-07 | Iowa State University Research Foundation, Inc. | Cloaking with footprints to provide location privacy protection in location-based services |
| CN110636065B (en) * | 2019-09-23 | 2021-12-07 | 哈尔滨工程大学 | Location point privacy protection method based on location service |
| CN110972133B (en) * | 2019-11-20 | 2023-04-18 | 安徽师范大学 | Semantic position privacy protection method based on space-time confusion in road network environment |
-
2020
- 2020-12-08 CN CN202011441365.XA patent/CN112601194B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105246072A (en) * | 2015-09-01 | 2016-01-13 | 重庆邮电大学 | User position privacy protection method under road network environment and system thereof |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112601194A (en) | 2021-04-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Luo et al. | Blockchain enabled trust-based location privacy protection scheme in VANET | |
| Lu et al. | Federated learning for data privacy preservation in vehicular cyber-physical systems | |
| Wang et al. | Consortium blockchain for secure resource sharing in vehicular edge computing: A contract-based approach | |
| Lu et al. | Blockchain and federated learning for 5G beyond | |
| Velliangiri et al. | Blockchain based privacy preserving framework for emerging 6G wireless communications | |
| Huo et al. | Y ou C an W alk A lone: trajectory privacy-preserving through significant stays protection | |
| Gao et al. | TrPF: A trajectory privacy-preserving framework for participatory sensing | |
| Yang et al. | Density-based location preservation for mobile crowdsensing with differential privacy | |
| CN110300029B (en) | Position privacy protection method for preventing edge-weight attack and position semantic attack | |
| Zheng et al. | K-anonymity location privacy algorithm based on clustering | |
| Lin et al. | Toward better data veracity in mobile cloud computing: A context-aware and incentive-based reputation mechanism | |
| Cheng et al. | A dynamic evolution mechanism for IoV community in an urban scene | |
| Tang et al. | A Trust‐Based Model for Security Cooperating in Vehicular Cloud Computing | |
| Chai et al. | A hierarchical blockchain aided proactive caching scheme for internet of vehicles | |
| CN112601194B (en) | Internet of vehicles position privacy protection method and system under road network environment | |
| Li et al. | FEEL: Federated end-to-end learning with non-IID data for vehicular ad hoc networks | |
| CN115052286A (en) | User privacy protection and target query method and system based on location service | |
| Xu et al. | Personalized location privacy protection for location-based services in vehicular networks | |
| He et al. | Differentially private tripartite intelligent matching against inference attacks in ride-sharing services | |
| Yang et al. | An overview of sybil attack detection mechanisms in vfc | |
| CN115035720A (en) | Traffic road condition data acquisition and processing method and management system based on satellite positioning | |
| Xu et al. | IFTS: A location privacy protection method based on initial and final trajectory segments | |
| Yan et al. | Reputation consensus-based scheme for information sharing in internet of vehicles | |
| Ye et al. | A trajectory privacy-preserving algorithm based on road networks in continuous location-based services | |
| CN108260083B (en) | Privacy protection method based on position ambiguity |
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 |