CN110138751A - Resist the car networking position data treating method and apparatus of position data poisoning attacks - Google Patents
Resist the car networking position data treating method and apparatus of position data poisoning attacks Download PDFInfo
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- CN110138751A CN110138751A CN201910337032.3A CN201910337032A CN110138751A CN 110138751 A CN110138751 A CN 110138751A CN 201910337032 A CN201910337032 A CN 201910337032A CN 110138751 A CN110138751 A CN 110138751A
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- position data
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- socialgram
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- 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
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1441—Countermeasures against malicious traffic
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- 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
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1441—Countermeasures against malicious traffic
- H04L63/145—Countermeasures against malicious traffic the attack involving the propagation of malware through the network, e.g. viruses, trojans or worms
Abstract
The present invention relates to a kind of car networking position data treating method and apparatus for resisting position data poisoning attacks, comprising the following steps: receives the position data that car networking user sends；The external source feature moved in conjunction with random walk and feature learning structuring user's；External source latent structure based on user movement speculates socialgram；The supposition socialgram is matched with the social network diagram of user, identifies toxic position data.The present invention can reduce the risk of car networking privacy of user leakage.
Description
Technical field
The present invention relates to secret protection technical fields in car networking, resist position data poisoning attacks more particularly to one kind
Car networking position data treating method and apparatus.
Background technique
With the development of mobile communication, networking technology, intellectualized technology, automobile, which will become, is only second to mobile terminal, such as
Mobile phone, the main access device of the Internet of Things of laptop etc., while car networking are answered as Internet of Things in the important of automobile industry
With at home and abroad as a new scientific and trechnolocial undertaking.In car networking, vehicle by the devices such as GPS, sensor to itself
Location information measures, and measurement position data are sent to server by Internet technology, and server joins a large amount of vehicles
The position data of network users is for statistical analysis, and ASSOCIATE STATISTICS result is used for Zhi can Jiao siphunculus Li ﹑ Intelligent Dynamic Xin breath Fu Wu ﹑
The application such as intelligent vehicle control ﹑ environmental monitoring.However, in the process, when car networking user is insincere, car networking system will
Car networking user location privacy can be caused to be leaked by position data poisoning attacks, while server data safety is by prestige
The side of body, and this privacy leakage and problem of data safety have become the maximum bottleneck of the wide general hair exhibition ﹑ application of car networking, therefore vehicle joins
User location privacy and the research of central processing problem of data safety have attracted extensive attention in net.
Part related work is absorbed in design anonymous authentication, to protect the location privacy of car networking user, thinks substantially
Want in the case where not revealing car networking subscriber identity information, server completes the authentication of user.A part of correlation work
The Privacy preserving algorithms that pursuit attack is resisted in design are focused on, the location privacy of car networking user is protected with this.Another portion
Divide related work to be dedicated to designing reliability management model in car networking, guarantees the data-privacy of car networking user.However, these
Research work has only focused on the location privacy protection of car networking user, but ignores the problem of data safety of server, i.e. opponent
Send toxic position data, it will cause server by position data poisoning attacks.Therefore correlative study work can not be resisted
Position data poisoning attacks.
Summary of the invention
Technical problem to be solved by the invention is to provide a kind of car networking positional numbers for resisting position data poisoning attacks
According to treating method and apparatus, the risk of car networking privacy of user leakage can be reduced.
The technical solution adopted by the present invention to solve the technical problems is: providing and a kind of resists position data poisoning attacks
Car networking position data processing method, comprising the following steps:
(1) position data that car networking user sends is received；
(2) the external source feature for combining random walk and feature learning structuring user's to move；
(3) the external source latent structure based on user movement speculates socialgram；
(4) the supposition socialgram is matched with the social network diagram of user, identifies toxic position data.
The step (2) specifically: all users and its position data are configured to a bipartite graph, using random walk
Mode be that each user generates α on bipartite graphwRandom walk track, the length of every random walk track are lw, and it is fixed
The transition probability of adopted random walk, by deep learning thought, in conjunction with random walk track, the movement for learning each user is special
Sign, and it is mapped as a vector row, finally export the vector row of user.
By comparing the vector row of any two user in the step (3), and calculate the line vector of two users
Similitude, when similitude be greater than threshold value when, then it is assumed that two users are friends, and friends by inference, structure
Make supposition socialgram.
The social network diagram of user is the user social contact network data sent according to social network-i i-platform in the step (4)
What construction obtained.
The step (4) specifically: k landmark of selection is saved in speculating socialgram and social network diagram respectively first
Point, is denoted as At={ a1,a2,…,akAnd As={ a1s,a2s,…,aks, the distance vector of any one node n isThat is the minimum hop count of node n to k landmark nodes, any two node nt∈AT/AtAnd ns∈AS/
AsBetween increase a line probability beWherein, ATAnd ASIt is to speculate socialgram and social network respectively
Node set in network figure, matched objective function areOptimal matching is found, i.e. maximization objective function, if
The finally node that is not matched, then it is assumed that be toxic user, data be toxic position data.
Further include the steps that combining the performance bounds for resisting technology without difference privacy Theoretical Proof of making an uproar after the step (4).
The technical solution adopted by the present invention to solve the technical problems is: also providing one kind and resists position data poisoning attacks
Car networking position data processing unit, comprising: data reception module, for receive car networking user transmission position data；
Relationship constructing module, in conjunction with the external source feature that random walk and feature learning structuring user's move, and according to the outer of user movement
Source latent structure speculates socialgram；Relationship match module, for carrying out the social network diagram of the supposition socialgram and user
Matching, identifies toxic position data.
The car networking position data processing unit further include: request and receiving module, for being sent out to social network-i i-platform
It send social network data to request, and receives the user social contact network data of social network-i i-platform transmission.
The car networking position data processing unit further include: result verification module resists technical performance for verifying.
The result verification module uses the performance bounds that technology is resisted without difference privacy Theoretical Proof of making an uproar.
Beneficial effect
Due to the adoption of the above technical solution, compared with prior art, the present invention having the following advantages that and actively imitating
Fruit: " Space Time-social activity " three-dimensional motion rule that the present invention is moved by SEEK BUS on-line customer, thus it is speculated that the social relationships of user,
Construction " speculates socialgram "；On this basis, in conjunction with the structure connection between " socialgram " of social networks and supposition socialgram
Property, optimum mapping is found, and then find toxic position data；And the hidden of the mechanism theoretically is proved in conjunction with without difference privacy of making an uproar
Private protective value boundary.The present invention breaks through the limitation of secret protection technical research in existing car networking, pays close attention to car networking for the first time
Middle position data poisoning attacks innovatively propose to speculate socialgram according to user movement external source latent structure, are in car networking
The research of secret protection technology important supplement is provided, to further decrease car networking privacy of user disclosure risk, push vehicle
In networking secret protection correlation theory system and technology enrich constantly and it is perfect.
Detailed description of the invention
Fig. 1 is the schematic diagram of the working environment of example scheme；
Fig. 2 is the schematic diagram of the composed structure of the server of embodiment；
Fig. 3 is the process signal for the processing method of car networking position data that position data poisoning attacks are resisted in embodiment
Figure；
Fig. 4 is the overall study that the processing method of car networking position data of position data poisoning attacks is resisted in embodiment
Thinking figure；
Fig. 5 is figure neighbours' schematic diagram of user in bipartite graph；
Fig. 6 is the structural relationship schematic diagram speculated between socialgram and the socialgram that social network data generates；
Fig. 7 is that the structure of the processing unit of the car networking position data for resisting position data poisoning attacks in embodiment is shown
It is intended to.
Specific embodiment
Present invention will be further explained below with reference to specific examples.It should be understood that these embodiments are merely to illustrate the present invention
Rather than it limits the scope of the invention.In addition, it should also be understood that, after reading the content taught by the present invention, those skilled in the art
Member can make various changes or modifications the present invention, and such equivalent forms equally fall within the application the appended claims and limited
Range.
Fig. 1 shows the working environment schematic diagram in one embodiment of the invention, as shown in Figure 1, car networking user 103
When position data is sent to server 101, car networking user may be incredible (such as in figure 102), deliberately generate
Toxic position data is simultaneously sent to server 101, it will causes server 101 by position data poisoning attacks, to make
It is revealed at 103 location privacy of car networking user, while 101 data safety of server is on the hazard.In addition, position data is in addition to energy
Enough the sensitive information (sensitive position such as accessed) that contains of reflection position data itself outside, is also generally comprised than position data sheet
The more sensitive personal information of body, and then lead to the leakage of more individual privacies, to be brought to car networking user 103 more tight
The privacy threats of weight.
Fig. 2 is the composed structure schematic diagram of server 101 in an example, including passes through processor, power supply module, storage
Medium, memory and communication interface.Wherein, storage medium is stored with operating system, database and one kind and resists position data poisoning
The computer applied algorithm of the processing unit of the car networking position data of attack；Processor, which is mainly responsible for, executes the computer application
Program realizes a kind of processing method of car networking position data for resisting position data poisoning attacks；Communication interface is mainly used for
Network communication is carried out with car networking user 103 and malice car networking user 102.Structure shown in Figure 2, only and embodiment
The block diagram of the relevant part-structure of scheme does not constitute the restriction for the server being applied thereon to this embodiment scheme, tool
The server of body may include perhaps combining certain components or with different than more or fewer components as shown in the figure
Component layout.
Fig. 3 shows the processing method of the car networking position data for resisting position data poisoning attacks in one embodiment
Flow diagram, in the embodiment be illustrated by taking the treatment process of server 101 as an example.
As shown in figure 3, the processing method of the car networking position data for resisting position data poisoning attacks in the embodiment
Include:
Step S301: the position data (partially may be toxic position data) that car networking user sends is received；
Step S302: in conjunction with random walk structuring user's random track；
Step S303: binding characteristic study and random track structuring user's movement external source attribute (social attribute)；
Step S304: the external source latent structure based on user movement speculates socialgram；
Step S305: social network-i i-platform receives social network data request, and server obtains user social contact network data；
Step S306: social network diagram is constructed according to social network data；
Step S307: algorithm for design finds the optimum mapping speculated between socialgram and social network diagram, and then identifies have
Malicious position data；
Step S308: technical performance is resisted in conjunction with without difference privacy theory quantization of making an uproar.
According to the scheme of the present embodiment, server receives position data from car networking user, is used by exploring car networking
The external source attribute (social attribute) of family movement, thus it is speculated that the social relationships of user, construction " speculate socialgram "；On this basis, it ties
It closes " socialgram " of social networks and speculates the structural relationship between socialgram, find optimum mapping, and then identify toxic
Position data；And theoretically in conjunction with the secret protection performance boundary for proving the mechanism without difference privacy of making an uproar.The present embodiment to
It further decreases car networking privacy of user disclosure risk, pushes the continuous of secret protection correlation theory system and technology in car networking
It is abundant and perfect.
In a specific example of the present embodiment, before above-mentioned steps S307 identifies toxic position data, further include
Step S305 and step S306, the purpose of the two steps be find user movement social attribute, construction speculate socialgram and
Social network diagram.
Based on embodiment as described above, Fig. 4 shows in a specific example and resists position data poisoning attacks
The flow diagram of the processing method of car networking position data.
After server 101 receives the position data of the transmission of car networking user terminal 102 and 103, in conjunction with random walk and
Feature learning finds the external source attribute of user movement；And then speculate the car networking user of any two participant position data collection
Between relationship；Then the relationship construction " speculating socialgram " between user by inference；Speculate socialgram in structure with
" socialgram " in social network is associated, because the movement of user is equally influenced by its social networks, i.e., social activity is to use
The external source attribute of family movement.So the user 102 of malice can be by finding the socialgram speculated in socialgram and social network
Between best match identify that is, no matched user is exactly malicious user 102.
The present embodiment is identification malicious user 102, and all users and its position data are configured to a bipartite graph G first
=(U, L, E), U are the set of all users, and L is all position data set, and E is side, the weights omega of each edge (u, l)u,l
The number of position l is accessed for user u ∈ U.In bipartite graph G, " the figure neighbours " of user u should access identical with user u
The position (as shown in Figure 5) that position, especially user u often access, the figure neighbours for defining user u are N (u).Using random trip
The mode walked is that each user generates α on bipartite graph GwRandom walk track, the length of every random walk track are lw,
In random walk process, the transition probability of random walk is defined as,
Based on this, deep learning (feature learning) thought is borrowed, in conjunction with random walk track, the movement for learning each user is special
Sign, and it is mapped as a continuous vector, finally export the vector row of user.Feature learning objective function are as follows:D is the dimension of vector row θ.Finally compare the continuous of any two user
Vector defines similar are as follows: | | θ (ui)θ(uj)||2, when similitude is greater than threshold θ0When, it is believed that two users are friends, and
Friends by inference, construction speculate socialgram (shown in such as Fig. 6 (a)).
The structure between social network diagram (shown in such as Fig. 6 (b)) that socialgram and social network data generate by inference
Relevance finds best match, and the user not being matched finally is considered as toxic user, and position is toxic position.It is first
K landmark node is first selected in speculating socialgram and social network diagram respectively, is denoted as At={ a1,a2,…,akAnd As=
{a1s,a2s,…,aks}.The distance vector of any one node n isThat is node n to k landmark sections
The minimum hop count of point.Any two node nt∈AT/AtAnd ns∈AS/AsBetween increase a line probability beWherein, ATAnd ASIt is the node set speculated in socialgram and social network diagram respectively, it is matched
Objective function isOptimal matching is found, is maximization objective function.The node not being matched finally, quilt
It is considered toxic user, data are considered as toxic position data.
The present embodiment is herein in connection with without the above performance for resisting position poisoning attacks technology of difference privacy demonstration of making an uproar.Note speculates society
Matching algorithm between intersection graph and social network diagram is α, thus it is speculated that any one user i is matched to social network diagram in socialgram
NodeRemember that any one user j and the matched weight of user i in socialgram are p (i, j).In conjunction with quasi- without difference privacy of making an uproar
It is as follows that proof resists tactful secret protection performance: for anyResisting strategy is each use
Family i provides (ε, δ)-without difference privacy of making an uproar, and wherein the expression formula of ε is as follows:
N is to speculate user in network
Number.
Based on thought same as mentioned above, the embodiment of the present invention also provides a kind of position data poisoning attacks resisted
The processing unit of car networking position data.
Fig. 7 gives the processing unit of the car networking position data for resisting position data poisoning attacks in one embodiment
Structural schematic diagram, in the embodiment be illustrated by taking server as an example.As shown in fig. 7, resisting position in the embodiment
The processing unit of the car networking position data of data poisoning attacks includes:
Data reception module 701, for receiving the position data of car networking user transmission；
Relationship constructing module 702, the external source feature for combining random walk and feature learning structuring user's to move, and root
Socialgram is speculated according to the external source latent structure of user movement；
Relationship match module 703 is identified for matching the supposition socialgram with the social network diagram of user
Toxic position data.
As shown in fig. 7, the processing unit of the car networking position data for resisting position data poisoning attacks of the present embodiment is also
Include:
Request and receiving module 704 for sending social network data request to social network-i i-platform, and receive social network
The user social contact network data that network platform is sent；
At this point, above-mentioned relation matching module combines by social network diagram and speculates the structural relationship between socialgram, know
Not toxic position data.
Result verification module 705, for verifying the performance for technology of resisting.The result verification module can be using without difference of making an uproar
Privacy Theoretical Proof resists the performance bounds of technology.
Claims (10)
1. a kind of car networking position data processing method for resisting position data poisoning attacks, which is characterized in that including following step
It is rapid:
(1) position data that car networking user sends is received；
(2) the external source feature for combining random walk and feature learning structuring user's to move；
(3) the external source latent structure based on user movement speculates socialgram；
(4) the supposition socialgram is matched with the social network diagram of user, identifies toxic position data.
2. the car networking position data processing method according to claim 1 for resisting position data poisoning attacks, feature
It is, the step (2) specifically: all users and its position data are configured to a bipartite graph, using random walk
Mode is that each user generates α on bipartite graphwRandom walk track, the length of every random walk track are lw, and define
The transition probability of random walk learns the motion feature of each user in conjunction with random walk track by deep learning thought,
And it is mapped as a vector row, finally export the vector row of user.
3. the car networking position data processing method according to claim 2 for resisting position data poisoning attacks, feature
It is, by comparing the vector row of any two user in the step (3), and calculates the phase of the line vector of two users
Like property, when similitude is greater than threshold value, then it is assumed that two users are friends, and friends by inference, construction push away
Survey socialgram.
4. the car networking position data processing method according to claim 1 for resisting position data poisoning attacks, feature
It is, the social network diagram of user is the user social contact network data structure sent according to social network-i i-platform in the step (4)
It makes.
5. the car networking position data processing method according to claim 1 for resisting position data poisoning attacks, feature
It is, the step (4) specifically: select k landmark node in speculating socialgram and social network diagram respectively first,
It is denoted as At={ a1,a2,…,akAnd As={ a1s,a2s,…,aks, the distance vector of any one node n isThat is the minimum hop count of node n to k landmark nodes, any two node nt∈AT/AtAnd ns∈AS/
AsBetween increase a line probability beWherein, ATAnd ASIt is to speculate socialgram and social network respectively
Node set in network figure, matched objective function areOptimal matching is found, i.e. maximization objective function, if
The finally node that is not matched, then it is assumed that be toxic user, data be toxic position data.
6. the car networking position data processing method according to claim 1 for resisting position data poisoning attacks, feature
It is, further includes the steps that combining the performance bounds for resisting technology without difference privacy Theoretical Proof of making an uproar after the step (4).
7. a kind of car networking position data processing unit for resisting position data poisoning attacks characterized by comprising data connect
Module is received, for receiving the position data of car networking user transmission；Relationship constructing module, in conjunction with random walk and feature learning structure
The external source feature of user movement is made, and socialgram is speculated according to the external source latent structure of user movement；Relationship match module, is used for
The supposition socialgram is matched with the social network diagram of user, identifies toxic position data.
8. the car networking position data processing unit according to claim 7 for resisting position data poisoning attacks, feature
It is, further includes: request and receiving module for sending social network data request to social network-i i-platform, and receive social activity
The user social contact network data that the network platform is sent.
9. the car networking position data processing unit according to claim 7 for resisting position data poisoning attacks, feature
It is, further includes: result verification module resists technical performance for verifying.
10. the car networking position data processing unit according to claim 9 for resisting position data poisoning attacks, feature
It is, the result verification module uses the performance bounds that technology is resisted without difference privacy Theoretical Proof of making an uproar.
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