CN101404572A - Network node total trust degree estimation method based on feedback trust aggregation - Google Patents

Network node total trust degree estimation method based on feedback trust aggregation Download PDF

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CN101404572A
CN101404572A CNA2008102322729A CN200810232272A CN101404572A CN 101404572 A CN101404572 A CN 101404572A CN A2008102322729 A CNA2008102322729 A CN A2008102322729A CN 200810232272 A CN200810232272 A CN 200810232272A CN 101404572 A CN101404572 A CN 101404572A
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trust
feedback
degree
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桂小林
李小勇
赵博
魏广福
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Xian Jiaotong University
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Abstract

The invention belongs to the field of trust management which is orientated to large-scale open network application, in particular relates to an evaluation method of total trust degree of a network node based on feedback trust aggregation, and is particularly fit for various large-scale open distributed applications with a network as a basic platform, such as networks, pervasive computation, P2P computation, Ad hoc and electronic commerce and the like. The method constructs a brand-new weighted directed tree data structure, namely a direct trust tree (DTT), takes direct trust degree as directed edge weighting, then carries out search of feedback trust and aggregation computation, and evaluates the total trust degree of the network node. The method can save network resources, increase network speed, and deny the subjective elements of the total trust evaluation in the conventional method, and thus the method has higher actual application value.

Description

A kind of network node total degree of belief appraisal procedure based on the feedback trust aggregating
Technical field
The invention belongs to the trust management field of using, be specifically related to a kind of network node total degree of belief appraisal procedure based on the feedback trust aggregating towards extensive open network.
Background technology
Along with being further investigations basic platform, the various complicated distributed application environments of opening (as grid, P2P, ecommerce, E-Government, Ad hoc and general fit calculation etc.) with the Internet, system shows as the dynamic cooperative model of being made up of a plurality of software services.In this dynamic and uncertain environment, for the user provides reliable, safe credible execution environment and information sharing service, be faced with stern challenge: at first, applied environment has features such as isomerism, dynamic, distributivity and multi-management area; Secondly, the way to manage of nodes such as user, application program, computational resource and computing environment no longer is concentrated and sealing, but open, dynamic and distributed; In addition, in open system, the behavior of node is difficult to tolerance and prediction, and the judgement of node identity does not have the managerial authority of centralization to rely on.The appearance of these new features and new problem, make many safe practice and means based on the traditional software form, especially security certificate mechanism, as static faith mechanism among access control list, public key certificate system and the PKI (Pubic Key Infrastructure) etc., no longer be applicable to the safety problem of system under the open network environment.Given this, scholars have proposed " dynamic trust management " technology at complicated open network environment, for the safety of the reliability service of guaranteeing distributed network, resource is shared and credible utilization provides new thinking, and become a hot issue that needs to be resolved hurrily.
Still there are some problems in the research at present, mainly comprise: at first, a key issue in the dynamic trust management research is how to feed back trust information by the effective and efficient manner polymerization, and existing trusting relationship forecasting mechanism is mostly by feeding back the search of trust in whole system based on the broadcast mode of trust chain, cause the slow convergence of system's computing under large-scale distributed environment and huge network bandwidth expense, and then influenced the extensibility of system.Secondly, existing trust model is when the calculated population confidence level, adopt subjective fusion computational methods such as expert opinion method or average weights method mostly, causing predicts the outcome has bigger subjective composition, has influenced the science of credible decision-making, and has lacked flexibility, in case weights are determined, with be difficult in actual applications by system dynamics go to adjust it, cause model to lack adaptivity, and then influenced the accuracy of model.
Summary of the invention
At above problem, the object of the present invention is to provide a kind of network node total degree of belief appraisal procedure based on the feedback trust aggregating, it can conserve network resources, improves network speed, abandons the subjective composition in the total trust degree assessment.
In order to achieve the above object, the present invention is achieved by the following technical solutions: a kind of network node total degree of belief appraisal procedure based on the feedback trust aggregating, and in network, appoint and get node P i, node P j, node P iTo node P jDirect degree of belief be Γ D(P i, P j), node P iNeed be to node P jTotal trust degree Γ (P i, P j) assess, it is characterized in that, may further comprise the steps:
Step 1: for the arbitrary node P in the network i, set up with node P iBe root, directly the neighbor node of trusting is the direct trust tree DTT of the double-layer structure of branch, and described direct trust tree DTT is the cum rights directed tree, node P iThe direct degree of belief of its neighbor node of directly trusting is arbitrarily set the directed edge weight of DTT as described direct trust;
Step 2: according to direct trust tree DTT, with node P iBe root search node P jFeedback person, set up feedback person set { W 1, W 2... W L, wherein feedback person is designated as node W arbitrarily k, k=1,2...L, L is feedback person's a number, determines node P iWith each feedback person's trust path, calculate feedback person's weighted factor Formula is as follows:
Figure A20081023227200052
Γ in the formula D(P m, P n) represent from P iTo W kNode P on the trust path mTo its descendant node P nDirect degree of belief, LEVEL is node W kApart from node P iThe number of plies;
Step 3: computing node P iTo node P jFeedback degree of belief Γ I(P i, P j), formula is as follows:
Figure A20081023227200061
Step 4: calculate assessment node P iTo node P jTotal trust degree Γ (P i, P j), formula is as follows:
Figure A20081023227200062
And according to total trust degree Γ (P i, P j) assessment result, node P iBe node P jSelectivity provides the service quality of setting classification.
Further improvement of the present invention is:
In step 2, described definite node P iWith each feedback person's trust path, as node P iWhen mulitpath being arranged, select nearest path with certain feedback person.
In step 2, set quality factor η ∈ [0,1], only screen node P iTo its direct degree of belief Γ DThe feedback person of 〉=η participates in node P iTo node P jFeedback degree of belief Γ I(P i, P j) calculate.
In step 2, the setpoint distance factor lambda, during search feedback person, the maximum number of plies of search is λ.
The present invention is by making up a kind of brand-new direct trust data tree structure DTT (Direct TrustTree), utilize DTT to feed back the trust information search, introduce quality factor simultaneously and regulate the scale that polymerization is calculated automatically apart from two parameters of the factor, and the structure of DTT is set up automatically according to the historical interaction data between the node fully, so this method does not need too many space-time expense, do not need to keep the needed adding of tree topology (JION) in the general network control and leave the control of (LEAVE) message yet, reduce network bandwidth consumption, strengthened the extensibility of system in large-scale distributed system; Search and polymerization that the present invention adopts feedback to trust more meet assessing based on self feed back mechanism total trust degree of human psychology cognition, are used for substituting the subjective judgement method of definite weight commonly used at present, can improve the accuracy of Model Calculation.
The inventor has carried out simulated experiment in " the complication system Modeling Platform " that realize based on the JAVA language, investigate the autgmentability of this method on time and space two dimensions by two parameters: (1) polymerization (ACT computing time, Aggregation Computing Time), be defined as under various network node scale expense average time that the feedback trust aggregating calculates.(2) average storage overhead (ASC, Average Storage Costs), refer to feed back the trust aggregating computational methods trust calculate in the size of the shared average memory space of various control messages and data structure.The inventor has observed what ASC of ACT of this method under dynamic variation network environment greatly, find from experimental result, at dynamic change of a height and busy environment, increase along with the system interaction traffic carrying capacity, this method on average can improve about 20% than the ACT of traditional weighted average method, and average storage overhead (ASC) can improve about 40%.
Description of drawings
Fig. 1 is node P 0The structure schematic diagram of ground floor DTT.
Fig. 2 is the node P of LEVEL=3 0Feedback person's weighted factor based on the example schematic diagram of DTT, solid arrow is represented the cum rights directed edge among the figure, the weights of the numeral directed edge on the solid arrow, dotted arrow is represented trust path.LEVEL1, LEVEL2, LEVEL3 represent the level (distance from root node to this node is respectively 1 layer, 2 layers, 3 layers) at node place.
Fig. 3 is the feedback search schematic diagram, illustrate feedback person's node searching flow process of the present invention and traditional feedback degree of belief appraisal procedure based on broadcasting respectively, wherein dotted arrow is represented query messages Query (Feedbacks), the fine line arrow is represented feedback message QueryHit (Feedbacks), and the runic lines are represented the cum rights directed edge among the DTT.
Embodiment
Below in conjunction with description of drawings and concrete example the present invention is described in further detail.
Based on the network node total degree of belief appraisal procedure of feedback trust aggregating, in network, appoint and get node P i, node P j, node P iTo node P jDirect degree of belief be Γ D(P i, P j), node P iNeeds assessment node P jTotal trust degree Γ (P i, P j), specifically may further comprise the steps:
At first, make up directly trust tree DTT.
For the arbitrary node P in the network i, set up with node P iBe root, directly the neighbor node of trusting is the direct trust tree DTT of the two-layer tree structure of branch, and described direct trust tree DTT is the cum rights directed tree, node P iThe direct degree of belief of its neighbor node of directly trusting is arbitrarily set the directed edge weight of DTT as described direct trust.
Make up new data structure directly trust the tree DTT be based on the direct trusting relationship between the node.Be located among the open network, if two node P are arranged iAnd P jInterbehavior once took place, and claimed node P so jBe node P iThe neighbor node (TNN, Trusted Neighbor Nodes) of trust.Node P iThe neighbor node of trust have a plurality ofly, so logically just constituted one with P iBe root, the neighbor node of trust is the two-layer tree structure of branch.According to above notion, the neighbor node of trust also has the neighbor node of the trust of oneself, and the like, meeting forms the tree structure of wooden fork multilayer more than in network like this, and this tree structure is called node P iDirect trust tree DTT.
With reference to Fig. 1, node P has been described 0The structure principle of ground floor DTT, P 1And P 2Be P 0Neighbor node (that is: P 0And P 1, P 2Interbehavior took place in the past, P 0Local data base in preserve P 1And P 2Trust value, claim P 1And P 2Be P 0Neighbor node, be similar to " acquaintance " in the human society), and P 1And P 2Neighbor node is also arranged, node P of structure that like this can be from level to level 0DTT.Because the structure of DTT is by keeping based on the mutual trust value of history in the node local data base fully, so the structure of DTT does not need to the needed addings of other tree-like enclosed structures (JION) and leaves (LEAVE) control messages, as seen DTT is a kind of logic data structure that is based upon application layer, and keep (mainly being the expense of keeping the tables of data of neighbor node) that needs less overhead just can realize DTT.
Secondly, according to the search that DTT feeds back trust information, calculate feedback person's weighted factor
Figure A20081023227200081
Computing node P iTo node P jFeedback degree of belief Γ I(P i, P j).
(1) according to directly trusting tree DTT, with node P iBe root search node P jFeedback person, set up feedback person set { W 1, W 2... W L, wherein feedback person is designated as node W arbitrarily k, k=1,2...L, L is feedback person's a number, determines node P iWith each feedback person's trust path, calculate feedback person's weighted factor
Figure A20081023227200082
Formula is as follows:
Figure A20081023227200091
Γ in the formula D(P m, P n) represent from P iTo W kNode P on the trust path mTo its descendant node P nDirect degree of belief, LEVEL is node W kApart from node P iThe number of plies.
(2) computing node P iTo node P jFeedback degree of belief Γ I(P i, P j), formula is as follows:
Figure A20081023227200092
With reference to Fig. 2, the node P of expression LEVEL=3 0Feedback person's weighted factor based on the example of DTT.As LEVEL=1 the time,
Figure A20081023227200093
When LEVEL=2
Figure A20081023227200094
When LEVEL=3,
Figure A20081023227200095
If node P 0Need obtain node P 14The feedback degree of belief, at P 0DTT go up altogether that search obtains 3 node P 5, P 8, P 9With P 14Interbehavior took place, and corresponding mutual satisfaction evaluation is respectively:
Γ D(P 5，P 14)＝0.6，Γ D(P 8，P 14)＝0.8，Γ D(P 9，P 14)＝0.9。According to formula (2):
Figure A20081023227200096
Figure A20081023227200098
According to formula (1):
Γ I(P 0，P 14)＝(0.28×0.6+0.21×0.8+0.24×0.9)/(0.28+0.21+0.24)＝0.75616。
By
Figure A20081023227200099
Definition as can be seen, along with the increase of LEVEL,
Figure A200810232272000910
Value reduce gradually. in addition, when feedback person's direct trust value hour, illustrate that its confidence level is lower, and then illustrate that its feedback information also has lower confidence level.According to above character, in order to improve the polymerization speed of feedback trust information in large-scale distributed system, the present invention has introduced two parameters: quality factor and the scale of regulating the calculating of feedback trust aggregating apart from the factor.
Quality factor η ∈ [0,1] is a constant of default, has only feedback person's direct trust value Γ DDuring 〉=η, this feedback person's feedback information is only believable.The Γ of certain node in DTT DDuring＜η, this node and be that the feedback of all nodes all is incredible on the subtree of root with this node.With reference to Fig. 2 example, the η of default=0.65 is so from P 5Feedback with disallowable, because Γ D(P 5, P 14)=0.6, it is less than η.
By the effective scale of Control and Feedback trust aggregating computing of quality factor, computing convergence that on the one hand can enhanced system also can reduce the malice feedback of the node of low trust value, the fail safe that improves system on the other hand.
Apart from factor lambda be one more than or equal to 1 positive constant, be used for the propagation degree of depth of Control and Feedback credential request information in DTT.When LEVEL≤λ, node is transmitted to the neighbor node (child node) of oneself with solicited message, otherwise stops forwarding.
Can reduce the length of trust chain by the distance factor, effectively improve the polymerization arithmetic speed of system.With reference to Fig. 2 example, default λ=3 are so from node P 7, P 8, P 9The feedback of neighbor node will be disallowable.
In particular cases, need screen the feedback trust information that obtains.Following situation may take place in feedback information, is not unique directly trusting the path of setting from root node to some feedback person's nodes.For example, with reference to Fig. 4, node P 0To node P 9May have two paths P 0→ P 1→ P 4→ P 9And P 0→ P 4→ P 9The node P that calculates by formula (1) and formula (2) like this 9To node P 14The feedback trust value just have two, that is to say and produced ambiguity, generation for fear of this situation, can carry out following agreement: if from root node to some feedback persons when if mulitpath is arranged, adopt the shortest link in path to calculate the foundation of final feedback degree of belief, and other path is given up to fall as us; Can certainly adopt other agreement.
Then, calculate assessment node P iTo node P jTotal trust degree Γ (P i, P j).
In formula (2), when LEVEL=0, we define
Figure A20081023227200101
Expression is from the feedback of the root node of DDT, just the feedback of node self.One of this method is also calculated the direct degree of belief of node self as the global trusting degree element, and the weight of the direct degree of belief of node self is maximum (being 1) in all feedback persons.We are called " the global trusting flowcollector aggregation scheme FlowCollector of self feed back (Self-feedback) " this mechanism.Computing formula is as follows:
Figure A20081023227200111
At last, according to total trust degree Γ (P i, P j) assessment result, node P iBe node P jSelectivity provides the service quality of setting classification.For example: a FTP services sites is arranged in certain open network, in order to guarantee security of network system, this FTP website has been introduced trust valuation mechanism, all service requester nodes are carried out the assessment of degree of belief, according to the assessment result of degree of belief, provide different classes of service quality to the node of different degree of beliefs.Suppose website P 0The service quality of Three Estate can be provided, establish the grade of service type and represent, website P with S set 0S can be defined as: S={s 1, s 2, s 3, s wherein 1The expression denial of service, s 2Represent read-only, s 3Expression both can read also can write.Our the service and decision-making function Ψ (Γ (P that can be defined as follows then 0, P j)):
&Psi; ( &Gamma; ( P 0 , P j ) ) = s 3 , 0.5 < &Gamma; ( P 0 , P j ) &le; 1 s 2 , 0.2 &le; &Gamma; ( P 0 , P j ) &le; 0.5 s 1 , 0 &le; &Gamma; ( P 0 , P j ) < 0.2
If node P 0Computational methods by this patent obtain certain entity P jTotal trust degree be Γ (P 0, P j)=0.19, then according to decision function Ψ, decision process is Ψ (Γ (P 0, P j))=Ψ (0.19)=s 1, node P is described jLevel of trust lower, website P 0To refuse to be P jThe service service is provided.If Γ (P 0, P j)=0.40, then Ψ (Γ (P 0, P j))=Ψ (0.40)=s 2, expression node P jCan read node P 0Resource, if Γ (P i, P j)=0.90, then Ψ (Γ (P 0, P j))=Ψ (0.90)=s 3, expression expression node P jPromptly can read node P 0Resource, also data can be preserved (uploading) to P 0Memory on.
With reference to Fig. 3, the search procedure based on feedback person's node in the network node total degree of belief appraisal procedure of feedback trust aggregating described in the invention is described.It is different with traditional broadcast mode, and method of the present invention is in order to assess network service request (Service Requesting) node P 14Total trust degree, network node P 0Only query messages Query (Feedbacks) is sent to the neighbor node P that oneself trusts 2With P 3, and do not send to incredible node P 1With P 4, same P 2With P 3Also only will trust query messages sends to believable neighbor node, and the like, finish until the inquiry control procedure.Node P 8, P 9, P 11Be the feedback person's node that searches, node P 8, P 9, P 11Use feedback message QueryHit (Feedbacks) with in their local data bases about P 14Direct degree of belief feed back to node P 0Analysis by example, the present invention utilizes DTT to feed back the trust information search as can be seen, introduce quality factor simultaneously and regulate the scale that polymerization is calculated automatically apart from two parameters of the factor, and the structure of DTT is set up automatically according to the historical interaction data between the node fully, so this method does not need too many space-time expense, do not need to keep the needed adding of tree topology (JION) in the general network control and leave the control of (LEAVE) message yet, reduce network bandwidth consumption, strengthened the extensibility of system in large-scale distributed system; Search and polymerization that the present invention adopts feedback to trust more meet assessing based on self feed back mechanism total trust degree of human psychology cognition, can be used for substituting the subjective judgement method of definite weight commonly used at present.

Claims (4)

1, a kind of network node total degree of belief appraisal procedure based on the feedback trust aggregating is appointed in network and is got node P i, node P j, node P iTo node P jDirect degree of belief be Г D(P i, P j), assessment node P iTo node P jTotal trust degree Г (P i, P j), it is characterized in that, may further comprise the steps:
At first, for the arbitrary node P in the network i, set up with node P iBe root, directly the neighbor node of trusting is the direct trust tree DTT of the double-layer structure of branch, and described direct trust tree DTT is the cum rights directed tree, described node P iThe direct degree of belief of its neighbor node of directly trusting is arbitrarily set the weight of the directed edge of DTT as described direct trust;
Secondly, according to direct trust tree DTT, with node P iBe root search node P jFeedback person, set up feedback person set { W 1, W 2... W L, wherein feedback person is designated as node W arbitrarily k, k=1,2...L, L is feedback person's a number, determines node P iWith each feedback person's trust path, calculate feedback person's weighted factor
Figure A2008102322720002C1
, formula is as follows:
Г in the formula D(P m, P n) represent from P iTo W kNode P on the trust path mTo its descendant node P nDirect degree of belief, LEVEL is node W kApart from node P iThe number of plies (jumping figure hops);
Then, computing node P iTo node P jFeedback degree of belief Г I(P i, P j), formula is as follows:
Figure A2008102322720002C3
At last, calculate assessment node P iTo node P jTotal trust degree Г (P i, P j), formula is as follows:
Figure A2008102322720002C4
And according to total trust degree Г (P i, P j) assessment result, node P iBe node P jSelectivity provides the service quality of setting classification.
2, a kind of network node total degree of belief appraisal procedure based on the feedback trust aggregating according to claim 1 is characterized in that, in step 2, and described definite node P iWith each feedback person's trust path, as node P iWhen mulitpath being arranged, select nearest path with certain feedback person.
3, a kind of network node total degree of belief appraisal procedure based on the feedback trust aggregating according to claim 1 is characterized in that, in step 2, sets quality factor η ∈ [0,1], only screens node P iTo its direct degree of belief Г DThe feedback person of 〉=η participates in node P iTo node P jFeedback degree of belief Г I(P i, P j) calculating.
4, a kind of network node total degree of belief appraisal procedure based on the feedback trust aggregating according to claim 1 is characterized in that, in step 2, and the setpoint distance factor lambda, during search feedback person, the maximum number of plies of search is λ.
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Cited By (7)

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CN101969647A (en) * 2010-09-10 2011-02-09 南京邮电大学 Trust model-based cooperative communication method in mobile self-organized network
CN102075424A (en) * 2011-01-11 2011-05-25 华中科技大学 Feedback updating mechanism-based trust routing method
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Publication number Priority date Publication date Assignee Title
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CN101969647B (en) * 2010-09-10 2013-03-06 南京邮电大学 Trust model-based cooperative communication method in mobile self-organized network
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CN102075424A (en) * 2011-01-11 2011-05-25 华中科技大学 Feedback updating mechanism-based trust routing method
CN102075424B (en) * 2011-01-11 2012-11-21 华中科技大学 Feedback updating mechanism-based trust routing method
CN103118010B (en) * 2013-01-11 2016-04-06 中国传媒大学 A kind of trust value computing method based on hyperbolic function
CN103118010A (en) * 2013-01-11 2013-05-22 中国传媒大学 Trust value calculation method based on hyperbola function
CN105447036A (en) * 2014-08-29 2016-03-30 华为技术有限公司 Opinion mining-based social media information credibility evaluation method and apparatus
CN105447036B (en) * 2014-08-29 2019-08-16 华为技术有限公司 A kind of social media information credibility evaluation method and device based on opining mining
CN105991617A (en) * 2014-12-01 2016-10-05 塔塔咨询服务有限公司 Computer implemented system and method for secure path selection using network rating
CN105991617B (en) * 2014-12-01 2020-04-24 塔塔咨询服务有限公司 Computer-implemented system and method for selecting a secure path using network scoring
CN105991596A (en) * 2015-02-15 2016-10-05 中兴通讯股份有限公司 Access control method and system
CN105991596B (en) * 2015-02-15 2020-11-20 中兴通讯股份有限公司 Access control method and system

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