A Trust Model Based on Capability and Quality for P2P Networks

Junfeng Tian Institute of Network Technology

Hebei University Baoding, P.R. China

e-mail: tjf@hbu.edu.cn

Dongdong Sun Institute of Network Technology

Hebei University Baoding, P.R. China

e-mail: chouzi19821982@126.com

Xiaohui Yang Institute of Network Technology

Hebei University Baoding, P.R. China

e-mail: yxh@hbu.edu.cn

Abstract—In existing P2P network trust models, the evaluation offered by applicant is always relatively simple, so there is lack of prior information in computing the trust value, which would weaken the rationality and objectivity of P2P model. To solve this problem, this paper proposes a novel trust model which is based on node’s capability and quality. Through the definition of capability and quality, we propose multi-dimensional evaluation vectors with which we induce the method of computing the trust value. Simulation and analysis show that our novel model can provide effective trust services and is eligible to resist and distinguish the malicious nodes which are characterized by attack, conspiracy and strategy.

Keywords-Prior Information, Capability, Quality, Trust Model, Evaluation Vectors

I. INTRODUCTION In the past few years, P2P technology has developed

rapidly, and various P2P applications are highly approbated. There are not central nodes in P2P networks, the relation between any nodes is equal-each node can provide his own resources to other nodes and also share the resources of others [1]. P2P networks are dynamic, openness and anonymity. Because of its dynamic [2], nodes can enter and logout the network at any moment. Also because of its openness and anonymity, some nodes only share resources and do not provide the resources [3], network resources are not fully utilized. There are even some nodes to provide false resources and distort truth of the network resources. In conclusion, it is very important and necessary to the network.

The trust mechanism represents the comprehensive evaluation of behavior and ability between one user and another, at present the establishment of trust mechanism mainly depend on the trust model [4-6]. The existing trust model is to calculate the trust value of service providers (SP) for service requester (SR), if the trust value meets certain conditions, the service can be carried out. After the service accomplished, SR gives the evaluation to this service. There are a little of central nodes in the PKI (Public Key Infrastructure) trust model, which supervised the behavior of other nodes, the legitimacy of central nodes were guaranteed by CA through awarding a certificate. There are some problem in such model[7], such as the failure of single point and poor scalability. In addition, there is a kind of trust model based on the global reputation [8] and local reputation [9], which uses the sociology knowledge from human relationships. If SR does not understand SP, SR will determine the trust value of SP through asking the recommend information which was

given by the friend nodes of SP. Because there are a great deal of methods to calculate the trust value, the model has been widely studied.

In recent years, some new models have emerged in the domestic and international studies, which were similar with the model introduced in this paper:

S. Kamvar [8] proposed the global trust model EigenTrust which was based on the trust transformation. EigenTrust calculated the global trust value of each node according to the trust iterative between the neighbor nodes. This method can reflect the true behaviors of nodes actually. But EigenTrust model had a high time complexity and low-risk capability, and did not give proof of iteration convergence.

The models advanced by S. Kamvar always assumed that the recommendation information which was provided by the high credible credibility nodes was authentic, but this assumption is not always valid. According to the similarity-weighted recommendation, Li Jingtao [10] and others proposed the trust model SWRTRUST which was based on the weighted nodes’ behaviors similarity, and improved the iterative speed. This model could identify and resist the several categories of malicious nodes synergies cheating acts in the large-scale P2P networks to a some extent.

Indeed, however the credibility of recommend nodes, the validity of the recommend information was uncertain. To solve this problem, Tian Chun-Qi established the trust model RETM [11] based on the DS evidence theory. This model divided the nodes to download the files into four categories, utilized the ratio of the numbers of the download files to each category and the total number of documents as recommended information. Through combining the recommended information and evidence, they established the trust model which linked the recommend information’s certainty and uncertainty and thus get an objective result. But in the model RETM, the evaluation to the node was monotony. Also, there was a accuracy problem in the algorithm of the evidence pretreatment.

The most models mentioned above focused on how to determine the trust mechanisms and judge the node malicious acts according to the existent evaluate information, but they ignored how the evaluation information was given. In the inspection to credible degree of recommendation information, these model only examined the credible degree of nodes itself and ignored credible degree of recommendation information. However, TMCQ established a rational P2P networks trust mechanism though inspecting credible degree of recommendation information.

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DOI 10.1109/FBIE.2008.75

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Through introducing the definition of dynamic service capability and combining the P2P environment characteristics-dynamic, open and anonymity, the author proposes a trust model TMCQ based on node’s capability and quality for p2p networks. The paper is organized as follows: (1)Through inspecting the credibility of the nodes, this paper introduces the definition of the capability and quality. We give the full consideration to the node dynamic service capabilities and utilize the combination of capability and quality as the basis for the nodes credibility calculation. (2) Our model uses the values of capability and quality as the evaluation vector which was given by the model’s evaluation system which can ensure the objectivism and rationality of the model. (3) We use the support degree algorithm to combine the recommended information and give a better algorithm to identify of the spam information in the recommendation information. (4) We use the real UCI datasets to test the new model and list the experimental results.

II. THE ESTABLISHMENT OF THE TRUST MODEL In order to discuss specifically, our model bases on the

P2P file-download environment. we firstly give the definition of several basic terms and subsequently design the process of building trust model.

Definition 1: State (St): This is a parameter used to describe the positive level of SP service.

Definition 2: Remaining time (Rm): It is the time from sending the service requests to the requests have been dealt.

Definition 3: Capability (CP): It is used to describe dynamic service capabilities of the node at this time. CP is a two-dimensional vector which memorizes the current St and Rm.

Definition 4: Quality (QT): QT is a two-dimensional vector which describes download speed (Dsp) and the download quality (Dqu).

Definition 5: Evaluation vector (F): Quaternion group

( , , , )F St Rm Dsp Dqu= is called the evaluation vector,

St denotes the state average value of SP after the completion

of the services, Rm denotes the time SR staying at SP, Dsp denotes the average download speed SR from SP, Dqu denotes the quality evaluation to the files which SR provides to SP.

Definition 6: Credible Degree (T): It is the credible degree SR to SP. The calculation formulation as follow:

T St Rm Dsp Dquλ α β γ= + + + (1) The basic idea of the TMCQ trust model is: (1): Compute every SP’s remaining time and St; (2): Compute n SPs’ CP; (3): Search m recommendation nodes RP of every SP,

computer every SP’s F using RP’s Evaluation vector; (4): Compute credible value T combining CP and F, the

computation formulation as follow: ( ) ( )

2 2St S t R m R mT D sp D quλ α β γ+ += + + +

(2) (5): Find one trust vector’s T meeting the requirements

from n SP, download the resource;

(6): After SR downloads the resources; calculate the evaluation vectors according to the evaluation mechanism.

III. BASIC CONCEPTS IN THE TRUST MODEL

A. Capability(CP) Capability is computed through the temporal dynamical

serving capability. It is similar to the capability of wicket. The temporal CP is mainly given by the serving quality, but it is made of several facts. TMCQ introduces the two above factors when computing CP:

(1). Download status: the degree of active service node is a very important indicator, the number of nodes in itself a very high reputation, but in the provision of services due to objective reasons is very unstable, such as server software failures, virus infections caused by the network to plug And so on. For this type of nodes, the system design to the appropriate mechanism for real-time monitoring and punishment.

(2). Remaining time: It is the time from putting the service requests to the requests have been dealt.

B. Quality(QT) In the download process, the node is the quality of their

closely related to the quality of service, if the node to provide high quality documents, and to provide the download speed, TMCQ considers the high quality of the nodes, so TMCQ used the two volume To study the quality of the node, that is, download speed and quality of the downloaded files.

(1). Speed of downloading: SP was available to SR, may be different, SR showed a different download speeds. Because the node is a download speed of the changes in the value of the differential to take it to calculate the speed of file download more reasonable, the formula is as follows

Fl

t

dDspd

= (3)

Where Dsp represents the download speed, Fl represents the size of the downloaded file, download the file, dt represents the time required. Used to describe the differential file download time, to guarantee good nodes in the provision of services will not be opportunistic, but also to make certain that the reputation of integrity to upload files.

(2). the quality of downloading files: Node in the provision of services to download files, may provide users with false documents or even malicious files, so the need for some mechanism for evaluating the quality of the paper. Assuming the files can be classified to five class that is G(good),C(common), N(no response), I(inauthentic) ，

M(malicious)，the evaluation mechanism is as follows: x G C N I M

F(x)(file quality) 0.8 0.6 0.4 0.2 0 In TMCQ view, download speed and quality of the

downloaded file is the impact of the two trust in the calculation of key indicators; the two nodes as well as the value of and length of stay on the state constitutes a node to download the evaluation vector; use this evaluation to describe

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the vector of the node The quality of service can be very detailed portrait of a node in the time available.

C. The evaluation vector TMCQ proposed a new evaluation of the vector used to

make the evaluation methods, and gave this assessment of the credibility of vector computing nodes.

TMCQ believes that the system should be based on SR evaluation of the download process is given vector, rather than by SR. To that end, TMCQ set up a node agent for the node to download real-time monitoring, evaluation and given vector. Vector evaluation by the Quaternary unit

( , , , )St Rm Dsp Dqu . St represents the state average when SR download files, Rm represents the length of stay, Dsp represents the download speed; Dqu represents the quality of the download; the four factors used to record the process of downloading a file, can be a more comprehensive description of the service.

D. Compute the trust value After getting the recommendation information,

recommended node itself has a certain recommendation credibility, to that end, recommended in the synthesis of information on each recommendation of nodes need to give different weights. In TMCQ, we define the support value as the weight. Support value is the degree how the recommendation information had been supported by others. The essence of this method is: If one recommendation information is similar with the other majority recommended information, the evidence is more reliable, contrarily, it can not be trusted.

Before calculate the trust value, we should pre-treat the recommendation information, the model uses the exponential method to standardize data, the approach is as follows:

min( )max min

xf x −=− . （4）

Where x is a vector for the elements, max and min were her out of the maximum and minimum value. After processing, the vector for each element of each of [0,1] as a proportion of the value of each element of elimination Dimensional, linear transformation for the converted, the simulation results (see Figure 1) proved that transformation does not change the distribution of data.

Definition 3.1: The recommended information is defined as a vector, denoting Fi.

Definition 3.2 distance matrix (D), for any two recommended information, the distance is defined as follows:

221 ( 2 , )

2ij i j i jd F F F F= + − < > (5).

Where iF represents the second vector norm, ,i jF F< > represents the inner product of the two vectors. the distance matrix is a symmetric matrix.

Definition 3.3 similar matrix(S), the similarity of two recommended information is defined as:

1ij ijs d= − . （6）

This constitutes a similarity matrix known as the similarity matrix. We can get the distance matrix for symmetric matrix easily.

Definition 3.4 the supporting vector (Spd), the support value of any recommendation information is defined as:

1

n

i ijjj i

Spd s=≠

=∑ . （7）

Definition 3.5 the recommended credibility vector (Cd), the recommendation credibility of any recommended information is defined as:

1

ii n

i

SpdCdSpd

=∑

. （8） Easy to know all information recommended by the

recommendation of the credibility is 1. Definition 3.6 evaluation vector (F): After computed the

recommendation credibility of every recommendation information, the credibility is regarded as the weight of recommended information. Then computing the evaluation vector as follows:

1

n

i iF Spd F=∑ . （9）

Evaluation vector reflected that SP provides SR when the downloading is completed, SR gave a comprehensive evaluation which is a static four-dimensional vector to SP. CP reflected the evaluation- a dynamic two-dimensional vector which was given by SR when SR send the request to SP. We can get the trust value through the synthesis of the evaluation vector and CP according to the formulation (2). The trust value is the integration of the real-time and historical service capability, which can reflect the SP’s service ability authentically.

IV. EXPERIMENTAL RESULTS We carry out the simulation experiments of the trust model

in this section. The simulation environment is based on P4 2.8GHZ CPU, 1G RAM, and it is programmed by c++ language.

In the simulation, we assumed that there are 50000 files uniformly distributed in 1000 nodes-that is, 50 files in each node. In the simulation, each node selects a file which does not belong to the node itself to download every time. Finally, if the user holds the file, the downloading is successful, whereas, the downloading is fail. The ratio of the successful downloading numbers and the total downloading numbers is defined as the transaction success rate.

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A. The influence for support value of disposing data in exponential method In TMCQ, because every dimension in the evaluation

information is different, the model uses the index approach. It has been proved that this method does not change the distribution of data. In order to verify this point of view, experimental results prove that TMCQ does not change the distribution of data after the index treatment. We randomly select ten sets of evaluation information to test. Figure 1 shows that there is no difference in the support degree after the index treatment.

deposing data in exponential method

0

0.05

0.1

0.15

1 3 5 7 9

real data

deposed data

Figure 1. The influence for Support value of disposing data in exponential method

B. Rate of successful identification to malicious nodes in TMCQ In literature [11], RETM used the evidence pretreatment,

that is, filter the noisy information. TMCQ shows the better results in the dealing with the noisy information in comparison with other models. In the simulation experiments, we use the iris database to generate 1000 sets of data. Every dataset contain 10 vectors. Iris dataset contain 150 four-dimensional vectors, it is divided into three categories, every category has 50 points. According to the principle of majority rule, the model determines evaluation information of the malicious nodes. And, we assume the ratio of the malicious nodes is under 50%. If the class labels of the malicious node determined by TMCQ are different from other models, the result is right. The ratio of the right judge numbers and the total judge numbers is defined as the successful identification rate. As shown in Figure 2, the value of the successful identification rate is high. This shows that our model’s accuracy in identifying the malicious nodes is very high.

Figure 2. Rate of succeeded identify on different percent of malicious peer in TMCQ

C. The impact of numbers of download on the successful download rate The simulation experiments verify the reasonableness of

the TMCQ according to the impact of the increase in the number of downloading on the successful download rate. We randomly select 5 good nodes from 1000 nodes, 3 general malicious nodes, 2 strategy malicious nodes, and then make the ten nodes sent the download requests to 1000 nodes.

Figure 3. Rate of succeeded download according to times of download

As shown in Figure 3, with the increase in the numbers of transactions, the successful download rates of good nodes gradually increase, the general malicious nodes rapidly decline, strategy malicious nodes show fluctuations, which attribute to the duplicity of the strategy malicious nodes. It is difficult for TMCQ to identify strategy malicious nodes, but the successful download rate is decline in the general trend. The results proved that the TMCQ is reasonable.

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D. The impact of the strategy malicious peers percent on the successful download rate In the downloading process, The impact of the proportion

of malicious nodes to the downloading success rate is very large. Because TMCQ is reasonable and effective for recognition of malicious nodes and the recognition success rate is high, The impact of the proportion of malicious nodes to the downloading success rate is smaller in this model. The Fluctuations of downloading success rate that changes with the general malicious nodes and strategic malicious nodes are as follows:

Figure 4. Rate of succeeded download in different mechanisms according to

general malicious peers percent

Figure 5. Rate of succeeded download in different mechanisms according to strategy malicious peers percent

From Figure 4 and Figure 5, the downloading success rate of TMCQ is greatly improved, comparing with Eigenrep and pet. What is more, the success rate is reduced, when the

proportion of malicious nodes increases. However, the reducing range is not large. This is because adopting exponential malicious nodes and the correct evaluation of the use of information support. The results of experiment prove that TMCQ has stronger anti-risk ability for dealing with the proportional change of malicious nodes.

V. CONCLURION Because dynamic, open and anonymity, the P2P nets have

behaviors of malicious nodes. Through introducing the definition of ability and quality, TMCQ establishes more comprehensive evaluation vector and presents the method of computing the trusting degree, which makes the believing relation of nodes more reasonable. The results of experiment show the model achieves the expected results in some standards. However, due to the complexity and uncertainty of P2P nets, the model is lack of good algorithm that is used to computing nodes, so our future works will be focus on this aspect.

ACKNOWLEDGMENT This work is supported by Natural Science Foundation of

China (Grant Number 60873203) and Natural Science Foundation of Hebei Province (Grant Number F2008000646).

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